Monitoring of selected soil contaminants using

Critical Reviews in Environmental Science and Technology

ISSN: 1064-3389 (Print) 1547-6537 (Online) Journal homepage: http://www.tandfonline.com/loi/best20

Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives Asa Gholizadeh, Mohammadmehdi Saberioon, Eyal Ben-Dor & Luboš Borůvka To cite this article: Asa Gholizadeh, Mohammadmehdi Saberioon, Eyal Ben-Dor & Luboš Borůvka (2018): Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives, Critical Reviews in Environmental Science and Technology, DOI: 10.1080/10643389.2018.1447717 To link to this article: https://doi.org/10.1080/10643389.2018.1447717

Published online: 19 Apr 2018.

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CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2018, VOL. 0, NO. 0, 1–36 https://doi.org/10.1080/10643389.2018.1447717

Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives Asa Gholizadeh a, Mohammadmehdi Saberioon and Lubos Boruvkaa

b

, Eyal Ben-Dor

c

,

a Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic; bLaboratory of Signal and Image Processing, Institute of Complex Systems, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, University of South Bohemia in Ceske Budejovice, Nove Hrady, Czech Republic; cThe Remote Sensing and GIS Laboratory, Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel

ABSTRACT

KEYWORDS

Soil degradation includes a number of processes, ranging from soil erosion to soil contamination, which reduce the capability of soil to work as a base for vegetation roots. Methods to quantify soil degradation due to contamination on a large area with a proper domain are needed and must be studied and developed. Proximal and remote sensing techniques are essential tools, well-suited for surveying large areas, and monitoring soil contamination at a high temporal and spatial interval. Recently developed and forthcoming satellites also dedicated to land monitoring and provide inimitable data streams, which have potential of soil contamination detection. This study peruses the potential of spectroscopy methods in various domains to assess selected soil contaminants including potentially toxic elements and petroleum hydrocarbons from reflectance information, plus a preliminary review of the newgeneration orbital Earth observation sensors. An aim is to review the means to do so from spaceborne sensors, which are considered to be state-of-the-art Earth orbit observation technologies. This review will help to answer the question: how can spectral information from proximal and remote sensing techniques in different domains be used for soil contamination modelling? This direction will pave the way for soil contamination monitoring using these techniques.

Imaging spectroscopy; spaceborne sensors; soil contamination; soil spectroscopy

1. Introduction Soil degradation can take place both physically and chemically. Physical processes result in changes of soil depth, particle size, structure as well as soil compaction CONTACT Asa Gholizadeh [email protected] Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, 16500 Praha 6, Suchdol, Prague, Czech Republic. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/best. © 2018 Taylor & Francis Group, LLC

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A. GHOLIZADEH ET AL.

status, while the reasons for chemical processes are properties of the soil that concern soil chemical constituents and their reactions. Water loss, soil toxicity, salinity, and erosion are the destructive effects of physical and chemical processes, which together direct to a reduction in soil health in both space and time.[1,2] Soil degradation may manifest in an increase of CO2 emissions following deforestation, decreases in above- and below-ground storage of carbon and through its influences on the ability of ecosystems to control soil-vegetation-atmosphere transfer (SVAT) processes[3,4] and to deliver essential ecosystem services. Thus, soil degradation processes have important implications for climate change too. The Soil Science Society of America (SSSA) defines contaminants as any substance in the soil that exceeds naturally occurring levels and poses human health risks. Indeed, soil contamination, which is one of the causes of soil degradation, refers to a process in which nonpedogenic components with no relation to the soil’s natural formation accumulate in soil and cause adverse effects on plant growth as well as animal and human health.[5] There are different sources of soil contaminants including agrochemicals (fertilizers, pesticides, and herbicides), natural gas, petroleum hydrocarbons, or potentially toxic elements (PTEs). Over the last few decades due to rapid economic development and severe industrial activities, high levels of natural gas, petroleum hydrocarbons, and PTEs are continuously being released into soils, even the agricultural soils. Natural hydrocarbon seepage and fuel pipeline and tank leakage may be sources of hydrocarbon liquid and gases in the soil. Hydrocarbon leakage is responsible for several chemical alterations in soils that overlay oil and gas reservoirs.[6] Large-scale opencast mines and anthropogenic activities also cause some of the most remarkable deformations to the Earth and mostly cause various spoil dumps that contain acidified substances as well as PTEs, which results in changes to the hydrological, geological, and vegetation situation.[7] For instance, fertile lands shift to wasteland, as mining activities produce extensive levels of wastes, which are deposited on the surface and cover a vast part.[8–10] Due to the persistent nature and long biological half-lives of elevated contaminants in soils, they affect not only the soil quality but also the entire ecology system and are added to the food chain and potentially can affect human health.[11,12] As respect to the above-mentioned dynamical effects, premonitoring permits the prevention before severe soil degradation occurs.[13] Soil natural gas, petroleum hydrocarbons, and PTEs concentrations can be determined using extensive sampling and conventional analysis methods, which are time-demanding and costly on a large area application.[14] For instance, the conventional techniques for PTEs concentration assessment in soil need complex, multistep laboratory analyses that include strong acid digestion and concentration procedure.[15] Similarly, common conventional and laboratory-based techniques for determination of petroleum hydrocarbon such as chromatography with flame ionization detector or two-dimensional gas chromatography with flame ionization detector are nonfield-portable, expensive, and need extra time for sample extraction and analysis[16–18]; however, they offer accurate determination of

CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY

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hydrocarbons. It should be also mentioned that conventional methods for monitoring natural gas and petroleum hydrocarbons leaks can perceive large spills only, therefore small and continues losses (below 1% of pipeline flow capacity) are problematic to be detected. Small leaks are usually noticed by visual inspection of kilometers of pipeline to spot losses and can be simply mixed up with soil humidity.[19] Furthermore, conventional techniques for environmental soil assessment based on raster sampling need several sample collection and complex laboratory processes, such as separation and preconcentration, which make them inconvenient for contaminated soil mapping over large areas.[20] According to Goodchild,[21] raster refers to a data model based on a regular (usually rectangular) tessellation of a plane, in which all locations information can be imputed to a record’s sequential position and thus data structure misses. Grauer-Gray and Hartemink[22] also stated that raster sampling suffers from some limitations. They mentioned that raster sampling, particularly a high-resolution one, is laborious. Moreover, sampling may be conducted from horizon boundaries rather than horizons. In addition, in raster sampling a thin horizon may not be sampled.[22] Further uncertainty in raster sampling occurs in the situation of a digital elevation model (DEM), as local estimations of slope may be made by fitting a plane to a small neighborhood, suggesting a piecewise linear rather than piecewise constant model.[21] Hence, there is an urgent need for a rapid, environmentally friendly, and costeffective sensing technology, which provides many advantages over traditional techniques including portability, speed, wide dynamic range of elemental quantification, little need for sample preparation, and simplicity,[23] to identify the aforementioned contaminants for prevention and remediation purposes. The emersion of proximal and remote sensing has been recognized as an alternative and efficient remote and noncontacted detection method for mapping and monitoring of various soil contaminants.[24,25] Employing proximal and remote sensing techniques can serve as a crucial tool, both at the stages of pollution detection[26,27] and ecological risk monitoring[28] and may eventually help to a noticeable decrease in pollution levels of native and man-altered landscapes. Some researchers have studied the potential of proximal and airborne sensors, as well as unmanned aerial vehicles (UAVs), to successfully quantitative estimation of contaminants.[4,25] However, the inclusion of spaceborne sensors and satellite remote sensing data with lower spectral resolution domains into soil contamination monitoring and digital mapping have been left behind, mostly because of the absence of appropriate sensors. Satellite-based hyper and superspectral sensors have been expanded and launched and more are under development for the near future, which will generate large data streams for land monitoring.[29–32] These have the potential to considerably improve the related information to soil contamination estimation over larger spatial areas. Thus, an extension of soil evaluation process from the local to the regional scale will be enabled.[33] Clearly, the use of high spectral resolution optical images along with a vast data stream, particularly from orbit, to monitor soil

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contaminants including natural gas, petroleum hydrocarbons, and PTEs, is in high demand. In this study, the authors review the comprehensive knowledge and information collected over three decades from various sources on proximal and remote sensing techniques in different domains for the selected soil contamination (natural gas, petroleum hydrocarbons, and PTEs) assessment. They also discuss the performance potential of the recently launched and forthcoming space sensors in combination to review the possibility of monitoring soil contamination from orbit. There then follows a discussion of the limitations and issues surrounding the remote sensing of soils and some potential solutions. The authors end with a short conclusion of concerns about the employment of innovative sensors for preserving soil contamination for upcoming studies.

2. Soil contamination Due to fast economic progress and urbanization in the last few decades, environmental pollution has become an increasingly serious issue. Excessive contaminants in the environment pose a severe threat to human health by means of entering the food chain and migrating into drinking water sources.[34] Because of their high metal-scavenging potential, soils are the main sink for released pollutants into the environment.[35] In turn, pollutants degrade the chemical and microbiological quality of soil,[12,36] and subsequently, because of their continual nature and long biological half-lives, create a risk to humans through their potential direct contact with contaminated soils or by transfer from soil to crop.[20,37] The most common soil contaminants are agrochemicals (fertilizers, pesticides, and herbicides), natural gas, petroleum hydrocarbons, and PTEs. Regarding to sever and increasing industrial activities worldwide, significant contents of natural gas, petroleum hydrocarbons, and PTEs are considerably being diffused into all soils including the agricultural soils.[6,34] Leaking oil and gas pipelines and petroleum hydrocarbons seepage are problematic in many areas. In the case of large undiscovered leakage, the extensive volume of explosive gases in the soil can occur.[6] The main influences of these contaminants in soil are microbiological alterations, neomineralization (e.g., calcite, pyrite), bleaching (discoloration of red soils), electrochemical alterations, and radiometric anomalies.[38,39] According to Noomen et al.,[40] depending on gas seepage length and soil-type hydrocarbons cause a vast range of alterations in the soil. Available oxygen of the soil is displaced by hydrocarbon gases[41] and is reduced by methanotrophic bacteria,[42] which affect vegetation growth and development.[43,44] Both underground and open-cast mining activities are associated with many environmental issues such as acid mine drainage,[45] PTEs release into the environment,[46] and generation of a large amount of PTEs.[15] Acid mine drainage leads to accumulation of Fe minerals, which cause progressive increases in the pH[47]; pH is a principal factor in distribution and mobilization of PTE across the soil profile. Therefore, in acidic soils several PTEs including Cd, Zn, Co, Cu, and Ni are simply


5

mobilized and easily obtainable for plant uptake.[48] PTE pollution in soils also happens because of anthropogenic activities and affects the physical and chemical features of the soil ecosystem.[49,50] Although soil PTEs and their negative effects have become a global concern, their concentrations in some areas are still extremely high. Concentrations of PTEs in soils close to mining areas from different countries are given in Table 1. Research on soil contamination and soil changes due to contamination is getting more attention, particularly in the soil ecosystems reclamation and sustainable use. For the sake of economic, environmental, and health perspectives, monitoring and analyzing these alterations are necessary for residents, decisionmakers, and environmental-observers. As previously mentioned, conventional methods for determining the soil condition in vast areas require field sampling, chemical analyses in a laboratory and geostatistical interpolation, which are time-consuming and expensive.[14] For instance, according to Shi et al.,[34] because of limited funds, investments on PTE decontamination have relatively lagged behind or have even been totally ignored, despite the prevalence of PTEs in soils. Moreover, monitoring of contaminated sites is typically conducted by the respective industries such as the petroleum industry using temperature, pressure, and flow changes methods along the pipeline, which have inherent risks and rely on the accuracy of the experts.[62] In addition, these methods are inefficient at detecting small soil changes and therefore, benefits of detecting small changes, controlling them before they become large and able to cause greater impacts, will be neglected.[39] However, any significant alteration of soil condition needs to be carefully evaluated using available high-tech sensors and techniques for early detection of soil status due to soil contamination. These sensors including proximal and remote sensing techniques, which differ in their radiometric, temporal, spectral, and spatial resolution and as a result, in their monitoring ability.[63] The spatial resolution may be millimeters (drone-based cameras), 0.5–2 m (airborne and some hyperspectral sensors such as AISA, HySPEX, APEX), 2–10 m (some satellite sensors such as WorldView-2, RapidEye, and Sentinel-2), 10–30 m (some satellite sensors such as Spot and Landsat), and up to 250–1000 m or greater (MODIS and NOAA AVHRR) depending on the sensor’s platform. In the following sections, a summary of various soil contamination monitoring techniques is presented.

3. Proximal sensing of selected soil contaminants Proximal sensing is defined as the use of electromagnetic radiation to quantitative assessment of a given object (e.g., soil) properties. It can be used by different sensors that obtain signals from the object when the sensor’s foreoptic is in contact with or close to (within 2 m) the object.[64] It is the field-based sensors that are sensitive to reflectance and emittance of radiation across the visible-near infraredshortwave infrared-longwave infrared (VIS-NIR-SWIR-LWIR) region (400–

No.: number of samples.

Oltu, Turkey Ptolemais, Greece Douro, Portugal Smolnica, Poland Pokrok, Czech Republic Xuzhou, China Guizhou, China Jiangsu, China Sonepur, India Ledo, India Suncheon, South Korea Waal, Netherlands Minimum Maximum Reference soil, China Reference soil, USA Soil quality for agricultural soil, Canada

Location Reclaimed soil Waste-impacted soil Reclaimed soil Reclaimed soil Reclaimed soil Mine-impacted soil Agricultural soils Opencast mine-impacted soil Mine-impacted soil Mine-impacted soil Floodplain soils

Soil type 19 101 3 — 103 — — 122 32 — 22 69

No.

Mn — — 139 — 599 — — — 3.96 — — — 86 3700 — 380 —

As — 12.3 38.3 — 4.48 — — 8.06 — — 226 — 0.5 38.3 11.2 5.2 12 35.9 — 57 142 25.2 130.9 135.8 — 947 — — 80.97 1.5 296 74 55 200

Zn 23.4 — 36.5 18 13.7 52.3 — 31.95 27 — 49.3 — 0.5 110.4 23 21 63

Cu 135.6 17.5 92.3 — — 73.4 — 73.96 98 112 — — 17.5 523 61 41 64

Cr 59.8 10.1 21.4 — — — — — 34 87.5 — — 4.3 390 27 15 50

Ni

34.2 — 30.8 39.9 18.4 47.4 42.4 28.17 27.3 183 88.1 — 0.5 433 27 17 70

Pb

Table 1. PTEs content (mg/kg) in mine area soils from various countries compared with global reference values and other reference soils.[12]

0.28 0.012 2.6 — 0.68 0.02 4.48 0.097 — 1.4

0.03 — 0.2 1.65 0.27 3.2

Cd

[51] [52] [53] [54] [15] [55] [56] [49] [57] [58] [90] [78] [12] [12] [59] [60] [61]

Reference

6 A. GHOLIZADEH ET AL.


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16000 nm). After introducing portable field spectrometers around 1993, more researchers appreciated the soil spectroscopy, which was resulted to the preparation of more spectral libraries.[65–68] Soil spectra across the VIS-NIR-SWIR-LWIR spectral range are specified by significant spectral features that allow quantitative analysis of various soil attributes. According to Viscarra Rossel et al.,[68] the information on soil composition, which contains minerals, organic compounds and water, is encoded by spectra reflectance or emissivity. These encodings are indicated in the spectra as absorptions at specific wavelengths of electromagnetic radiation and their measurements can describe soil either qualitatively or quantitatively.[68] Quantitative soil spectroscopy was investigated by the pioneering study of Bowers and Hanks,[69] who introduced the correlation between soil reflectance and soil moisture content. Later, Hunt[70] showed that soil minerals and water have particular and specific spectral fingerprints. Due to successful experience of various sectors such as food science, tobacco, and textiles in employment of spectroscopy technique, Dalal and Henry[71] implemented the proximal sensing approach to soils for prediction of different attributes. Many researchers displayed the power of VIS-NIR-SWIRLWIR reflectance spectroscopy in accounting for different soil attributes.[72–76] Although soil contaminants at low concentration levels do not have direct and recognizable spectral features within the VIS-NIR-SWIR-LWIR region, they may be detected indirectly via intercorrelation with the soil attributes that are spectrally active in this region[24,49,75,77,78] and through their complexation with organic matter (OM), association with sulfides, carbonates, hydroxide, or oxides, which are acquirable. They can also be assessed by their adsorption onto clay surfaces that are a recognized chromophore in this wavelength range.[79] To clarify spectral signals variations related to contaminants being bound with minerals, the binding reaction of the metal onto the surface of minerals needs to be considered, which starts from the principle that chemical composition and surface activity can change the minerals appointed spectral position.[80] Thus, reflectance spectra can be utilized for the indirect assessment of contaminant contents in soil samples via the spectrally active soil attributes and contaminant concentrations correlation. According to Wu et al.,[24] the availability of Fe in ferric or ferrous forms is the reason for absorption features at wavelengths in the 400–1300 nm region and the intercorrelation between total Fe and PTEs is a key predictive mechanism. Wu et al.[26] explained that PTEs with a high correlation with Fe also have high cross-validation statistics. The prediction accuracy of Co, Ni, and Cr was high and had the highest correlation with Fe. Jia et al.[81] subsequently showed strong negative correlation coefficients between Cr, Cu, Zn, and As and the absorption features of OM, clays, and Fe oxides. They also displayed significant correlations between Cd, Pb, and Hg with the spectral region related to organic carbon. Therefore, by using soil proxy methods with reflectance spectroscopy, various soil contamination can be monitored efficiently.[15,82–85]

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3.1. Potentially toxic elements (PTEs)

Some experimental studies that employed the soil proxy approach have addressed the contamination of alluvial soils with PTEs.[26,86,87] However, the first report on the prediction of PTEs in sediments by VIS-NIR spectroscopy was published by Malley and Williams.[79] They proved the possibility of predicting six (Cu, Pb, Zn, Ni, Mn, and Fe) of the seven (Plus Cd) elements examined with VIS-NIR spectroscopy in a set of sediment samples with high variability, collected in Northwestern Ontario, Canada. The elements were modeled using multiple linear regression (MLR) and partial least square regression (PLSR), in which OM was introduced as the predictor element of sediment PTE contents.[79] Kooistra et al.[78] predicted the Cd and Zn levels in floodplains along the river Rhine in the Netherlands based on the positive correlation between the OM and Zn and Cd concentrations. They stated wavelengths 700, 1050, 1400, 1850, 2150, 2280, 2400, and 2470 nm are comparable regions, thus it supports the hypothesis that reflectance spectra can be used for the indirect determination of metal concentrations in soil via the correlation between spectrally active soil characteristics and metal concentrations. To measure Fe, Pb, As, Hg, S, and Sb in the Aznalcollar mine area in Spain, Kemper, and Sommer[88] effectively used diffuse reflectance spectroscopy (DRS). For estimation of both PTE-contaminated and uncontaminated soil with different amounts of Cu, Zn, Cd, and Pb, VIS-NIR-mid infrared (MIR) spectroscopy (400– 25000 nm) was successfully employed by Bray et al.[89] According to them spectroscopic diagnostic screening for the selected PTE concentrations are possible due to their relationship with clay, Fe, and OM. Ren et al.[14] have also estimated Cu and As concentrations using reflectance spectroscopy of areas near mining activities. Choe et al.[90] examined the capability of spectral absorption feature to predict PTEs using stepwise multiple linear regression (SMLR) and enter multiple linear regression (EMLR). In their experiment, the EMLR model showed qualitative prediction performance of R2 D 0.60 for As and R2 D 0.81 for Cu. The correlation they obtained, indicated that some of the spectral absorption feature parameters values have potential in terms of representing As and Cu when undertaking mapping and quantitative analysis based on NIR spectroscopy. Pandit et al.[91] found very high correlation coefficients between laboratory-determined and PLSR-predicted values for several PTEs (highest correlation coefficient for Pb with R2 D 0.99); however, the correlation between Pb and OM contents was rather weak (R2 D 0.47). They suggested that OM cannot be used to predict Pb because only a little OM is needed to retain Pb, nevertheless it is a factor in Pb retention.[91] In a study by Gholizadeh et al.,[15] VIS-NIR-SWIR spectroscopy was used to predict PTEs in dumpsites of the Czech Republic using PLSR and support vector machine regression (SVMR) algorithms. As expected, they observed three essential absorption bands throughout all the compressed spectra (around 1400, 1900, and 2200 nm). The general shape and slopes of all the curves were similar. They mentioned although intense bands in the VIS-NIR spectra are not directly associated


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to the presence of PTEs, it is clear that PTEs can interact with the main spectrally active components of soil. Based on this phenomenon, chemometric models can be developed for soil samples in order to screen their toxic element concentrations. The most accurate prediction was reported for As (R2 D 0.89), which was obtained by SVMR technique. Correlation between the reflectance of VIS-NIR-SWIR and PTEs in their study has been illustrated in Fig. 1. 3.2. Petroleum hydrocarbons

Some studies have also focused on reflectance properties of soils contaminated with petroleum hydrocarbon. Cloutis [92] demonstrated that two major absorption bands (1730 and 2310 nm) identify petroleum hydrocarbons. Several authors[93–95] used these spectral features for directly detection of petroleum hydrocarbons in soils. In a laboratory experiment by Winkelmann,[96] several hydrocarbons were mixed with various soil types and using the reflectance spectra, they were evaluated spectrally and separated into hydrocarbon groups. They concluded that identification of the petroleum hydrocarbons was feasible using NIR spectroscopy. According to them, NIR region hold a considerable potential for quantitative analysis but also for qualitative analysis of petroleum hydrocarbons. In the infrared region, the interactions of electromagnetic radiation and matter are related to the infrared radiation absorption by specific vibrations of organic molecules. In the infrared region, the interactions of electromagnetic radiation and matter are related to the infrared radiation absorption by specific vibrations of organic molecules. Molecules can vibrate in different modes and the vibration modes of a molecule is independently from one another. Each vibration has a characteristic vibrational frequency. It requires to be noted that only those vibrations that yield in a change of the dipole moment of a molecule can be excited by infrared

Figure 1. Correlation between reflectance of VIS-NIR-SWIR and PTEs.[15]

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radiation and are then evident in infrared range.[96] Schwartz et al.[97] artificially contaminated the soil with known Octane 95 petrol and were able to show that proxy analysis of petroleum hydrocarbons in soil is as good as traditional analytical methods. Their results demonstrated that under laboratory conditions, the reflectance spectroscopy could provide a good estimation of the petroleum hydrocarbon. Correa Pabon and de Souza Filho[19] showed that hydrocarbon diagnostic features are mainly seen in two spectral regions: region I, between 1700 and 1780 nm and region II, between 2290 and 2367 nm. They stated that proximal sensing could significantly reduce either the time or costs related to detecting for soil contaminated by hydrocarbons in comparison to wet analysis. Table 2 shows the summary of proximal sensing application in soil contamination monitoring.

4. Remote sensing of selected soil contaminants According to Elachi and Van Zyl,[98] remote sensing is using electromagnetic radiation in order to information acquisition of an object or phenomenon without physical contact. It contains sensors, which obtain electromagnetic reflectance within a wide range of frequencies passively or actively. The remote sensing discipline uses mostly UAVs, air and spaceborne sensors for soil monitoring.[99] In a review of remote sensing tools for soil applications, Mulder et al.[100] showed that optical remote sensors cover most of information required for soil applications. The spectral resolution of the optical sensors largely depends on the numbers, sampling, and position of bands. The multispectral sensors offer from 3 to 7 bands, the superspectral sensors from 7 to 20 bands, the hyperspectral sensors from 20 to 500, and the ultraspectral sensors offer more than 1000 bands. Remote sensing data provide an alternative to conventional ground-based methods and proximal sensing methods to detect soil contaminants and play a valuable role in providing Table 2. Summary of proximal sensing application in soil contamination. Properties As, Cu, Fe Cu, Mn, Cd, Zn, Fe, Pb, As Ni, Cr, Cu, Hg Ni, Cr, Co, Cu, As, Zn, Pb Cr, Cu, As Cd, Pb, Hg Cd, Zn Cd, Cu, Zn, Pb, Ni, Mn, Fe Cd, Pb, Hg Cr, Cu, Zn, As Cu, Pb, Zn, Fe, Mn Cd As, Fe, Hg, Pb, S, Sb As, Cu, Pb Pb, Cd, Mn, Cu, Zn Hydrocarbons Hydrocarbons Hydrocarbons

Algorithm

Approach

Reference

PLSR PLSR, SVMR PLSR PLSR PLSR PLSR PLSR PLSR Univariate regression Univariate regression PLSR Univariate regression ANN, MLR SMLR, EMLR PLSR PLSR SMLR, PLSR, SVMR, RF, penalized spline RF, PLSR

Indirect—Fe Direct Indirect—Fe2O3 Indirect—SOC, Fe2O3 Indirect—Fe2O3 Indirect—OM Indirect—OM, Clay Indirect—OM Indirect—OM Indirect—Clay, Fe2O3 Indirect—OM, Carbon Indirect—Clay, OM, Fe2O3 Indirect—Fe, Fe2O3 Direct Indirect—OM — —

[14] [15] [24] [26] [49] [49] [78] [79] [81] [81] [83] [87] [88] [90] [91] [93] [94] [95]


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time-specific information for soil rehabilitation due to their ability to detect spatial variability. Researchers also use soil indices to extract soil information remotely, which may serve as convenient means.[101] However, the remote sensing data using imaging spectroscopy (IS) provide high spectral and spatial view of the area in question and hence demonstrate more promising capability than any other remote sensing tools that are based on limited or broad band indices.[102] 4.1. Unmanned aerial vehicles (UAVs)

To assess soil condition high spectral and spatial resolution remote sensing data are required. Some suppose that conventional ground-based, airborne, or spaceborne domains are mostly not capable to provide timely and frequent remote sensing data at a sufficient spectral and/or spatial resolution.[103–105] Furthermore, they are not available at affordable costs for small-area research with a huge number of plots, as frequent revisit-time and high spatial resolution are necessary for management applications.[103–105] Using UAVs for obtaining the required high spatial and spectral data as well as providing temporal flexibility fills the above-mentioned gap between information requirements and availability. Operational resilience plus enough spatial and temporal details without excessive sampling to monitor ecological phenomena over time is accessible by using UAVs.[106] Advantages such as flexibility in the choice of the height above the subject and spectral resolution (by the alternative choice of camera) are easily accessible. Moreover, the equipment is fast, small and easy to transport.[107] 4.1.1. Radiation contamination UAVs have received attention in fields such as environmental impact assessment and environmental contamination monitoring.[108,109] Sanada et al.[110] and Martin et al.[111] used UAVs mounted with a radiation detector, three charge-coupled devices (CCDs) (Japan Radiation Engineering Co. Ltd., Hitachi, Japan) and a miniature gamma-ray spectrometer (KromekTM Co., Durham, UK), respectively, to measure soil surface nuclear radiation contamination around the Fukushima Daiichi nuclear power station in Japan. They stated that the system could be used for radiation monitoring in case of a nuclear emergency and consequent disturbing effects on soil. Using UAV in this case, prohibits the possible radiation exposure of manned-aircrafts and ground in-situ measurements. 4.1.2. Natural gas The use of UAV as an approach to soil oil and gas detection is also a way to efficiently ensure accurate monitoring. Rossi and Brunelli[112] developed a gas sensing system using UAV for localization of gas leakage to soil and showed that the analysis of the target environmental parameters is not perturbed by the air flow generated by propellers. Later, Mochammad et al.[113] used a UAV platform to monitor soil hazardous gas level. They claimed that mapping the hazardous gas level and

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distribution was successfully conducted due to the system capability to flying at the low speed. Chinchu et al.[114] also implemented a system using a UAV equipped with detection cameras and could accurately identify leakage of harmful gases in agricultural soils, housing areas, and industrial regions. Despite the UAVs advantages, they are frequently surveying small areas and in conflict to assure security, which makes their application limited. 4.2. Airborne sensors

For IS and particularly hyperspectral imaging purposes, airborne sensors can be employed because of the flexibility of their operations. High spectral and spatial resolutions and flexible revisit-time can be delivered using airborne sensors. The IS sensors can fly on both manned and UAV aircrafts; however for UAVs, covering the entire spectral region is still limited by the size and weight of the sensor as well as the platform availability. The IS technology had shown the potential of soil monitoring based on their high spectral and spatial resolutions, ranging between 0.5 and 2 m pixel size with 2–20 nm bandwidths in the 400–2500 nm spectral range.[103] Therefore, to fit the applications needs, ground sampling distance and swath width can be easily adjusted.[115] Over the past years, many airborne systems have been expanded and enhancements have been made in number of spectral channels, swath width and spectral and radiometric calibration accuracy.[116,117] Several soil properties can be extracted both directly and indirectly using a wide variety of old and recent airborne hyperspectral sensors, including AVIRIS, GER, CASI, HYDICE, HyMap, MIVIS, DAIS-7915, ROSIS, AISA, HySPEX, FENIX, OWL, APEX, etc.[118,119]. Main technical characteristics of some of the most common airborne sensors in optical domain can be seen in Table 3. 4.2.1. Potentially toxic elements (PTEs) Several studies also demonstrated the capabilities of applying airborne IS techniques for environmental monitoring and environmental impacts assessment[47] and even spectrally featureless parameters such as PTEs were modelled reasonably using airborne hyperspectral data.[25,120] Some researchers have studied the potential of hyperspectral airborne sensors such as Probe-1,[38] HyMap,[40] CASI-2,[121] and MIVIS[122] to attain quantitative estimates of contaminants. It seems that the use of high-quality IS airborne data improves DRS results.[25,122] Table 3. Technical specifications of some airborne sensors. Sensor

Spectral range (nm)

Channels (no.)

Spectral bandwidth (nm)

Spatial resolution (m)

Swath (km)

SNR

HyMap AISA AVIRIS CASI

440–2500 400–2400 400–2500 380–1050

128 807 227 288

10–20 3.3–12 10 7.5

2–10 1 30 1–4

2.3–4.6 0.4–1.5 7.5 1.6

>500:1 >350–800:1 >500:1 >200:1

SNR: signal to noise ratio.


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Cocks et al.[123] studied soil samples using a hyperspectral image of the HyMap and variable multiple endmember spectral mixture analysis (VMESMA) modeling approach, to assess the distribution and quantity of the remaining tailing material in the field. Based on an artificial mixture experiment prepared with three various soils, the semiquantitatively information of pyritic material was converted to quantitative data and distribution of PTE was estimated. Employing the HyMap airborne data, Chevrel et al.[124] successfully investigated six mining areas for PTE contamination in the MINEO project, five locations in Europe (Portugal, UK, Germany, Austria, and Finland) and one in Greenland. Kemper and Sommer[125] used the HyMap sensor with the aim of the extent of PTE contamination assessment in the dam of a mine tailings pond in Aznalcollar, Spain. They successfully measured the concentrations of As, Cd, Cu, Fe, Hg, Pb, S, Sb, and Zn. Using the unmixing technique of HyMap data also made the recognition of oxidation and associated acidification possible.[125] Choe et al.[25] studied the possibility of using spectral variations linked to PTEs in order to map the distribution of PTEs in affected regions of Rodalquilar gold-mining area in southeast Spain with the HyMap data. The selected spectral features proved considerable correlations with Pb, Zn, and As contents. 4.2.2. Acid mine drainage Hyperspectral airborne remote sensing was also utilized to monitor acid mine drainage contamination. Swayze et al.[47] employed the AVIRIS airborne hyperspectral data to identify acid mine drainage sources at the California Gulch Superfund Site. They indicated that the contaminants were possible to be detected via Fe-bearing secondary minerals, which are spectrally unique. Fischer and Busch[126] used the HyMap data for an indirect assessment (using vegetation data as reference) of acid mine drainage and the pH range in coal mines and confirmed that semiquantified surface pH could be effectively obtained. 4.2.3. Natural gas and petroleum hydrocarbons In order to assess natural gas seeps and petroleum hydrocarbons in soils of the Ventura basin, southern California, Van der Meer et al.[38] used airborne hyperspectral remote sensing data from the Probe-1 in the VIS-NIR-SWIR region. They stated that oil pools and macroseeps often can be detected directly; however, microseeps cause vegetation stress and bring geochemical alterations in soil and rocks, therefore can be studied indirectly using airborne hyperspectral sensors. The indirect detection of hydrocarbons through monitoring vegetation anomalies as soil proxies, in agricultural soils close to pipeline leakage in the Netherlands using the HyMap airborne imagery was also studied by Van der Werff et al.[127] They developed a moving and growing kernel procedure in order to normalize red edge values relative to values of neighboring pixels, which increased the pollutionrelated anomalies in the image. Comparison of the spatial distribution of anomalies with geochemical data gained by drilling indicated that 8 out of 10 polluted

14


sites could be properly monitored although 2 out of 30 sites were predicted clean while they were polluted. Despite the high temporal, spectral, and spatial resolutions in airborne remote sensing, the use of airborne sensors for research activities has so far been limited due largely to the long turnaround times, high operational costs, and the need to private corporations offering cost-effective products.[103] Summary of airborne sensors application in soil contamination assessment can be seen in Table 4. 4.3. Spaceborne sensors

The above-mentioned techniques create a pixel spectrum, which has the potential of soil properties differentiation using hundreds of contiguous spectral bands with a narrow bandwidth.[128,129] However, for economical and frequent soil mapping and assessment of the contaminated surfaces, these platforms are not appropriate. Fast, spatially extensive and effective mapping and monitoring of contaminated areas and mining activities can be attained via spaceborne sensors. The main advantages of the spaceborne approach over other remote sensing platforms can be summarized in the comprehensive monitoring of large areas, data reduction, efficient classification of results, availability of high-quality temporal images, and frequent revisit-time.[117,130,131] 4.3.1. Acid mine drainage For the first time, Goetz and Rowan[132] carried out geological and soil remote sensing based on satellite data, using a Landsat multispectral scanner (MS) to create iron oxide maps. Since 1995, ASTER was also employed to provide satellite data for geological and soil remote sensing to map surface soil.[133–135] According to Mars and Rowan,[136] the ASTER SWIR bands allow for effective mapping of soil due to their relative fine spectral resolution. For monitoring mine tailings site employing simulated 4 broad-band IKONOS image and the full 65-band hyperspectral data (CASI), Levesque et al.[137] observed the similar results. Chevrel et al.[138] studies the potential of the ASTER data in three sites Table 4. Summary of airborne sensors application in soil contamination monitoring. Properties Hydrocarbons PTE Hydrocarbons AMD Hydrocarbons PTE PTE PTE PTE AMD Hydrocarbons

Algorithm

Sensor

Most related range

Indirectly—Vegetation stress SMLR, EMLR SDI Tetracoder — Indirectly—Vegetation stress VMESMA Linear unmixing VMESMA Indirectly—Vegetation stress Indirectly—Vegetation stress

HyMap HyMap Probe-1 AVIRIS HyMap CASI-2 HyMap HyMap HyMap HyMap HyMap

VNIR, SWIR VNIR, SWIR VNIR-SWIR NIR, SWIR SWIR VNIR VNIR, SWIR VNIR, SWIR VNIR, SWIR VNIR VNIR

AMD: acid mine drainage, SDI: statistical data integration.

Reference [6] [25] [38] [47] [120] [121] [123] [124] [125] [126] [127]


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(South Africa, Dominican Republic, and Mexico) and concluded that using the ASTER can bring an invaluable contribution in characterizing, identifying and mapping land use, mining residues, and mining effluents in mining areas. They related this capability to well-targeted spectral bands and the TIR spectral range covering.

4.3.2. Petroleum hydrocarbons Arellano et al.[139] used Hyperion satellite with a spatial resolution of 30 m with each pixel covering the spectral range, 400–2500 nm, for detection of contamination with oil in soils of the Ecuadorian Amazon rainforest. At those sites, crude oil had affected the surroundings and despite the evaporation of gaseous hydrocarbons, liquid hydrocarbons transferred from the open pits to the soil and water. Their investigation demonstrated the suitability of the use of the Hyperion spaceborne imager for the detection and characterization of hydrocarbon pollution in tropical forests soils.[139] There are also some soil and geology studies that have been based on the simulated imagery and the synthetic satellite images. For instance, Mielke et al.[130] combined the capabilities of hyper and superspectral spaceborne sensors for soil mapping and monitoring. They explored the potential of spaceborne sensors OLI, Sentinel-2, and EnMAP for spatial extent of mine waste surface mapping. The mines, gold and platinum, have been extracted for about 90 years and contained trace elements (U, Pb, and Cr). They suggested a new index, the iron feature depth (IFD) acquired from OLI data, to map the 900 nm absorption feature for monitoring the spatial extent of mine waste. The mean accuracy for mapping was as follows: EnMAP 100%, Sentinel-2 94.5%, and OLI 92%. Therefore, they proved that in the future Sentinel-2 and EnMAP data might be employed as two sensors for cost saving in mining areas remediation.[130] Rogge et al.[140] also used the simulated the EnMAP scene to assess the sensor’s capability to map Ni, Cu, and platinum group elements. They could assess the value of this upcoming satellite sensor system to support large geological mapping and mineral exploration in Canada. However, Kruse and Perry[141] supposed that field-of-view (FOV) of the sensors bands differ (slightly larger) from what the studies have achieved with simulated data, which brings inaccuracy. Van der Werff and Van der Meer[142] also presumed that in the conducted studies with synthesized data, the compaction of data used has not been considered. They also mentioned that the atmospheric correction in synthetic datasets, which are based on the band information of existing sensors, can be different to real data and thus may cause ambiguity. It can therefore, be stated that spaceborne sensors offer information on soil contaminants assessment and the imagery has a larger spatial coverage and a more frequent revisit-time for end-users.[143] However, the number of publications in which satellite hyper and superspectral data has been employed for soil contamination estimation and mapping is still very limited, due to the unavailability of suitable sensors.

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5. Potential of recently developed and forthcoming spaceborne sensors for soil contamination monitoring The identified potential of IS is not counterbalanced by the accessibility of satellite imaging data, although their number and availability are constantly growing. Moreover, the opening of large data sources such as Landsat,[144] entire space missions expanded for the public domain such as the Copernicus program of the European space agency (ESA),[145–147] the WorldView-3 and the WorldView-4 of the American DigitalGlobe[142] has brought advantages for application of spaceborne technologies. Furthermore, in near future a new generation of hyper and superspectral spaceborne sensors with higher signal to noise ratio (SNR) and shorter revisit-time is due to be launched, including HYPXIM,[148] HyspIRI,[149] EnMAP,[150] PRISMA,[151] FLEX,[152] and SHALOM.[153] Because of such advancements, it is anticipated that these technologies will direct to an exceptional movement in the application of space-based remote sensing techniques for soil contamination monitoring. Table 5 summarizes the main technical specifications of some of the spaceborne imagers. Soil scientists gradually study dynamic phenomena over space and time including oil and gas seeps, soil contamination, and general soil degradation, which need a longer time series of calibrated data. Recently developed, as well as forthcoming space-based sensors, are very valuable sources of data in this regard and provide an innovative and cost-effective method, which can allow the monitoring of such processes for repetitive coverage of large areas. Owing to the high potential of hyper and superspectral spaceborne images in soil and geological applications, special features of the recently operated the Sentinel-2, as well as forthcoming EnMAP and HyspIRI are briefly explained below. 5.1. Sentinel-2

The Sentinel-2 satellite was successfully launched on 23 June 2015 (Table 5). It was planned to provide continuity to monitor services over global surfaces, leaning on superspectral high-resolution observations. It guarantees continuity of the SPOT and Landsat assignments and offers applications such as land change detection maps and land cover maps.[154] The lately launched WorldView-3 has eight SWIR bands, which four were taken from the ASTER[141]; however, Sentinel-2 is missing such narrow bands in the SWIR range (Fig. 2). Afforded proxies with Sentinel-2 are the same as Landsat obtained ones, even though it had a slightly higher spatial resolution. According to Van der Meer et al.,[155] as there is a good correspondence between ASTER and Sentinel-2, several band ratios are proposed for Sentinel-2 to derive some soil attributes. Van der Werff and Van der Meer[142] stated that Sentinel-2 brings us information on soil attributes, together with iron oxide (which has good correlation with PTEs) that previous multispectral missions could not. To the best of our knowledge, the Sentinel-2 actual data has not yet been commonly used to analyze and

USA USA USA USA USA EU USA EU France USA USA EU Italy Japan China Germany USA Italy/Israel

Landsat-5 Landsat-7 ASTER IKONOS Hyperion CHRIS-PROBA MODIS MERIS SPOT-5 Landsat-8 WorldView-3 Sentinel-2 PRISMA HISUI CCRSS EnMAP HyspIRI SHALOM

NASA/NOAA NASA/USGS NASA GeoEye NASA ESA NASA ESA CNES NASA/USGS DigitalGlobe ESA ASI METI CNSA DLR NASA/JPL ASI/ISA

Organization 1984 1999 1999 1999 2000 2001 2002 2002 2002 2013 2014 2015 2017 2018 2018 2020 2020 2021

Launching year 30, 57, 120 30 15, 90 0.82, 3.28 30 17, 34 250, 500, 1000 300, 1200 10 30 0.3, 1.24, 3.70 10, 20, 60 30 30 30 30 60 10

GSD (m) 7 8 15 4 220 18, 37, 6 19 15 4 11 16 13 247 185 328 242 214 241

Spectral bands (no.) 450–2,350 450–12,500 520–11,650 445–853 360–2,600 400–1,050 620–965 390–1,040 480–1,750 430–12,510 400–2,365 440–2,195 400–2,500 400–2,500 400–2,500 420–2,450 380–2,510 400–2,500

Spectral range (nm) 185 185 60 11 7.65 13 2330 1150 80 185 13.1 290 30 30 40 30 145 10

Swath at nadir (km)

16 16 16 3–5 16 Varies* 16 35 2–3 16 1–4 5 NA NA NA 27 19 4

Temporal res. (days)

NA: not available, NASA: national aeronautics and space administration, NOAA: national oceanic and atmospheric administration, USGS: United States geological survey, ESA: European space agency, CNES: centre national d’etudes spatiales, ASI: agenzia spaziale Italiana, METI: ministry of economy, trade and industry, DLR: Deutschen zentrums f€ur luft- und raumfahrt, CNSA: China national space administration, JPL: jet propulsion laboratory, ISA: Israel space agency. Varies: It has a nominal sun synchronous polar orbit (SSO) but no orbit maintenance capability.

Country

Sensor

Table 5. Technical characteristics of some spaceborne remote sensing imagers.

CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY 17

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Figure 2. The bands of the Sentinel-2 sensor in comparison to WorldView-3 and ASTER.[155]

predict soil properties, including soil contamination. Though, the simulated imagery of the superspectral Sentinel-2 was used by Mielke et al.[130] to map the spatial extent of mine waste surfaces in South Africa, which contained problematic trace elements of U, Pb, and Cr. The reliability assessment of the sensor to provide operational products would necessitate inquiry of real data. 5.2. EnMAP

The spaceborne EnMAP sensor is devoted to environmental applications of state-of-theart hyperspectral technology.[156] The launch of EnMAP is projected for 2020 and the anticipated mission lifetime is 5 years (Table 5). It has been introduced as a sensor wellsuited to soil and geological mapping due to its fine spectral resolution of 6.5 nm in the VIS-NIR range and 10 nm in the SWIR range. It will offer new opportunities for shortterm minerals and metals detection, and long-term mine waste observation, as well as retrieving accurate soil data for mapping the world’s soils.[130,157] Using the full wavelength range of EnMAP will provide the chance to track hydrothermal alteration by mineral distribution spectroscopic mapping to imply PTEs through noticeable absorption features of minerals, which are these deposit types characteristic.[156] Figure 3 represents an EnMAP overpass depicting VIS-NIR and SWIR spectrometers FOV. Mielke et al.[130] employed synthetic EnMAP data to monitor areas around a gold mine; the mean accuracy for mapping of 900 nm Fe absorption features of minerals related to mine waste was 100%. Therefore, they claimed that in the future, in remediation of mining areas, EnMAP might be economical. Rogge et al.[140] also proved that EnMAP provided reconnaissance mapping of Ni, Cu, and platinum group elements over vast subarctic and arctic regions in Canada. 5.3. HyspIRI

The HyspIRI mission will be launched during the next decade and will comprise of two instruments: a VIS-NIR-SWIR imaging spectrometer and an eight-band TIR multispectral imager, plus an intelligent payload module (IPM) for on-board large data sets processing and quick downlink of selected data.[63,158] The HyspIRI


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Figure 3. EnMAP operating principles.[156]

mission expected to provide high-quality global datasets for a vast range of science and application requirements, including monitoring natural and anthropogenic disturbances such as oil spills in soil because of the availability of multiple SWIR bands.[159] For instance, following the deep-water horizon oil spill in Barataria Bay soils to map the soil contamination, Kokaly et al.[160] used hydrocarbon absorptions detectable in SWIR imaging spectrometry data. HyspIRI TIR data would bring the opportunities for new research on hydrocarbon resource explorations with its possibility of temperature anomalies mapping.[155,161] HyspIRI VIS-NIR-SWIR and TIR data fusion can significantly improve the capability of surface materials (rocks, soils, and vegetation) discrimination,[155,162] which would be an early attempt for assessment of land surface change, if caused naturally or of anthropogenic source. In all, due to the spatial, spectral, and temporal specifications of recently developed and forthcoming satellite imagers, it seems that there is a potential for developing products that can benefit the soil science and geology community. This is expected to offer faster worldwide support while retaining the maximum level of data consistency. Nonetheless, efforts are still needed for IS products advancement that can solidly support soil spectral libraries (SSLs) development,[68] global soil digital mapping, and soil contamination monitoring.

6. Current limitations of spaceborne sensors for soil contamination monitoring Despite the significant progress made in remote sensing of soils during the last decades, and the promising performance of recently developed spaceborne missions, which open opportunities to implement novel and accurate retrieval algorithms in operational processing chains, there are still some limitations in using their data, which still have not been solved. These problems are mainly associated with the data

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acquisition condition, environmental situation, and the interpretation of different surface variables from satellite data at the temporal and spatial data scales.[163] The main difficulty in soil remote sensing is the presence of vegetation. Soils are usually covered by vegetation and the sensors obtain the signal from the surface, which is covered with vegetation and soil; however, the evaluation of extracted soil spectral information from satellite images is related to the bare soil availability.[164] The vegetation coverage and accessibility of bare soil in satellite images of cultivated lands are limited to a short period of the year. Hence, the possibility to assess soil attributes from space-based remote sensing imagery over vegetated areas is an important prospect, which requires the development of a strategy to remove the vegetation effect to achieve bare soil and reach soils attributes through satellite images in these conditions. To attain bare soil and estimate soil clay content in agriculture areas from Landsat data, Shabou et al.[165] simultaneously analyzed the Landsat, the normalized difference vegetation (NDVI) and MIR data over all test fields to achieve bare soil from satellite images. According to Zhang and Zhou,[163] to obtain quantitative soil moisture under vegetation cover using satellite data, two-source ALEXI models can be utilized to separate the soil and vegetation information and describe the energy exchange among the soil, vegetation, and atmosphere. Garnier et al.[166] showed a relationship between plant’s attributes as responses to soil condition variation. Additionally, Li et al.[167] and Solon et al.[168] investigated the relationship between soil characteristics and vegetation through satellite remote sensing, suggesting that these two elements are closely related. Thus, using vegetation as bioindicators of soil condition can be a solution in vegetated areas.[43] Roelofsen et al.[44] stated that deriving the soil conditions through their impact on the natural vegetation is an efficient alternative, as plant communities usually have a narrow tolerance toward soil factors, making them indicative of the site’s conditions. Regarding the airborne remotesensing approach, Noomen et al.[40] used the hyperspectral airborne Probe-1 data in the VIS-NIR-SWIR region to study the long-term hydrocarbon seepage in soil using vegetation response as a proxy of soil condition. The analysis of natural forest vegetation to obtain soil information based on satellite remote sensing was studied by Arellano et al.[139] and Dematte et al.[164] using Hyperion and Landsat, respectively. Their studies showed that this approach could be an efficient way to reach accurate soil results. However, the need to develop techniques in different regions has been pointed out by most authors, since correlation of vegetation with soils is complex. Indeed, obtaining information from vegetated soil is a different method. On the other hand, vegetation expression shows important inferences and correlations with soil attributes. This methodology can assist soil monitoring in vegetated areas with difficult soil access. Another problem in remote sensing of soil arises from the fact that passive sensors’ images are sensitive to weather conditions. In the presence of clouds and shadows in a pixel, the usability of this pixel is prevented.[169] This problem is more serious in the case of using high-resolution images (Landsat, SPOT, ASTER, etc.) and in areas with high frequency of cloud coverage. In the case of Sentinel-2 also, cloud masking in the absence of a TIR band and the need to combine spatial


21

resolutions for 10 m will be the main limitation.[170] In the case of poor atmospheric correction, problems can also occur due to lower sunlight intensity and limitations of simulated datasets.[171] The revisit-time of satellites can also be an issue, which still needs to be improved. Generally, in comparison to low temporal resolution satellite images, high-resolution ones have more information in their spatial domain, attained at infrequent time intervals.[169] Thus, the procedure of cloud recovery from a high temporal resolution image depends either on the image features[172] or on a limited number of images,[173] while the advantage of high temporal resolution is for low-resolution data. Therefore, many images that cover a short period of time can be processed and the best observations from these images selected, which can be assumed that no cloud has occurred above the area. There is another limitation in soil remote sensing, which is the heterogeneous nature of remote sensing data. The recorded reflectance by remote sensors, especially on large-scale areas, is resulted from the interactions of electromagnetic radiation with multiple constituents within each pixel[174] that consequently constrains the accuracy of spectral analysis and their interpretation for quantifying soil condition. Over the past decade, although numerous studies addressed the mixing problem and proposed analysis techniques,[175,176] spectra unmixing is still challenging and need more development. In addition, a wide range of soil spectral measurements are being gathered around the globe, which provide different outputs due to various measurement conditions including sampling techniques, sample preparation, instrument specifications, different protocols as well as analytical algorithms, which severely affect the prediction performance of spectroscopic models and outputs.[118,177] These variations of condition justify the importance to establish a simple procedure for standardization that minimizes the systematic and random effects and enables unification of spectral libraries.[118,178] A specific limitation concerning soil contamination is the fact that pollutants do not have direct spectral features and that a correlation with soil characteristics is crucial. Moreover, most of the satellites provide spatial resolution of around 30 m, which can be a limitation in soil contamination assessment. All in all, major issues in the use of remote-sensing data, specifically hyper and superspectral sensors information, for soil applications are (i) the evaluation of soil spectral information in vegetation-covered soils[164]; (ii) the sensor errors correction and atmospheric attenuation removal difficulties[179]; (iii) spectral mixing problem and limited available algorithms for unmixing[176]; (iv) the inadequate SNR of some sensors (e.g., the Hyperion SWIR spectral region)[180]; (v) lack of standardization and an agreed-upon protocol[118]; and (vi) incomplete spectral coverage of some sensors (e.g., the CHRIS-PROBA with lack of SWIR bands).[181] Therefore, further works are needed to deal with the above-mentioned limitations and issues.

7. Conclusions and perspective The current study reviewed various studies and clearly reported a high potential of optical proximal and remote sensing techniques in soil pollution monitoring and

22


illustrated their advantages and disadvantages. It also showed that precision mapping and monitoring of soil contaminants using forthcoming innovative remote-sensing space sensors is viable and reliable. However, this information brings a lower spatial resolution than information that can be prepared by an airborne hyperspectral sensor; it provides larger spatial coverage, which combines with short revisit-time and more frequent area coverage. However, future studies should still focus on better exploring the limitations and problems of soil remote sensing. More studies not only need to work on the problem of clouds and cloud shadow detection and removal but also to develop more efficient unmixing algorithms to increase the accuracy of soil contamination assessment. Finding the ideal bands layout of the sensors, which would enable highly precise mine waste and soil contamination mapping is also very much recommended. Furthermore, future developments will be needed for combining different approaches, sensors, and platforms for contaminant monitoring, as the use of multiple sensors and platforms offers the potential for more data acquisition and a denser temporal dataset, mainly with optical sensors, when cloud cover is an obstacle.[182] For example, combining data from sensors with similar spatial and radiometric features such as ASTER, Landsat 7 and 8, or Sentinel 2, which each has repetition periods of days or weeks, can give a solid dataset for high frequency analysis. It needs to be mentioned that, efforts to establish a common protocol and a consistent standard can also be considered as a necessary future work, which will permit the reliability and the comparability of results in order to the building of large SSLs that are beneficial for the entire soil and geology community. Certainly, in the upcoming orbital hyper and superspectral imagers, the enhancement of sensor quality will raise the soil contamination prediction accuracy at the field scale compared to direct sampling prediction. Launching of the next generation of IS sensors including devices in thermal infrared (TIR) range is also expected to bring a major step toward mapping of global soil surface from space. Likewise, better data acquirement timing will enhance the classification of results. In the future, with the aforementioned orbital sensors, solid time series will be accessible. Accordingly, further research and new ideas on how to take advantage of the new proximal and remote sensing tools will need to focus on developing of new algorithms and estimation accuracies[18] for the retrieval of soil contaminants. Technically, future works need to confirm the potential of upcoming space-based sensors in soil science and Earth observation using real and actual data. Ultimately, the use of proximal and remote sensing techniques in different domains as well as the promising performances of the forthcoming hyper and superspectral spaceborne missions will be an opportunity to implement novel and accurate retrieval algorithms in operational processing chains for global quantitative determination of soil contamination.

Acknowledgments The authors wish to thank the partially financial support of the Ministry of Education, Youth and Sport of the Czech Republic projects CENAKVA (Project No. CZ.1.05/2.1.00/01.0024), and


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CENAKVA II (Project No. LO1205 under the NPU I program). The support of the Czech Science Foundation (Project No. 18–28126Y and Project No. 17–27726S) is also appreciated.

Funding e Republiky (CZ) 18-28126Y; Ministerstvo Skolstvı, Mladeze a Grantova Agentura Cesk Telovychovy LO1205 under the NPU I program; Ministerstvo Skolstvı, Mladeze a Telovychovy e Republiky 17-27726S. CZ.1.05/2.1.00/01.0024; Grantova Agentura Cesk

ORCID Asa Gholizadeh http://orcid.org/0000-0003-4419-5463 Mohammadmehdi Saberioon http://orcid.org/0000-0003-1627-4957 Eyal Ben-Dor http://orcid.org/0000-0001-6757-3530

References [1] Sahoo, P. K., Tripathy, S., Equeenuddin, S. M., and Panigrahi, M. K. (2012). Geochemical characteristics of coal mine discharge vis-a-vis behavior of rare earth elements at Jaintia Hills Coalfield, Northeastern India. Journal of Geochemical Exploration 112, 235–243. doi.org/10.1016/j.gexplo.2011.09.001. [2] Chuncai, Z., Guijian, L., Dun, W., Ting, F., Ruwei, W., and Xiang, F. (2014). Mobility behavior and environmental implications of trace elements associated with coal gangue: A case study at the Huainan Coalfield in China. Chemosphere 95, 193–199. doi.org/ 10.1016/j.chemosphere.2013.08.065. [3] Cao, M., Zhang, Q., and Shugart, H. H. (2001). Dynamic responses of African ecosystem carbon cycling to climate change. Climate Research 17, 183–193. doi.org/10.3354/ cr017183. [4] Vagen, T. G., Winowiecki, L. A., Tondoh, J. E., Desta, L. T., and Gumbricht, T. (2016). Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263, 216–225. doi.org/10.1016/j.geoderma.2015.06.023. [5] Okrent D. (1999). On intergenerational equity and its clash with intragenerational equity and on the need for policies to guide the regulation of disposal of wastes and other activities posing very long time risks. Risk Analysis 19, 877–901. doi.org/10.1111/j. 1539-6924.1999.tb00449.x. [6] Noomen, M. F., Skidmore, A. K., Van der Meer, F. D., and Prins, H.H.T. (2006). Continuum removed band depth analysis for detecting the effects of natural gas, methane and ethane on maize reflectance. Remote Sensing of Environment 105, 262–270. doi.org/ 10.1016/j.rse.2006.07.009. [7] Gotze, C., Glasser, C., and Jung, A. (2016). Detecting heavy metal pollution of floodplain vegetation in a pot experiment using reflectance spectroscopy. International Journal of River Basin Management, 14(4), 1–24. doi.org/10.1080/15715124.2016.1205077. [8] Li, M. S. (2006). Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: A review of research and practice. Science of the Total Environment 357, 38–53. doi.org/10.1016/j.scitotenv.2005.05.003. [9] Strudl, M., Boruvka, L., Dimitrovsky, K., and Kozak, J. (2006). Content of potentially risk elements in natural and reclaimed soils of Sokolov region. Soil and Water Research 1, 99–107.

24


[10] Chen, T., Chang, Q., Clevers, J.G.P.W., and Kooistra, L. (2015). Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy. Environmental Pollution 206, 217–226. doi.org/10.1016/j.envpol.2015.07.009. [11] N’Guessan, Y. M., Probst, J. L., Bur, T., and Probst, A. (2009). Trace elements in stream bed sediments from agricultural catchments (Gascogne region, S-W France): Where do they come from? Science of Total Environment 407, 2939–2952. doi.org/10.1016/j. scitotenv.2008.12.047. [12] Sahoo, P. K., Equeenuddin, S. M., and Powell, M.A. (2016). Trace elements in soils around coal mines: Current scenario, impact and available techniques for management. Current Pollution Reports 2, 1–14. doi.org/10.1007/s40726-016-0025-5. [13] Schmid, T., Rodriguez-Rastrero, M., Escribano, P., Palacios-Orueta, A., Ben-Dor, E., Plaza, A., Milewski, R., Huesca, M., Bracken, A., Cicuendez, V., Pelayo, M., and Chabrillat, S. (2015). Characterization of soil erosion indicators using hyperspectral data from a Mediterranean rainfed cultivated region. IEEE Journal of Selected Topics in Applied. Earth Observations and Remote Sensing 9(2), 845–860. doi.org/10.1109/JSTARS.2015. 2462125. [14] Ren, H. Y., Zhuang, D. F., Singh, A. N., Pan, J. J., Qid, D. S., and Shi, R. H. (2009). Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study. Pedosphere 19, 719–726. doi.org/10.1016/S1002-0160 (09)60167-3. [15] Gholizadeh, A., Boruvka, L., Vasat, R., Saberioon, M. M., Klement, A., Kratina, J., Tejnecky, V., and Drabek, O. (2015). Estimation of potentially toxic elements contamination in anthropogenic soils on a brown coal mining dumpsite by reflectance spectroscopy: A case study. PLOS ONE 10(2), e0117457. doi.org/10.1371/journal.pone.0117457. [16] Forrester, S., Janik, L., McLaughlin, M., and Gilkes, R. J. (2010). An infrared spectroscopic test for total petroleum hydrocarbon (TPH) contamination in soils. Proceedings of the 19th World Congress of Soil Science: Soil solutions for a changing World, Brisbane, Australia. [17] Forrester, S. T., Janik, L. J., McLaughlin, M. J., Soriano-Disla, J. M., Stewart, R., and Dearman, B. (2013). Total petroleum hydrocarbon concentration prediction in soils using diffuse reflectance infrared spectroscopy. Soil Science Society of American Journal, 77(2), 450–460. doi.org/10.2136/sssaj2012.0201. [18] Douglas, R. K., Nawar, S., Alamar, M. C., Coulon, F., and Mouazen, A.M. (2017). Almost 25 years of chromatographic and spectroscopic analytical method development for petroleum hydrocarbons analysis in soil and sediment: State-of-the-art, progress and trends. Critical Reviews in Environmental Science and Technology, 47(16), 1497–1527 2017. doi.org/10.1080/10643389.2017.1385368. [19] Correa Pabon, R. E., and de Souza Filho, C. R. (2016). Spectroscopic characterization of red latosols contaminated by petroleum-hydrocarbon and empirical model to estimate pollutant content and type. Remote Sensing of Environment 175, 323–336. doi.org/ 10.1016/j.rse.2016.01.005. [20] Xie, X., Pan, X. Z., and Sun, B. (2012). Visible and near-infrared diffuse reflectance spectroscopy for prediction of soil properties near a Copper smelter. Pedosphere 22, 351–366. doi.org/10.1016/S1002-0160(12)60022-8. [21] Goodchild, M. (1992). Geographical data modeling. Computer & Geosciences 18(4), 401–408. doi.org/10.1016/0098-3004(92)90069-4. [22] Grauer-Gray, J., and Hartemink, A. E. (2018). Raster sampling of soil profiles. Geoderma 318(2018), 99–108. doi.org/10.1016/j.geoderma.2017.12.029.


25

[23] Weindorf, D. C., Bakr, N., and Zhu, Y. (2014). Advances in portable X-ray fluorescence (PXRF) for environmental, pedological and agronomic applications. Advances in Agronomy 128, 1–45. doi.org/10.1016/B978-0-12-802139-2.00001-9. [24] Wu, Y., Chen, J., Wu, X., Tian, Q., Ji, J., and Qin, Z. (2005). Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Applied Geochemistry 20, 1051–1059. doi.org/10.1016/j.apgeochem.2005.01. 009. [25] Choe, E., Van der Meer, F., Van Ruitenbeek, F., Van der Werff, H., de Smeth, B., and Kim, K. W. (2008). Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain. Remote Sensing of Environment, 112(7), 3222–3233. doi.org/10.1016/j.rse.2008.03.017. [26] Wu, Y., Chen, J., Ji, J., Gong, P., Liao, Q., Tian, Q., and Ma, H. (2007). A mechanism study of reflectance spectroscopy for investigating heavy metals in soil. Soil Science Society of American Journal 17, 918–926. doi.org/10.2136/sssaj2006.0285. [27] Saghatelyan, A., Sahakyan, L., Asmaryan, S., Amirkhanyan, M., and Muradyan, V. (2009). Remote sensing in geological assessment of heavy metal pollution of territories. Proceeding from 5th International Conference EMAN: Environmental accounting-sustainable development indicators, Prague, Czech Republic. [28] Asmaryan, S., Muradyan, V., Sahakyan, L. V., Saghatelyan, A. K., and Warner, T. (2014). Development of remote sensing methods for assessing and mapping soil pollution with heavy metals, In D. Arrouays, N. McKenzie, J. Hempel, A.C.R. de Forges, and A. McBratney (Eds.), Basis of the global spatial soil information system. Orleans, France: Proceeding of the 1st GlobalSoilMap Conference. [29] Buckingham, R., and Staenz, K. (2008). Review of current and planned civilian space hyperspectral sensors for EO. Canadian Journal of Remote Sensing, 34, s187–s197. doi. org/10.5589/m08-002. [30] Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F., and Hanssen, R. F. (2012). ESA’s Sentinel missions in support of Earth system science. Remote Sensing of Environment 120, 84–90. doi.org/10.1016/j.rse.2011.07.023. [31] Malenovsky, Z., Rott, H., Cihlar, J., Schaepman, M., Garcia-Santos, G., Fernandes, R., and Berger, M. (2012). Sentinels for science: Potential of sentinel-1, 2, and 3 missions for scientific observations of ocean, cryosphere, and land. Remote Sensing of Environment, 120, 91–101. doi.org/10.1016/j.rse.2011.09.026. [32] Sanchez, N., Alonso-Arroyo, A., Martinez-Fernandez, J., Piles, M., Gonzalez-Zamora, A., Camps, A., and Vall-llosera, M. (2015). On the synergy of airborne GNSS-R and Landsat 8 for soil moisture estimation. Remote Sensing, 7(8), 9954–9974. doi.org/10.3390/ rs70809954. [33] Castaldi, F., Palombo, A., Santini, F., Pascucci, S., Pignatti, S., and Casa, R. (2016). Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing of Environment, 179, 54–65. doi.org/10.1016/j.rse.2016.03.025. [34] Shi, T., Wang, J., Chen, Y., and Wu, G. (2016). Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants. International Journal of Applied Earth Observation and Geoinformation 52, 95–103. doi.org/10.1016/j.jag.2016.06.002. [35] Banat, K. M., Howari, F. M., and Al-Hamada, A. A. (2005). Heavy metals in urban soils of central Jordan: Should we worry about their environmental risks? Environmental Research 97, 258–73. doi.org/10.1016/j.envres.2004.07.002.

26


[36] Zhang, Z. Y., Abuduwaili, J., and Jiang, F. Q. (2013). Determination of occurrence characteristics of heavy metals in soil and water environments in Tianshan mountains, central Asia. Analytical Letters 46, 2022–2031. doi.org/10.1080/00032719.2013.784919. [37] Rashid, H., Shanto, M.A.S., Roy, D. R., Hossain, M. S., Islam, M. S., Hoque, M.M.M., and Urbi, Z. (2014). Impact of coal mining on soil, water and agricultural crop production: A cross-sectional study on Barapukuria coal mine industry, Dinajpur, Bangladesh. Journal of Environmental Science Research 1(1), 16. [38] Van der Meer, F., Van Dijk, P., Van der Werff, H., and Yang, H. (2002). Remote sensing and petroleum seepage: A review and case study. Terra Nova 14(1), 1–17. doi.org/ 10.1046/j.1365-3121.2002.00390.x. [39] Sanches, I. D., Souza Filho, C. R., Magalhaes, L. A., Quiterio, G.C.M., Alves, M. N., and Oliveira, W. J. (2013). Unravelling remote sensing signatures of plants contaminated with gasoline and diesel: An approach using the red edge spectral feature. Environmental Pollution 174, 16–27. doi.org/10.1016/j.envpol.2012.10.029. [40] Noomen, M. F., Van der Werff, H.M.A., and Van der Meer, F. D. (2012). Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage. Ecological Informatics 8, 55–64. doi.org/10.1016/j.ecoinf.2012.01.001. [41] Schumacher, D. (1996). Hydrocarbon-induced alteration of soils and sediments. In D. Schumacher, and M.A. Abrams (Eds.), Hydrocarbon migration and its near surface expression (vol. 66, pp. 71–89). USA: AAPG Memoir. [42] Steven, M. D., Smith, K. L., Beardsley, M. D., and Colls, J. J. (2006). Oxygen and methane depletion in soil affected by leakage of natural gas. European Journal of Soil Science 57 (6), 800–807. doi.org/10.1111/j.1365-2389.2005.00770.x. [43] Zinnert, J. C., Via, S. M., and Young, D. R. (2013). Distinguishing natural from anthropogenic stress in plants: Physiology, fluorescence and hyperspectral reflectance. Plant Soil 366, 133–141. doi.org/10.1007/s11104-012-1414-1. [44] Roelofsen, H. D., Van Bodegom, P.M., Kooistra, L., Van Amerongen, J. J., and Witte, J. P.M. (2015). An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments. International Journal of Applied Earth Observation and Geoinformation 43, 149–159. doi.org/10.1016/j.jag.2015.05.005. [45] Akcil, A., and Koldas, S. (2006). Acid Mine Drainage (AMD): Causes, treatment and case studies. Journal of Cleaner Production 14, 1139–1145doi.org/10.1016/j.jclepro. 2004.09.006. [46] Gomes, M.E.P., and Kavas, P.J.C. (2006). Mineralogical controls on mine drainage of the abandoned Ervedosa tin mine in north-eastern Portugal. Applied Geochemistry 21, 1322–1334. doi.org/10.1016/j.apgeochem.2006.06.007. [47] Swayze, G. A., Smith, K. S., Clark, R. N., Sutley, S. J., Pearson, R. M., Vance, J. S., Hageman, P. L., Briggs, P. H., Meier, A. L., Singleton, M. J., and Roth, S. (2000). Using imaging spectroscopy to map acidic mine waste. Environmental Science & Technology 34(1), 47–54. doi.org/10.1021/es990046w. [48] Bussinow, M., Sarapatka, B., and Dlapa, P. (2012). Chemical degradation of forest soil as a result of polymetallic ore mining activities. Polish Journal of Environmental Studies 21, 1551–1561. [49] Song, Y., Li, F., Yang, Z., Ayoko, G. A., Frost, R. L., and Ji, J. (2012). Diffuse reflectance spectroscopy for monitoring potentially toxic elements in the agricultural soils of Changjiang river delta, China. Applied Clay Science 64, 75–83. doi.org/10.1016/j.clay. 2011.09.010. [50] Galunin, E., Ferreti, J., Zapelini, I., Vieira, I., Tarley, C.R.T., Abrao, T., and Santos, M. J. (2014). Cadmium mobility in sediment and soils from coal mining area on Tibagi river


[51]

[52]

[53]

[54]

[55]

[56]

[57]

[58]

[59]

[60] [61] [62]

[63]

[64] [65]

[66]

27

watershed: Environmental risk assessment. Journal of Hazard Materials 265, 280–287. doi.org/10.1016/j.jhazmat.2013.11.010. Tozsin, G. (2014). Hazardous elements in soil and coal from the Oltu coal mine district, Turkey. International Journal of Coal Geology 131, 1–6. doi.org/10.1016/j.coal. 2014.05.011. Pentari, D., Typou, J., Goodarzi, F., and Foscolos, A. E. (2006). Comparison of elements of environmental concern in regular and reclaimed, near abandoned coal mines Ptolemais-Amynteon, northern Greece: Impact on wheat crops. International Journal of Coal Geology 65, 51–8. doi.org/10.1016/j.coal.2005.04.008. Ribeiro, J., Silva, E. F., Ward, L. C., and Flores, D. (2010). Petrographic, mineralogical and geochemical characterization of the Serrinhal coal waste pile (Douro Coalfield, Portugal) and the potential environmental impacts on soil, sediments and surface waters. International Journal of Coal Geology 83, 456–66. doi.org/10.1016/j. coal.2010.06.006. Pietrzykowski, M., Socha, J., and Van Doorn, N. S. (2014). Linking heavy metal bioavailability (Cd, Cu, Zn and Pb) in Scots pine needles to soil properties in reclaimed mine area. Science of the Total Environment 470-471, 501–510. Ge, Y., Cui, X., and Bai, Z. (2008). Evaluation on potential ecological risk of heavy metals pollution in reclaimed soil of opencast-taking Pingshuo opencast mine as an example. Journal of Shanxi Agricultural University, 28, 85–8. You, M., Huang, Y., Lu, J., and Li, C. (2014). Characterization of heavy metals in soil near coal mines and a power plant in Huainan, China. Analytical Letters 48(4), 726–737. doi. org/10.1080/00032719.2014.940531. Das, S. K., and Chakrapani, G. J. (2011). Assessment of trace metal toxicity in soils of Raniganj Coalfield, India. Environmental Monitoring and Assessment 177, 63–71. doi. org/10.1007/s10661-010-1618-x. Equeenduddin, S. M. (2010). Control of coal and overburden on acid mine drainage and metal mobilization at Makum coalfield, Assam, India. Doctor of Philosophy Dissertation, Kharagpur, India, Indian Institute of Technology. Chen, J., Wei, F., Zheng, C., Wu, Y., and Adriano, D. C. (1991). Background concentrations of elements in soils of China. Water, Air, & Soil Pollution 57-58, 699–712. doi.org/ 10.1007/BF00282934. Shacklette, H. T., and Boerngen, J. G. (1984). Element concentrations in soils and other surficial materials of the conterminous United States. USGS Professional Paper 1270. CCME (Canadian Council of Ministers of the Environment). (1991). Soil Quality Guidelines. Smith, K. L., Steven, M. D., and Colls, J. J. (2004). Spectral responses of pot-grown plants to displacement of soil oxygen. International Journal of Remote Sensing 25(20), 4395– 4410. doi.org/10.1080/01431160410001729172. Lausch, A., Erasmi, S., King, D. J., Magdon, P., and Heurich, M. (2016). Understanding forest health with remote sensing—Part I—A review of spectral traits, processes and remote sensing characteristics. Remote Sensing 8(1), 1029. doi.org/10.3390/rs8121029. Viscarra Rossel, R. A., McBratney, A. B., and Minasny, B. (2010). Proximal soil sensing (p. 448). Dordrecht, Netherlands: Springer. Shepherd, K. D., and Walsh, M. G. (2002). Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66(3), 988–998. doi.org/10.2136/sssaj2002.9880. Bellinaso, H., Dematte, J.A.M., and Araujo, S. R. (2010). Spectral library and its use in soil classification. Revista Brasileira de Ciência do Solo 34, 861–870. doi.org/10.1590/S010006832010000300027.

28


[67] Gholizadeh, A., Amin, M.S.M., Boruvka, L., and Saberioon, M. M. (2014). Models for estimating the physical properties of paddy soil using visible and near-infrared reflectance spectroscopy. Journal of Applied Spectroscopy 81(3), 534–540. doi.org/10.1007/ s10812-014-9966-x. [68] Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D. J., Dematte, J.A.M., Shepherd, K. D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aichi, H., Barthes, B. G., Bartholomeus, H. M., Bayer, A.D., Bernoux, M., Bottcher, K., Brodsky, L., Du, C. W., Chappell, A., Fouad, Y., Genot, V., Gomez, C., Grunwald, S., Gubler, A., Guerrero, C., Hedley, C. B., Knadel, M., Morras, H.J.M., Nocita, M., Ramirez-Lopez, L., Roudier, P., Rufasto Campos, E. M., Sanborn, P., Sellitto, V. M., Sudduth, K. A., Rawlins, B. G., Walter, C., Winowiecki, L. A., Hong, S. Y., and Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews 155, 198–230. doi.org/10.1016/j.earscirev.2016.01.012. [69] Bowers, S., and Hanks, R. J. (1965). Reflectance of radiant energy from soils. Soil Science 100, 130–138. doi.org/10.1097/00010694-196508000-00009. [70] Hunt, G. R. (1982). Spectoscopic properties of rock and minerals. In C. R. Stewart (Ed.), Handbook of physical properties rocks (p. 295). Boca, Raton: CRC Press. [71] Dalal, R. C., and Henry, R. J. (1986). Simultaneous determination of moisture, organic carbon and total nitrogen by near-infrared reflectance spectroscopy. Soil Science Society of America Journal 50, 120–123. doi.org/10.2136/sssaj1986.03615995005000010023x. [72] Ben-Dor, E., and Banin, A. (1995). Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal 59(2), 364–372. doi.org/10.2136/sssaj1995.03615995005900020014x. [73] Nanni, M. R., and Dematte, J.A.M. (2006). Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal 70(2), 393–407. doi.org/10.2136/sssaj2003.0285. [74] Scheidt, S., Ramsey, M. S., and Lancaster, N. (2010). Determining soil moisture and sediment availability at White Sands Dune Field, NM from apparent thermal inertia (ATI) data. Journal of Geophysical Research 115, F02019. doi.org/10.1029/2009JF001378. [75] Sobrino, J. A., Franch, B., Mattar, C., Jimenez-Munoz, J. C., and Corbari, C. (2012). A method to estimate soil moisture from Airborne Hyperspectral Scanner (AHS) and ASTER data: Application to SEN2FLEX and SEN3EXP campaigns. Remote Sensing of Environment 117, 415–428. doi.org/10.1016/j.rse.2011.10.018. [76] Gholizadeh, A., Amin, M.S.M., Saberioon, M. M., and Boruvka, L. (2013). Visible and near-infrared reflectance spectroscopy to determine chemical properties of paddy soils. Journal of Food, Agriculture and Environment 11(2), 859–866. [77] Ben-Dor, E., Inbar, Y., and Chen, Y. (1997). The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sensing of Environment 61, 1–15. doi.org/ 10.1016/S0034-4257(96)00120-4. [78] Kooistra, L., Wehren, R., Leuven, R.S.E., and Buydens, L.M.C. (2001). Possibilities of visible-near-infrared spectroscopy for the assessment of soil contamination in river flood plains. Analytica Chimica Acta 446, 97–105. doi.org/10.1016/S0003-2670(01)01265-X. [79] Malley, D. F., and Williams, P. C. (1997). Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environmental Science & Technology 31(12), 3461–3467. doi.org/10.1021/es970214p. [80] Ben-Dor, E., Irons, J. A., and Epema, A. (1999). Soil spectroscopy. In A. Rencz (Ed.), Manual of remote sensing, 3rd Edition. New York: J. Wiley & Sons Inc. [81] Jia, J., Song, Y., Yuan, X., and Yang, Z. (2010). Diffuse reflectance spectroscopy study of heavy metals in agricultural soils of the Changjiang river delta, China. Proceeding of 19th World Congress of Soil Science. Brisbane, Australia.


29

[82] Dematte, J.A.M., Gama, M.A.P., Cooper, M., Araujo, J. C., Nanni, M. R., and Fiorio, P. R. (2004). Effect of fermentation residue on the spectral reflectance properties of soils. Geoderma, 120(3-4), 187–200. doi.org/10.1016/j.geoderma.2003.08.016. [83] Vohland, M., Bossung, C., and Frund, H. C. (2009). A spectroscopic approach to assess trace heavy metal contents in contaminated floodplain soils via spectrally active soil components. Journal of Plant Nutrition and Soil Science 172(2), 201–209. doi.org/10.1002/ jpln.200700087. [84] Schwartz, G., Eshel, G., and Ben-Dor, E. (2011). Reflectance spectroscopy as a tool for monitoring contaminated soils. InTechOpen, 67–90. [85] Gholizadeh, A., Boruvka, L., Saberioon, M. M., and Vasat, R. (2013). Visible, near-infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: State-of-the-art and key issues. Applied Spectroscopy 67, 1349–1362. doi.org/10.1366/13-07288. [86] Kooistra, L., Leuven, R.S.E.W., Nienhuis, P. H., Wehrens, R., Buydens, L.M.C., Salas, E.A. L., and Clevers, J.G.P.W. (2004). Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. Environmental Pollution 127, 281–290. doi.org/ 10.1016/S0269-7491(03)00266-5. [87] Xia, X. Q., Mao, Y. Q., Ji, J. F., Ma, H. R., Chen, J., and Liao, Q. L. (2007). Reflectance spectroscopy study of Cd contamination in the sediments of the Changjiang river, China. Environmental Science & Technology 41, 3449–3454. [88] Kemper, T., and Sommer, S. (2002). Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environmental Science & Technology 36, 2742–2747. doi.org/10.1021/es015747j. [89] Bray, J.G.P., Viscarra Rossel, R. A., and McBratney, A. B. (2009). Diagnostic screening of urban soil contaminants using diffuse reflectance spectroscopy. Australian Journal of Soil Research 47, 433–442. doi.org/10.1071/SR08068. [90] Choe, E., Kim, K. W., Bang, S., Yoon, I. H., and Lee, K. Y. (2009). Qualitative analysis and mapping of heavy metals in an abandoned Au-Ag mine area using NIR spectroscopy. Environmental Geology 58(3), 477–482. doi.org/10.1007/s00254-0081520-9. [91] Pandit, C. M., Filippelli, G. M., and Li, L. (2010). Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression. International Journal of Remote Sensing 31(15), 4111–4123. doi.org/10.1080/014311 60903229200. [92] Cloutis, E. A. (1989). Spectral reflectance properties of hydrocarbons: Remote sensing implications. Science 245, 165–168. doi.org/10.1126/science.245.4914.165. [93] Okparanma, R. N., and Mouazen, A.M. (2013). Visible and near-infrared spectroscopy analysis of a polycyclic aromatic hydrocarbon in soils. Scientific World Journal 2013(2), 160360. [94] Chakraborty, S., Weindorf, D. C., Zhu, Y. D., Li, B., Morgan, C.L.S., Ge, Y. F., and Galbraith, J. (2012). Spectral reflectance variability from soil physicochemical properties in oil contaminated soils. Geoderma, 177, 80–89. doi.org/10.1016/j.geoderma.2012. 01.018. [95] Chakraborty, S., Weindorf, D. C., Li, B., Ali, M. N., Majumdar, K., and Ray, D. P. (2014). Analysis of petroleum contaminated soils by spectral modeling and pure response profile recovery of n-hexane. Environmental Pollution 190, 10–18. doi.org/10.1016/j. envpol.2014.03.005. [96] Winkelmann, K. H. (2005). On the applicability of imaging spectrometry for the detection and investigation of contaminated sites with particular consideration given to the

30

[97]

[98] [99]

[100]

[101]

[102]

[103]

[104]

[105]

[106]

[107]

[108]

[109]


detection of fuel hydrocarbon contaminants in soil. Doctor of Philosophy Dissertation, Brandenburg, Germany, University of Technology. Schwartz, G., Ben-Dor, E., and Eshel, G. (2012). Quantitative analysis of total petroleum hydrocarbons in soils: Comparison between reflectance spectroscopy and solvent extraction by 3 certified laboratories. Applied and Environmental Soil Science 2012, 1–11. doi. org/10.1155/2012/751956. Elachi, C., and Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing. California, USA: John Wiley & Sons. Kruse, F. A., Boardman, J. W., and Huntington, J. F. (2003). Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Transactions on Geoscience and Remote Sensing 41, 1388–1400. doi.org/10.1109/TGRS.2003. 812908. Mulder, V. L., de Bruin, S., Schaepman, M.E, and Mayr, T. R. (2011). The use of remote sensing in soil and terrain mapping—A review. Geoderma 162(1), 1–19. doi.org/10.1016/ j.geoderma.2010.12.018. Deng, Y., Wu, C., Li, M., and Chen, R. (2015). RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation 39, 40–48. doi.org/10.1016/j. jag.2015.02.010. Clasen, A., Somers, B., Pipkins, K., Tits, L., Segl, K., Brell, M., Kleinschmit, B., Spengler, D., Lausch, A., and Forster, M. (2015). Spectral unmixing of forest crown components at close range, airborne and simulated sentinel-2 and EnMAP spectral imaging scale. Remote Sensing 7(11), 15361–15387. doi.org/10.3390/rs7111 5361. Berni, J.A.J., Zarco-Tejada, P. J., Suarez, L., Gonzalez-Dugo, V., and Fereres, E. (2009). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38, 1–6. Zhang, C., and Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture 13, 693–712. doi.org/10.1007/ s11119-012-9274-5. Colomina, I., and Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 92, 79– 97. doi.org/10.1016/j.isprsjprs.2014.02.013. Faye, E., Rebaudo, F., Yanez-Cajo, D., Cauvy-Fraunie, S., and Dangles, O. (2016). A toolbox for studying thermal heterogeneity across spatial scales: From unmanned aerial vehicle imagery to landscape metrics. Methods in Ecology and Evolution 7, 437–446. doi.org/ 10.1111/2041-210X.12488. Severtson, D., Callow, N., Flower, K., Neuhaus, A., Olejnik, M., and Nansen, N. (2016). Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture 17(6), 659–677. doi.org/ 10.1007/s11119-016-9442-0. Peter, K. D., d’Oleire-Oltmanns, S., Ries, J. B., Marzolff, I., and Ait Hssaine, A. (2014). Soil erosion in gully catchments affected by land-levelling measures in the Souss Basin, Morocco, analysed by rainfall simulation and UAV remote sensing data. Catena 113, 24–40. doi.org/10.1016/j.catena.2013.09.004. Turner, D., Lucieer, A., and de Jong, S. M. (2015). Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sensing 7(2), 1736–1757. doi.org/10.3390/rs70201736.


31

[110] Sanada, Y., Orita, T., and Torii, T. (2016). Temporal variation of dose rate distribution around the Fukushima Daiichi nuclear power station using unmanned helicopter. Applied Radiation and Isotopes 118, 308–316. doi.org/10.1016/j.apradiso. 2016.09.008. [111] Martin, P. G., Kwong, S., Smith, N. T., Yamashiki, Y., Payton, O. D., Russell-Pavier, F. S., Fardoulis, J. S., Richards, D. A., and Scott, T. B. (2016). 3D unmanned aerial vehicle radiation mapping for assessing contaminant distribution and mobility. International Journal of Applied Earth Observation and Geoinformation 52, 12–19. doi.org/10.1016/j. jag.2016.05.007. [112] Rossi, M., and Brunelli, D. (2015). Autonomous gas detection and mapping with unmanned aerial vehicles. IEEE Transactions on Instrumentation and Measurement 65 (4), 1–11. [113] Mochammad, F., Putra, A. R., and Trilaksono, B. R. (2016). Implementation of hazardous chemical gas monitoring system using unmanned aerial vehicle (UAV). Proceeding of 6th International Conference on System Engineering and Technology (ICSET), Bandung, Indonesia. [114] Chinchu, V. J., Sheela, K. G., and Manu, A. (2017). Implementation of gas detection system using unmanned moving vehicle. International Advanced Research Journal in Science, Engineering and Technology 4(2), 121–123. doi.org/10.17148/IARJSET. 2017.4227. [115] Nieke, J., Schwarzer, H., Neumann, A., and Zimmermann, G. (1997). Imaging spaceborne and airborne sensor systems in the beginning of the next century. Symposium of Aerospace Remote Sensing. IEEE Conference of Sensors, Systems and Next Generation Satellites III, London, U.K. [116] Birk, R. J., and McCord, T. B. (1994). Airborne hyperspectral sensor systems. IEEE Aerospace and Electronic Systems Magazine 9, 26–33. doi.org/10.1109/62.318881. [117] Gianinetto, M., and Lechi, G. (2004). The development of superspectral approaches for the improvement of land cover classification. IEEE Transactions on Geoscience and Remote Sensing 42(11), 2670–2679. doi.org/10.1109/TGRS.2004.835347. [118] Ben-Dor, E., Ong, C., and Lau, I. C. (2015). Reflectance measurements of soils in the laboratory: Standards and protocols. Geoderma 245-246, 112–124. doi.org/10.1016/j. geoderma.2015.01.002. [119] Gomez, C., Gholizadeh, A., Boruvka, L., and Lagacherie, P. (2016). Using legacy data for correction of soil surface clay content predicted from VNIR/SWIR hyperspectral airborne images. Geoderma 276, 84–92. doi.org/10.1016/j.geoderma. 2016.04.019. [120] Horig, B., Kuhn, F., Oschutz, F., and Lehmann, F. (2001). HyMap hyperspectral remote sensing to detect hydrocarbons. International Journal of Remote Sensing 22, 1413–1422. doi.org/10.1080/01431160120909. [121] Reusen, L., Bertels, S., Debacker, W., Debruyn, P., Scheunders, S., Sterckx, and Van den Broek, W. (2003). Detection of stressed vegetation for mapping heavy metal polluted soil. The 3rd EARSeL Workshop on Imaging Spectroscopy, Oberpfaffenhofen, Germany. [122] Pascucci, S., Belviso, C., Cavalli, R. M., Palombo, A., Pignatti, S., and Santini, F. (2012). Using imaging spectroscopy to map red mud dust waste: The podgorica Aluminum complex case study. Remote Sensing of Environment 123, 139–154. doi.org/10.1016/j. rse.2012.03.017. [123] Cocks, T., Jenssen, R., Stewart, A., Wilson, I., and Shields, T. (1998). The HyMapTM airborne hyperspectral sensor: The system, calibration and performance. Proceeding of 1st EARSeL Workshop on Imaging Spectroscopy. Zurich, Switzerland.

32


[124] Chevrel, S., Kuosmannen, V., Belocky R., Marsh, S., Tukiainen, T., Mollat, H., Quental, L., Vosen, P., Schumacher, V., Kuronen, E., and Aastrup, P. (2001). Hyperspectral airborne imagery for mapping mining-related contaminated areas in various European environments—First results of the MINEO project. 5th International Airborne Remote Sensing Conference, San Francisco, California, USA. [125] Kemper, T., and Sommer, S. (2003). Mapping and monitoring of residual heavy metal contamination and acidification risk after the Aznalcollar mining accident (Andalusia, Spain) using field and airborne hyperspectral data. Proceedings of 3rd EARSeL Workshop on Imaging Spectroscopy, Paris, France. [126] Fischer, C., and Busch, W. (2002). Monitoring of environmental changes caused by hardcoal mining. Proceedings of SPIE 4545, 64–72. [127] Van der Werff, H., Van der Meijde, M., Van der Meer, F., and Groothuis, G. J. (2008). A spatial-spectral approach for visualization of vegetation stress resulting from pipeline leakage. Sensors 8(6), 3733–3743. doi.org/10.3390/s8063733. [128] Goetz, A.F.H., Vane, G., Solomon, J. E., and Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science 228, 1147–1153. doi.org/10.1126/science.228. 4704.1147. [129] Vane, G., and Goetz, A.F.H. (1993). Terrestrial imaging spectrometry: Current status, future trends. Remote Sensing of Environment 44, 117–126. doi.org/10.1016/0034-4257 (93)90011-L. [130] Mielke, C., Boesche, N. K., Rogass, C., Kaufmann, H., Gauert, C., and de Wit, M. (2014). Spaceborne mine waste mineralogy monitoring in South Africa, applications for modern Push-Broom missions: Hyperion/OLI and EnMAP/Sentinel-2. Remote Sensing 6(8), 6790–6816. doi.org/10.3390/rs6086790. [131] Yokoya, N., Chan, J.C.W., and Segl, K. (2016). Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated EnMAP and Sentinel-2 images. Remote Sensing 8(3), 172. doi.org/10.3390/rs8030172. [132] Goetz, A.F.H., and Rowan, L. C. (1981). Geologic remote sensing. Science 211, 781–791. doi.org/10.1126/science.211.4484.781. [133] Abrams, M. (2000). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): Data products for the high spatial resolution imager on NASA’s Terra platform. International Journal of Remote Sensing 21, 847–859. doi.org/10.1080/ 014311600210326. [134] Abrams, M., and Hook, S. J. (1995). Simulated ASTER data for geologic studies. IEEE Transactions on Geoscience and Remote Sensing 33, 692–699. doi.org/10.1109/36.387584. [135] Yamaguchi, Y., Kahle, A. B., Tsu, H., Kawakami, T., and Pniel, M. (1998). Overview of advanced spaceborne thermal emission and reflection radiometer (ASTER). IEEE Transactions on Geoscience and Remote Sensing 36, 1062–1071. doi.org/10.1109/36.700991. [136] Mars, J. C., and Rowan, L. C. (2010). Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals. Remote Sensing of Environment 114, 2011–2025. doi.org/10.1016/j.rse.2010.04.008. [137] Levesque, J., Staenz, K., and Szeredi, T. (2000). The impact of spectral band characteristics on unmixing of CASI data for monitoring mine tailings site rehabilitation. Canadian Journal of Remote Sensing 26, 231–240. doi.org/10.1080/07038992.2000.10874772. [138] Chevrel, S., Bourguignon, A., Cottard, F., and Itard, Y. (2005). Exploitation of ASTER imagery in mining-related environmental management. Pecora 16, Global Priorities in Land Remote Sensing, Sioux Falls, South Dakota, USA. [139] Arellano, P., Tansey, K., Balzter, H., and Boyd, D. S. (2015). Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution 205, 225–239. doi.org/10.1016/j.envpol.2015.05.041.


33

[140] Rogge, D., Rivard, B., Grant, B., and Feng, J. (2014). Mapping of Ni-Cu–PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada. Remote Sensing of Environment 152, 302–317. doi.org/10.1016/j.rse.2014.06.024. [141] Kruse, F. A., and Perry, S. L. (2013). Mineral mapping using simulated Worldview-3 short-wave-infrared imagery. Remote Sensing 5, 2688–2703. doi.org/10.3390/rs5062688. [142] Van der Werff, H., and Van der Meer, F. (2015). Sentinel-2 for mapping iron absorption feature parameters. Remote Sensing 7, 12635–12653. doi.org/10.3390/rs71012635. [143] Castaldi, F., Casa, R., Castrignann, A., Pascucci, S., Palombo, A., and Pignatti, S. (2014). Estimation of soil properties at the field scale from satellite data: A comparison between spatial and non-spatial techniques. European Journal of Soil Science 65, 842–851. doi.org/ 10.1111/ejss.12202. [144] Wulder, M.A., and Coops, N. C. (2014). Satellites: Make earth observations open access. Nature 513, 30–31. doi.org/10.1038/513030a. [145] Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M. H., Femenias, P., Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., Nieke, J., Rebhan, H., Seitz, B., Stroede, J., and Sciarra, R. (2012). The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sensing of Environment 120, 37–57. doi.org/10.1016/j. rse.2011.07.024. [146] Verrelst, J., Camps-Valls, G., Munoz-Mari, J., Rivera, J. P., Veroustraete, F., Clevers, J.G.P.W., and Moreno, J. (2015). Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—A review. ISPRS Journal of Photogrammetry and Remote Sensing 108, 273–290. doi.org/10.1016/j.isprsjprs.2015. 05.005. [147] Majasalmi, T., and Rautiainen, M. (2016). The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: A simulation study. Remote Sensing Letters 7, 427–436. doi.org/10.1080/2150704X.2016.1149251. [148] Michel, S., Gamet, P., and Lefevre-Fonollosa, M. J. (2011). HYPXIM-A hyperspectral satellite defined for science, security and defense users. IEEE Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal. [149] Houborg, R., Anderson, M., Gao, F., Schull, M., and Cammalleri, C. (2012). Monitoring water and carbon fluxes at fine spatial scales using HyspIRI-like measurements. Piscataway, NJ: IEEE International Geoscience and Remote Sensing Symposium. [150] Richter, K., Hank, T., Atzberger, C., Locherer, M., and Mauser, W. (2012). Regularization strategies for agricultural monitoring: The EnMAP vegetation analyzer (AVA). Piscataway, NJ: IEEE International Geoscience and Remote Sensing Symposium. [151] Pignatti, S., Acito, N., Amato, U., Casa, R., de Bonis, R., Diani, M., Laneve, G., Matteoli, S., Palombo, A., Pascucci, S., Romano, F., Santini, F., Simoniello, T., Ananasso, C., Zoffoli, S., Corsini, G., and Cuomo, V. (2012). Development of algorithms and products for supporting the Italian hyperspectral PRISMA mission: The SAP4PRISMA project. Piscataway, NJ: IEEE International Geoscience and Remote Sensing Symposium. [152] Kraft, S., del Bello, U., Bouvet, M., Drusch, M., and Moreno, J. (2012). FLEX: ESA’s earth explorer 8 candidate mission (pp. 7125–7128). Munich, Germany: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). [153] Ben-Dor, E., Kafri, A., and Varacalli, G. (2013). SHALOM: Spaceborne Hyperspectral Applicative Land and Ocean Mission: A joint project of ASI-ISA. Proceedings of the International Geoscience and Remote Sensing Symposium (IAGARSS’13), Melbourne, Australia.

34


[154] Drusch, M., del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment 120, 25–36. doi.org/10.1016/j. rse.2011.11.026. [155] Van der Meer, F., Van der Werff, H., and Van Ruitenbeek, F. (2014). Potential of ESA’s Sentinel-2 for geological applications. Remote Sensing of Environment 148, 124–133. doi. org/10.1016/j.rse.2014.03.022. [156] Guanter, L., Kaufmann, H., Segl, K., Forster, S., Rogass, C., Chabrillat, S., Kuster, T., Hollstein, A., Rossner, G., Chlebek, C., Straif, C., Fischer, S., Schrader, S., Storch, T., Heiden, U., Muller, A., Bachmann, M., Muhle, H., Muller, R., Habermeyer, M., Ohndorf, A., Hill, J., Buddenbaum, H., Hostert, P., Van der Linden, S., Leitao, P. J., Rabe, A., Doerffer, R., Krasemann, H., Xi, H., Mauser, W., Hank, T., Locherer, M., Rast, M., Staenz, K., and Sang, B. (2015). The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing 7(7), 8830–8857. doi.org/ 10.3390/rs70708830. [157] Rogass, C., Segl, K., Mielke, C., and Fuchs, Y. (2013). EnGeoMAP—A geological mapping tool applied to the EnMap mission. EARSeL eProceedings 12, 94–100. [158] Lee, C. M., Cable, M. L., Hook, S. J., Green, R. O., Ustin, S. L., Mandl, D. J., and Middleton, E. M. (2015). An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities. Remote Sensing of Environment 167, 6–19. doi. org/10.1016/j.rse.2015.06.012. [159] Leifer, I., Lehr, W. J., Simecek-Beatty, D., Bradley, E., Clark, R., Dennison, P., Hu, Y., Matheson, S., Jones, C. E., Holt, B., Reif, M., Roberts, D. A., Svejkovsky, J., Swayze, G., and Wozencraft, J. (2012). State of the art satellite and airborne marine oil spill remote sensing: Application to the BP deep-water horizon oil spill. Remote Sensing of Environment 124, 185–209. doi.org/10.1016/j.rse.2012.03.024. [160] Kokaly, R. F., Couvillion, B. R., Holloway, J. M., Roberts, D. A., Ustin, S. L., Peterson, S. H., Khanna, S., and Piazza, S. C. (2013). Spectroscopic remote sensing of the distribution and persistence of oil from the deep-water horizon spill in Barataria Bay marshes. Remote Sensing of Environment 129, 210–230. doi.org/10.1016/j. rse.2012.10.028. [161] Peltier, A., Finizola, A., Douillet, G. A., Brothelande, E., and Garaebiti, E. (2012). Structure of an active volcano associated with a resurgent block inferred from thermal mapping: The Yasur Yenkahe Volcanic Complex (Vanuatu). Journal of Volcanology and Geothermal Research 243-244, 59–68. doi.org/10.1016/j.jvolgeores.2012.06.022. [162] Da Luz, B. R., and Crowley, J. K. (2007). Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0–14.0 Mm). Remote Sensing of Environment 109(4), 393–405. doi.org/10.1016/j.rse.2007. 01.008. [163] Zhang, D., and Zhou, G. (2016). Estimation of soil moisture from optical and thermal remote sensing: A review. Remote Sensing 168(8), 1308. [164] Dematte, J.A.M., Sayao, V. M., Rizzo, R., and Fongaro, C. T. (2017). Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing. Geoderma 302, 39–51. doi.org/10.1016/j. geoderma.2017.04.019. [165] Shabou, M., Mougenot, B., Chabaane, Z. L., Walter, C., Boulet, G., Aissa, N.B., and Zribi, M. (2015). Soil clay content mapping using a time series of Landsat TM data in semiarid lands. Remote Sensing 7, 6059–6078. doi.org/10.3390/rs70506059.


35

[166] Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., Dolezal, J., Eriksson, O., Fortunel, C., Freitas, H., Golodets, C., Grigulis, K., Jouany, C., Kazakou, E., Kigel, J., Kleyer, M., Lehsten, V., Leps, J., Meier, T., Pakeman, R., Papadimitrou, M., Papanastasis, V. P., Quested, H., Quetier, F., Robson, M., Roumet, C., Rusch, G., Skarpe, C., Sternberg, M., Theau, J. P., Thebault, A., Vile, D., and Zarovali, M. P. (2007). Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: A standardized methodology and lessons from an application to 11 European sites. Annals of Botany 99, 967–985. doi.org/10.1093/aob/mcl215. [167] Li, W. Q., Liu, X. J., Khan, M.A., and Gul, B. (2008). Relationship between soil characteristics and halophytic vegetation in coastal region of North China. Pakistan Journal of Botany 40(3), 1081–1090. [168] Solon, J., Roo-Zielinska, E., and Degorski, M. (2012). Landscape scale of topography-soil vegetation relationship: Influence of land use and landform. Polish Journal of Ecology 60 (1), 3–17. [169] Latif, B. A., Lecerf, R., and Mercier G. (2008). Preprocessing of low resolution time series contaminated by clouds and shadows. IEEE Transactions on Geoscience and Remote Sensing 46(7), 2083–2096. doi.org/10.1109/TGRS.2008.916473. [170] ESA/ESRIN. (2012). Services and perspectives. Frascati, Italy: 1st Sentinel-2 Preparatory Symposium. [171] Verrelst, J., Rivera, G. P., Alonso, L., Guanter, L., and Moreno, J. (2012). Evaluating machine learning regression algorithms for operational retrieval of biophysical parameters: Opportunities for Sentinel. Frascati, Italy: 1st Sentinel-2 Preparatory Symposium. [172] Chen, F., Zhao, Z., Peng, L., and Yan, D. (2005). Clouds and cloud shadows removal from high-resolution remote sensing images. Proc. IGARSS, 6, 4256–4259. [173] Melgani, F. (2006). Contextual reconstruction of cloud-contaminated multitemporal multispectral images. IEEE Transactions on Geoscience and Remote Sensing 44(2), 442– 455. doi.org/10.1109/TGRS.2005.861929. [174] Keshava, N., and Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine 19, 44–57. doi.org/10.1109/79.974727. [175] Somers, B., Asner G. P., Tits L., and Coppin, P. (2011). Endmember variability in spectral mixture analysis: A review. Remote Sensing of Environment 115(7), 1603–1616. doi.org/ 10.1016/j.rse.2011.03.003. [176] Heylen, R, Parente, M., and Gader, P. (2014). A review of nonlinear hyperspectral unmixing methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(6), 1844–1868. doi.org/10.1109/JSTARS.2014.2320576. [177] Gholizadeh, A., Carmon, N., Klement, A., Ben-Dor, E., and Boruvka, L. (2017). Agricultural soil spectral response and properties assessment: Effects of measurements protocol and data mining technique. Remote Sensing 9(10), 1078. doi.org/ 10.3390/rs9101078. [178] Viscarra Rossel, R. A., and Bouma, J. (2016). Soil sensing: A new paradigm for agriculture. Agricultural Systems 148, 71–74. doi.org/10.1016/j.agsy.2016.07.001. [179] Ben-Dor, E., Chabrillat, S., Dematte, ˛ J.A.M., Taylor, G. R., Hill, J., Whiting, M. L., and Sommer, S. (2009). Using imaging spectroscopy to study soil properties. Remote Sensing of Environment 113, s38–s55. doi.org/10.1016/j.rse.2008.09.019. [180] Liao, L. B., Jarecke, P. J., Gleichauf, D. A., and Hedman, T. R. (2000). Performance characterization of the Hyperion imaging spectrometer instrument. Proceedings of Society of Photographic Instrumentation Engineers Conference: Earth Observing Systems, SPIE, San Diego, CA.

36


[181] Casa, R., Castaldi, F., Pascucci, S., Palombo, A., and Pignatti, S. (2013). A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing. Geoderma 197-198, 17–26. doi.org/10.1016/j. geoderma.2012.12.016. [182] Lausch, A., Erasmi, S., King, D. J., Magdon, P., and Heurich, M. (2017). Understanding forest health with remote sensing—Part I—A review of approaches and data models. Remote Sensing 9(2), 129. doi.org/10.3390/rs9020129.

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