Spectral reflectance characteristics of soils in ...

Environ Monit Assess (2016) 188:616 DOI 10.1007/s10661-016-5631-6

Spectral reflectance characteristics of soils in northeastern Brazil as influenced by salinity levels Luiz Guilherme Medeiros Pessoa & Maria Betânia Galvão Dos Santos Freire & Bradford Paul Wilcox & Colleen Heather Machado Green & Rômulo José Tolêdo De Araújo & José Coelho De Araújo Filho

Received: 6 April 2016 / Accepted: 5 October 2016 # Springer International Publishing Switzerland 2016

Abstract In northeastern Brazil, large swaths of onceproductive soils have been severely degraded by soil salinization, but the true extent of the damage has not been assessed. Emerging remote sensing technology based on hyperspectral analysis offers one possibility for large-scale assessment, but it has been unclear to what extent the spectral properties of soils are related to salinity characteristics. The purpose of this study was to characterize the spectral properties of degraded (saline) and non-degraded agricultural soils in northeastern Brazil and determine the extent to which these properties correspond to soil salinity. We took soil samples from 78 locations within a 45,000-km 2 site in Pernambuco State. We used cluster analysis to group the soil samples on the basis of similarities in salinity and sodicity levels, and then obtained spectral data for each group. The physical properties analysis indicated a L. G. M. Pessoa (*) : M. B. G. D. S. Freire : R. J. T. De Araújo Department of Agronomy, Universidade Federal Rural de Pernambuco, Recife, PE, Brazil e-mail: [email protected] B. P. Wilcox Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, USA C. H. M. Green U. S. Department of the Interior, Bureau of Land Management, National Operations Center, Salt Lake City, UT, USA J. C. De Araújo Filho Brazilian Agricultural Research Corporation, EMBRAPA, Recife, PE, Brazil

predominance of the coarse sand fraction in almost all the soil groups, and total porosity was similar for all the groups. The chemical analysis revealed different levels of degradation among the groups, ranging from nondegraded to strongly degraded conditions, as defined by the degree of salinity and sodicity. The soil properties showing the highest correlation with spectral reflectance were the exchangeable sodium percentage followed by fine sand. Differences in the reflectance curves for the various soil groups were relatively small and were not significant. These results suggest that, where soil crusts are not present, significant challenges remain for using hyperspectral remote sensing to assess soil salinity in northeastern Brazil. Keywords Soil salinity . Soil sodicity . Salinity monitoring . Semiarid

Introduction The salinization of soils is a major contributor to environmental degradation and seriously impedes agricultural productivity. Metternicht and Zinck (2003) estimate that as much as 7 % of the Earth’s continental surface—or 1 billion ha—is affected by soil salinity. By adversely affecting biological processes in ecosystems, it diminishes the quality and health of soil and water resources. This problem is particularly acute in arid and semiarid regions, where agriculture is already a challenge (Fan et al. 2012; De la Paix et al. 2013).

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In their global assessment of soil degradation, Dregne and Chou (1992) estimate that about 11 % of irrigated dry lands in Brazil are degraded by soil salinization. It is the extensive semiarid zone in the northeastern portion of the country that is most susceptible to such degradation. Pereira et al. (1982) estimate that some 9 million hectares in this zone are affected by soil salinity. The net result is reduced food production— even as populations continue to grow. Salt-affected soils are caused by excess accumulation of salts most pronounced at the soil surface. Salts in soils are often derived from geological formations featuring shale, marl, limestone, sylvite, gypsum, and halite (Aldabaa et al. 2015). Variability of soil salinity is affected by parent material, soil type, and landscape position (Clay et al. 2001). Moreover, salts can be transported to the soil surface by capillary action from brackish water tables and accumulated due to evaporation. They can also accumulate as a result of anthropogenic activities such as fertilization or irrigation with salty water. For this reason, developing effective land management plans for semiarid agricultural regions depends critically on a better understanding of saline soils. In particular, regular monitoring of soil salinity is essential for efficient soil and water management and sustainability of agricultural lands (Bilgili et al. 2011), especially in arid and semiarid environments. To alleviate the cost of extensive sampling, remote sensing techniques have been widely applied to assess soil characteristics (Whiting et al. 2004) and are potentially useful for detecting and monitoring soil salinization (Metternicht and Zinck 2003; Schaepman et al. 2009; Morshed et al. 2016). These techniques can measure salinity levels in bare or salt-crusted soils or assess salinity through the biophysical properties of vegetation growing in the soils (Palacios-Orueta et al. 1999; Fernandez-Buces et al. 2006). Although remote sensing technology has already been widely used to assess and characterize salt-affected soils at large scales (Sharma and Bhargava 1988; Evans and Caccetta 2000; Farifteh et al. 2006), improved methodologies are emerging that potentially enable both salt type and concentration to be identified (Farifteh et al. 2007). One of these improved methodologies, which uses imaging spectrometry and is sometimes referred to as hyperspectral remote sensing, appears to hold particular promise for characterizing soil salinity at large

scales (Metternicht and Zinck 2003; Ben-Dor et al. 2008; Shoshany et al. 2013), but significant challenges remain (Ben-Dor 2002). The spectral reflectance of a given soil is determined by the soil’s inherent physical and chemical properties—including texture, organic matter, type of clay minerals, salt presence, and moisture content. Some studies have found good correlation between spectral reflectance and soil properties such as organic matter content, moisture content, mineral composition, iron oxide content, color, brightness, roughness, size and shape of the soil aggregate, and the salt and sodium content (Csillag et al. 1993; Metternicht and Zinck 1997; Brown et al. 2006; Setia et al. 2013; Ganjegunte et al. 2014). The objective of this study was to analyze the spectral reflectance characteristics of soils in northeastern Brazil across a salinity gradient, to determine whether and to what extent spectral reflectance is related to degree of salinity.

Materials and methods Study area Our study area consisted of four contiguous watersheds within the semiarid region of Pernambuco State in northeastern Brazil; these watersheds cover a total area of about 45,000 km2 (Fig. 1). This region is characterized by low rainfall (averaging about 600 mm annually) and high potential evapotranspiration (exceeding 2000 mm/year). Average annual temperatures range from 23 to 27 °C. The climate is characterized by distinct wet and dry seasons, with rain occurring mostly from January until June. Rainfall is exceedingly variable, in terms of both space and time, and multiple-year droughts are common (Sampaio 1995). The prevalent vegetation type is Caatinga shrubland, a semi-deciduous thorn forest that covers an extensive portion of northeastern Brazil (Pereira et al. 2003; Leal et al. 2005; de Albuquerque et al. 2012). Irrigated agriculture is typically carried out along the river valleys, where saline soils are concentrated. In the study region, due to the scarcity of good quality of irrigation water, the use of saline water in irrigated agriculture is common, which has affected production and also degraded soils over the years of cultivation, thus affecting agricultural sustainability in these regions (Ferreira et al. 2012). Soils in the upland zones are typically shallow and


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Fig. 1 Map of the study area in the state of Pernambuco, Brazil. Black dots indicate sampling locations

rocky and are derived from metamorphic rocks. Along the ephemeral rivers and streams, soils are alluvial and typically deep and sandy (Queiroz and Norton 1992; Sampaio 1995). According to Ribeiro et al. (2003), problems of high levels of salinity and sodicity in the semiarid part of Brazil are closely related to the formation of soil that has suborders and great groups are formed under conditions of deficient drainage in semiarid, as Fluvisols, vertisols, Cambisols, planosols, and Gleysols. Soil sampling For soil sampling, we randomly selected a total of 78 sites across the study area, encompassing both agricultural and non-agricultural areas (Fig. 1). By the random sampling method, it was possible for us to compare soils across the study region and not just in the irrigated portions. Each sampling site measured approximately 400 m2; the soil samples were collected at 10 to 15 points within the site, from the top layer of the soil (0to 5-cm depth). The 10–15 single samples from each site

were mixed to form a composite sample representing that site. The composite soil samples were air-dried and passed through a 2-mm sieve. Each composite sample was then split into three subsamples used for the physical and chemical analysis (to characterize saline soil properties) and for spectroscopic measurements. Physical analysis The physical analysis consisted of sieving and sedimentation to determine the sand, silt, clay, and water-dispersible clay content, as described by MacDowell (1997). The sand component was divided into coarse sand (particles measuring 0.2–2 mm in diameter) and fine sand (particles measuring 0.05– 0.2 mm in diameter). Bulk density was analyzed on an unmixed sample, and soil-particle density was analyzed by volumetric flask (MacDowell 1997). Unmixed samples were also used to measure saturated hydraulic conductivity, by means of a vertical column and a constant-load permeameter.

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The bulk density and soil particle density data were then used to calculate total porosity, and the total clay and water-dispersible clay data were used to calculate degree of flocculation and degree of dispersion. Chemical analysis For the chemical analysis, soil samples were analyzed by the electrical conductivity of the saturation extract (ECse at 25 °C) and by the soluble elements, as described by Richards (1954). For the cations Ca2+ and Mg2+, atomic absorption spectrophotometry–inductively coupled plasma techniques were used; for the cations Na and K, flame emission photometry was used. The soil pH was measured in a soil/water mixture of 1:2.5, and the exchangeable cations Ca2+, Mg2+, K+, and Na+ were extracted with ammonium acetate solution (1 mol L−1) and quantified according to Richards (1954). The organic carbon content was determined as described by Yeomans and Bremner (1988). The cation exchange capacity (CEC) was determined through the use of sodium acetate and ammonium acetate, 1 mol L−1. The results of these chemical analyses were used to calculate the sum of bases (S), the percentage of base saturation (BS), the sodium adsorption ratio (SAR), and the exchangeable sodium percentage (ESP) (Richards 1954). Spectroscopic measurements To collect spectral data from the soil samples, a hyperspectral sensor was used—the FieldSpec Spectroradiometer. The instrument covers the visible to short-wave infrared wavelength range (450 to 2500 nm) using three separate detectors: the VNIR (450–1050 nm), the SWIR1 (1000–1800 nm), and the SWIR2 spectrometer (1800–2500 nm). The spectrometer has a sampling interval of 1 nm for the region 450– 1000 nm and 2 nm for the region 1000–2500 nm, with a spectral resolution of 3 and 10 nm, respectively (Hatchell 1999). A Spectralon plate (barium sulfate) with 100 % of reflectance was used as a standard reference for the soil spectral readings, requiring a spectrum of the plate to calibrate the equipment every 25 min or 25 samples. A high-intensity contact probe was employed to minimize measurement errors associated with stray light. This accessory has a halogen bulb and a fiber optic cable connected to the spectroradiometer. The contact probe is


placed on the sample that is illuminated and the reflectance radiation is conducted by the fiber optic to the spectroradiometer. For the spectral data acquisition geometry, the sensor was placed vertically at 8 cm distant from the platform where the Spectralon plate and soil samples were located. The sensor detected the energy reflected in an area of approximately 2 cm2 with a field of view of 18° (Terra et al. 2015). A 600-W halogen lamp was used as source of illumination. Soil reflectance measurements were obtained after calibrating the device. The level of radiance reflected by the sample was compared with the maximum reflection from the white plate. The ratio of the spectral radiant flux reflected by a sample to the radiant flux reflected by the reference material generates the bidirectional spectral reflectance factor, from which the reflectance curve is fitted (Nicodemus et al. 1977), according to Eq. (1): BRF ðλÞ ¼ Lsoil ðλÞ=Lplate ðλÞ :ACF ðλÞ

ð1Þ

where BRF (λ) is the bidirectional reflectance factor, ACF (λ) is the absolute calibration factor of the Spectralon plate (ACF = 1), and λ is the wavelength. Statistical analysis Cluster analysis was used to identify similarities in the salinity and sodicity characteristics (EC, pH, ESP, and SAR) of the soils from the various sampling sites. For the cluster analysis, the data were standardized in standard scores (z scale) and dendrograms were constructed using the Euclidean distance, with the unweighted pair group method with arithmetic mean being used for grouping samples. Choice of the method was based on the assumption that the cophenetic matrix generated by that method had a higher Pearson correlation with the original distance compared to others. The higher the correlation, the better the representativeness of the analysis. A dendrogram was employed to differentiate the soil samples (defined as 20 % dissimilarity), on the basis of that at 20 %, there is a greater uniformity among the samples that constitute the groups, which were categorized into six homogenous groups (Fig. 2). The values found by cluster analysis for the salinity and sodicity variables of each of the six groups were expressed in terms of mean and standard deviations. Cluster analysis has been widely used, to good effect, by researchers addressing soil salinity issues in semiarid


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Fig. 2 Dendrogram obtained by cluster analysis of the saline and sodic variables (pH, EC, ESP, and SAR) found for soil samples from the four experimental watersheds

100

Euclidean Distances

80

60

40

20

6

5

4

3

2

1

0

Soil Samples

Brazil as well as other landscapes (Shrestha et al. 2005; Elnaggar and Noller 2010; Andrade et al. 2011). Six spectral readings were performed on each soil sample, to generate an average representative curve for that sample. Soil reflectance was assessed according to the following key spectral ranges: 550–770 nm (visible), 900–1030 nm, and 1270–1520 nm (near-infrared) and 1940–2150 nm, 2150–2310 nm, and 2330–2400 nm (middle infrared). These spectral ranges were chosen according to Csillag et al. (1993), which identified wavelength intervals according to their importance in discriminating salinity classes. For this study, nine spectral bands from the continuum of wavelengths were selected to correlate soil reflectance with soil properties: 488, 530, 670, 880, 940, 1400, 1900, 2200, and 2300 nm (Leone and Sommer 2000). Pearson correlation analysis was then applied to determine the relationship between those nine bands and the properties related to soil salinity (in addition to texture and organic carbon content, which also exert great influence on the spectral reflectance of soils).

Results Physical and chemical analyses Soil texture results indicated that the coarse sand fraction was predominating, whereas group 2

exhibited higher levels of silt and clay and group 4 had the highest average level of fine sand (Table 1). Group 3 had the highest level of coarse sand. All of the soil groups had high dispersion index values. The average particle densities were similar for all the groups, varying from 2.67 to 2.73 g cm−3, and bulk densities ranged from 1.35 to 1.44 g cm−3. Mean values of total porosity calculated from these data were similar for all six groups. Groups 5 and 6 had the highest saturated hydraulic conductivity (Table 1). The highest levels of carbonates and bicarbonates (and therefore the highest pH) were found in group 3. Groups 2 and 4 also had high alkalinity. The other three groups had average pH values more suitable for plant growth (Table 2). In general, all groups had high levels of exchangeable cations and soluble Ca2+, Mg2+, Na+, and K + ; high values of sum of bases and base saturation; and levels of organic carbon ranging from 0.7 to 1.6 dag kg−1 (Table 2). Groups 2 and 3 had the highest ESP values—over 15 %, which is the threshold for classifying soils as sodic (Richards 1954). Groups 1, 2, and 3 have high EC and SAR values, indicating the presence of elevated levels of soluble salts. These salts include soluble sodium, the predominant ion revealed through saturation extract. Salt crusts were observed in most of the soil samples for groups 1, 2, and 3 (Table 2). On the basis of the physical and chemical analyses, it is possible to observe that groups 1, 2, and 3 represent

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Table 1 Physical properties of soils in the six groups established through cluster analysis (based on salinity and sodicity variables—pH, electrical conductivity, sodium adsorption ratio, and exchangeable sodium percentage) Property

Soil group 1

2

3

4

5

6

Mean SD

Mean SD

Mean SD

Mean SD

Mean SD

Mean SD

Coarse sand (%)

31.00 18.00 19.00 25.00 56.00 04.00 31.00 18.00 29.00 16.00 36.00 19.00

Fine sand (%)

30.00 18.00 26.00 18.00 29.00 04.00 32.00 12.00 26.00 14.00 31.00 14.00

Clay (%)

15.00 14.00 17.00 22.00 05.00 01.00 15.00 08.00 17.00 13.00 15.00 10.00

Silt (%)

24.00 21.00 37.00 26.00 10.00 01.00 22.00 14.00 28.00 27.00 18.00 17.00

Particle Density (g cm−3)

02.70 00.08 02.68 00.19 02.72 00.08 02.73 00.10 02.69 00.09 02.67 00.12

Bulk Density (g cm−3)

01.40 00.04 01.40 00.19 01.35 00.21 01.44 00.11 01.43 00.15 01.42 00.18

Total Porosity (cm3 cm−3)

00.48 00.27 00.48 00.05 00.51 00.07 00.47 00.05 00.47 00.05 00.47 00.06

Dispersion index

00.48 00.27 00.46 00.29 00.45 00.28 00.53 00.14 00.49 00.17 00.49 00.18

Saturated hydraulic conductivity—K0 (cm h−1) 04.08 00.19 03.01 06.09 03.58 03.41 05.97 07.91 14.73 29.55 15.54 20.60 SD standard deviation

the soils most degraded by salinity, due to high EC, ESP, and SAR levels, and groups 5 and 6 represent non-

degraded soils. Group 4 was intermediate in terms of salinity levels (Tables 1 and 2).

Table 2 Chemical properties of soils in the six groups established through cluster analysis (based on salinity and sodicity variables—pH, electrical conductivity, sodium adsorption ratio, and exchangeable sodium percentage) Property

Soil group 1

2

3

4

5

6

Mean SD

Mean SD

Mean SD

Mean SD

Mean SD

Mean SD

Exchangeable cations pH

07.2

00.56

08.3

00.90

10.2

00.64

08.2

00.43 06.5

00.29 07.2

Ca2+ (cmolc kg−1)

14.0

12.80

06.1

04.70

08.9

03.22

05.6

03.62 03.2

02.98 06.4

06.72

Mg2+ (cmolc kg−1)

03.3

02.85

02.7

02.34

01.8

00.84

01.7

01.35 01.7

01.59 01.8

02.00

Na+ (cmolc kg−1)

01.5

01.79

04.1

03.12

02.0

00.93

00.3

00.52 00.2

00.17 00.4

00.39 00.68

+

−1

00.20

K (cmolc kg )

00.5

00.33

00.3

00.21

00.5

00.29

00.6

00.75 00.4

00.23 00.5

Cation exchange capacity—CEC (cmolc kg−1)

19.9

16.28

16.1

12.50

13.3

02.86

11.3

05.73 10.0

08.53 09.7

08.70

Sum of bases—S (cmolc kg−1)

19.3

16.40

13.2

09.99

13.2

02.83

08.4

04.81 05.5

04.31 08.9

08.63

Base saturation—BS (%)

90.4

18.65

83.5

18.14

99.4

00.13

73.9

24.09 60.4

21.10 87.4

18.87

Exchangeable sodium percentage—ESP (%)

05.8

03.23

31.8

10.44

16.4

10.57

03.2

02.68 02.8

01.69 06.3

05.71

Organic carbon—OC (dag kg−1)

01.4

07.90

00.8

04.10

00.7

01.4

06.50 01.6

001.5

011.7

01.1

06.10

Saturation extract Electrical conductivity—EC (dS m−1)

064.5

021.33 028.4

023.14 0066.1 0043.88 003.7

007.1

0.8

1.00

2.1

04.10

Ca2+ (mmolc L−1)

205.6

189.53 058.5

071.72 0014.3 0009.08 020.4

053.0

1.9

2.48

6.6

13.33

Mg2+ (mmolc L−1)

265.3

161.96 106.3

146.37 0005.8 0006.06 026.6

054.7

3.8

3.64

7.5

10.64

Na+ (mmolc L−1)

937.4

755.54 779.8

671.09 1856.1 1099.78 108.2

306.9

5.0

8.35

8.0

20.58

K+ (mmolc L−1)

012.4

016.45 001.9

001.47 0143.6 0090.20 001.6

002.9

0.5

0.35

0.8

01.26

Sodium adsorption ratio—SAR (mmolc L−1)0,5

090.6

079.90 097.4

102.92 0585.1 0122.30 010.3

027.2

3.7

6.57

2.6

04.56

SD standard deviation


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Spectroscopic measurements Spectral behavior for the six soil groups are presented in Fig. 3. Each curve was obtained by calculating the average of the reflectances for the soil samples in each group. All of the curves displayed an increase in reflectance with increasing wavelength. The average reflectance for all six groups was lowest in the visible region, which can be attributed to strong absorption caused by the presence of Fe 3+ in the wavelengths shorter than 540 nm (Demattê et al. 2005). Differences among the soil groups were quite small and not statistically significant (Table 3). For wavelengths larger than 1400, the non-degraded soils had higher reflectance values but these differences were not significant. The best correlation coefficients were for the ESP variable, followed by percent sand (Table 4). The ESP correlation coefficients were positive in virtually all wavelengths tested, whereas the fine sand correlation coefficients were negative. Interestingly, the other saline and sodic variables (pH, EC, and SAR) had lower correlation coefficients, indicating minimal influence on the spectral reflectance of soils. The organic carbon variable correlated negatively with the spectral bands at 488, 530, 670, 2200, and 2300 nm, indicating that the lower the levels of organic carbon, the greater the spectral reflectance.

Discussion We found cluster analysis to be a valuable tool for grouping soils on the basis of aggregate soil characteristics. The identified groups fall into three logical 70

Reflectance (%)

60 50 40 30 20

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

10 0 350

700

1050

1400

1750

2100

2450

Wavelength (nm)

Fig. 3 Average spectral reflectance curves of the six soil groups established through cluster analysis

categories: (i) the soils in groups 1, 2, and 3 are the most degraded; their high salt content makes them unsuitable for cultivation. (ii) The soils in group 4 are degraded as well, and vulnerable to further degradation, but are still suitable for cultivation (in these locations, best management practices need to be adopted to reduce further degradation). (iii) The soils in groups 5 and 6 showed no evidence of degradation by salinization or sodification. As noted by Ben-Dor (2002), identifying relationships between soil characteristics and soil spectra is not straightforward. We did find that one of the salinity attributes, ESP, was strongly correlated with spectral reflectance. High ESP increases the soil dispersion decreasing the amount of macropores and thus increases the spectral reflectance. These results differ somewhat from those of earlier studies that found EC to be the most closely correlated (Rao et al. 1995; Metternicht and Zinck 2003). The low correlation of reflectance with EC in our study may be due to the hydroscopic properties of salts in the degraded soils (Goncalves and Rodrigues 2006). Further, we found that there were few differences among the spectral reflectance curves of the different soil groups (Fig. 3). Ben-Dor (2002) claim that spectral signature of saline soils can be a result of the salt itself, or indirectly, from other chromophores related to the presence of the salt (e.g., organic matter, particle size distribution). Thus, other factors, such as soil mineralogy, texture, and organic matter likely have much more influence. The soils in the semiarid part of the state of Pernambuco present the coarse sand, fine sand, and silt fractions constituted predominantly by quartz, feldspars, and micas. Clay fraction is mainly composed of kaolinite and smectite (Olveira et al. 2004). Demattê et al. (2007) have studied the influence of soil mineralogy on spectral reflectance, and they conclude that clay minerals influenced the spectral region between 400 and 2500 nm and in some specific bands, where the reflectance increased as the iron content decreased. According to their study, the main absorption bands were centered at 450 and 850 nm and were attributed to interactions between electromagnetic radiations and the content of iron oxide in the soil, where water and hydroxyl absorption bands at 1450, 1950, and 2200 nm were also enhanced, and allowed a correlation with the presence of either 2:1 or kaolinitic mineralogy. Also, they stated that the contribution of 2:1 minerals, particularly the vermiculite and the micas, was small. On the

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Table 3 Mean values of the reflectance of soil groups formed by cluster analysis and the F-value of the one-way ANOVA analysis of selected spectral band parameters Soil group

Selected spectral band (nm) 550–770

900–1030

1270–1520

1940–2150

2150–2310

2330–2400

1

0.24

0.39

0.49

0.47

0.48

0.44

2

0.30

0.44

0.52

0.52

0.51

0.48

3

0.26

0.38

0.47

0.49

0.50

0.48

4

0.22

0.35

0.47

0.49

0.47

0.44

5

0.26

0.41

0.51

0.52

0.50

0.46

6

0.26

0.41

0.51

0.52

0.50

0.46

F-value

1.76ns

1.38ns

0.71ns

0.81ns

0.29ns

0.38ns

ns not significant at 5 % level

strongly saline soils (dry surface crust), different levels of saline and sodic soils are not easily identified, because the optical proprieties of the soil surfaces (color, brightness, roughness, etc.) could mask the salinity and sodicity effects, and consequently, their spatial distribution will probably be underestimated. They suggest that some spectral confusion occurs between salinity and sodicity levels, as well unaffected soils with bright and clear color. Another important factor to be considered in modern agriculture influencing the spectral reflectance of salt-affected soils is that farmers are adding gypsum to sodic soils for soil reclamation (Singh 1994). The artificial increase of the gypsum and other chemical content in such soils may alter the soil reflectance spectra significantly and, hence, requires attention. In summary, although

other hand, such minerals contributed to a higher absorption at 1900 nm, similarly reported by Grove et al. (1992). Because no direct significant spectral features are found in the VNIR-SWIR region for identifying saltaffected soils, indirect techniques are thought to be more appropriate for classifying salt-affected areas (Verma et al. 1994). Csillag et al. (1993) analyzed highresolution spectra taken from about 90 soils in the USA and Hungary against chemical parameters, including clay and organic matter content, pH, and salt. They state that because salinity is such a complex phenomenon, it cannot be attributed to a single soil property. It supports the results found in this study having low correlation between EC and the spectral reflectance of soils. Bannari et al. (2008) claim that although remote sensing offers a good potential for mapping

Table 4 Pearson correlation coefficients between soil properties and spectral reflectance Soil property

Wavelength (nm) 488

530

670

880

940

1400

1900

2200

2300

pH

0.29

0.31

0.17

−0.14

−0.24

−0.56

−0.11

0.51

0.23

Electrical conductivity—EC

0.23

0.20

0.11

0.01

−0.02

−0.30

−0.45

0.38

0.20

Sodium adsorption ratio—SAR

0.30

0.32

0.22

−0.05

−0.14

−0.45

−0.07

0.66*

0.32

Exchangeable sodium percentage—ESP

0.92**

0.92**

0.85*

0.65

0.34

0.42

0.74*

0.70* −0.66*

0.71*

Organic carbon—OC

−0.66*

−0.66*

−0.57

−0.36

−0.29

0.00

−0.36

−0.73*

Coarse sand (0.2–2 mm)

−0.16

−0.14

−0.20

−0.40

−0.46

−0.61

−0.15

0.30

0.05

Fine sand (0.05–0.2 mm)

−0.70

−0.71

−0.78*

−0.76*

−0.74*

−0.64

−0.60

−0.66

−0.66

Clay

−0.05

−0.07

0.03

0.29

0.37

0.62

0.17

−0.46

−0.15

Silt

0.40

0.38

0.43

0.43

0.61

0.67

0.27

−0.02

0.16


salt is not a strong and direct chromophore, its interaction with other soil components (water, structure, iron and organic matter) makes its assessment possible but complicated. In cases where the spectral reflectance of saltaffected soils is not able to identify those areas, vegetation is an indirect factor that facilitates detection of salt soils from reflectance measurements (Wiegand et al. 1994). Gausman (1970), for example, pointed out that cotton leaves grown in saline soils had a higher chlorophyll content than leaves grown in low-salt soil. Hardisky et al. (1983) used the spectral reflectance of a Spartina alterniflora canopy to show a negative correlation between soil salinity and spectral vegetation indices. With respect to soil texture, we found a negative correlation between fine sand and reflectance. These results differ from those of Stoner (1979), who found that soils with a high proportion of fine and very fine sand have higher reflectance, owing to the reduced particle size. Shi and Huang (2007), however, noted that fine sand will not increase reflectance if salt content is high, similar to the results found in this study. It is widely known that reflectance increases as particle sizes decrease or with increasing presence of salt crust (Howari et al. 2002). Since the sand fraction is related to the soil fraction comprising particles from 50 to 2.000 μm, by increasing the fine and coarse sand content, there is a reduction in the soil spectral reflectance mainly due to the increase in surface roughness (Cierniewski and Kuśnierek 2010). Soil salinity is a serious and growing problem in northeastern Brazil, but the true scope of the problem has yet to be accurately determined. Remote sensing technology offers the possibility of accurately mapping the extent of soils degraded by salinity, but in order to do so, we must have a good understanding of how salinity affects the spectral characteristics of soils. We found that spectral characteristics were well correlated with ESP. Other soil properties influencing soil spectral characteristics were organic carbon and the amount of fine sands. However, differences in these properties did not translate into large differences in the spectral reflectance bands of the six soil groups. Although limited in scope, this research highlights both the opportunities and the challenges in using spectral analysis to identify and characterize saline soils.

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Conclusion This research helps to understand the relationship between the spectral behavior and salinity and sodicity levels, facilitating the identification and monitoring of areas degraded by salts and sodium. The vast majority of studies investigating soil salinity indicate that higher salinity correlates with increased spectral reflectance. The soils we studied have higher levels of salts than those of the soils studied and reported on by others, and it was possible to observe that the groups with the highest salinity levels (groups 1, 2 and 3) presented higher spectral reflectance than did the groups with the lowest salinity levels, agreeing with the previous studies. However, this property presented a low correlation coefficient with the spectral reflectance indicating in this study that the reflectance level is more related to other soil properties. The ESP values found for the soils in this study correlated strongly with spectral reflectance. Although in most of the literature ESP is presented as a parameter that causes a reduction in reflectance, our findings indicate that the correlation is governed by the organic matter content of the soil; if OC is low, ESP can contribute to increased spectral reflectance.

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