Understanding the Heterogeneity of Soil Moisture and

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 11, NOVEMBER 2017

Understanding the Heterogeneity of Soil Moisture and Evapotranspiration Using Multiscale Observations From Satellites, Airborne Sensors, and a Ground-Based Observation Matrix R. Jin , Member, IEEE, X. Li, Senior Member, IEEE, and S. M. Liu, Member, IEEE

Abstract— This letter summarizes a special stream of the IEEE Geoscience and Remote Sensing Letters devoted to understanding the heterogeneity in soil moisture, evapotranspiration, and other related ecohydrological variables based on multiscale observations from satellite-based and airborne remote sensors, a flux observation matrix, and an ecohydrological wireless sensor network in the Heihe Watershed Allied Telemetry Experimental Research project. Scaling and uncertainty are the key issues in the remote-sensing research community, especially regarding the heterogeneous land surface. However, a lack of understanding and an inadequate theoretical basis impede the development and innovation of forward radiative transfer models, as well as the quantitative retrieval and validation of remote-sensing products. We summarize the prior considerations regarding surface heterogeneity research and report the main outcomes and contributions of this special stream. The highlights of this stream are related to spatial sampling, upscaling, uncertainty analysis, the validation of remote-sensing products, and accounting for heterogeneity in remote-sensing models. Index Terms— Ground truth, heterogeneity, remote-sensing products, scaling, uncertainty.

I. I NTRODUCTION OIL moisture, evapotranspiration, land surface temperature (LST), albedo, and other land surface parameters are of crucial importance for ecohydrological science and water resource management in arid regions. The remote sensing of these parameters has advanced rapidly in past decades. However, considerable challenges still exist and are associated with a fundamental problem: the land surface is heterogeneous. Both forward remote-sensing models and retrieval algorithms of these land surface parameters do

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Manuscript received June 7, 2017; accepted September 15, 2017. Date of publication October 3, 2017; date of current version October 25, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 41531174, in part by the Key Research Program of Frontier Sciences, Chinese Academy of Sciences, under Grant QYZDY-SSWDQC011, and in part by the National Natural Science Foundation of China under Grant 41471357. (Corresponding author: R. Jin.) R. Jin and X. Li are with the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China, and also with the CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China (e-mail: [email protected]; [email protected]). S. M. Liu is with the State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China (e-mail: [email protected]). Color versions of one or more of the figures in this letter are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LGRS.2017.2754961

not sufficiently consider the heterogeneity effects within a remote-sensing pixel. Therefore, some current and relevant research initiatives focused on heterogeneity and scaling in remote sensing have been proposed as follows: 1) developing remote-sensing products over heterogeneous land surfaces; 2) quantifying the uncertainty associated with heterogeneity and scaling; 3) validating remote-sensing products of heterogeneous land surfaces by taking advantage of newly developed in situ observation techniques, such as wireless sensor networks (WSNs) and footprint-scale measurement instruments. Scale is closely associated with the land surface heterogeneity, is one of the major challenges of quantitative remote sensing, and has been the focus of extensive discussions in the remote-sensing community. Although considerable attention has been given to this issue, scale research has progressed slowly, and various bottlenecks have been encountered. Notably, such studies lack in systematic and widely recognized theory regarding scaling and heterogeneity methodology. In addition, multiscale observations are not available to support theory development and method validation [1]. To promote a better understanding of the above issues, the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, an intensive experiment focused on the application of remote sensing in ecology and hydrology research at the basin scale, was launched in an inland river basin in the arid region of northwestern China [2]. HiWATER includes the integrated use of multisource remote sensors, a flux observation matrix, and an ecohydrological WSN to capture the multiscale heterogeneity characteristics of surface hydrothermal variables and the associated influential factors, and to address the complicated problems related to heterogeneity, scaling, and uncertainty [2]–[4]. The IEEE G EOSCIENCE AND R EMOTE S ENSING L ETTERS called for papers for a special stream devoted to understanding the heterogeneity of soil moisture, evapotranspiration, and other related ecohydrological variables. Twenty-eight letters were accepted for publication among 40 submissions. These letters used HiWATER data sets and focused on heterogeneity, upscaling, and uncertainty. The letters were organized into four themes: 1) soil moisture; 2) evapotranspiration; 3) other land

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JIN et al.: UNDERSTANDING HETEROGENEITY OF SOIL MOISTURE AND EVAPOTRANSPIRATION

surface parameters; and 4) the HiWATER information system. This letter summarizes the related considerations of heterogeneity and scaling in remote sensing and reviews the outcomes and contributions of this special stream. The remainder of this letter is organized as follows. Sections II presents an overview of spatial heterogeneity, the methods required to effectively observe surface heterogeneity, and the methods for obtaining real ground data and validating remote-sensing products. Section III details the progress in spatial sampling, observation representativeness assessment, heterogeneity analysis, upscaling, heterogeneity and scaling in forward models, and the retrieval of remote sensing. Finally, Section IV provides conclusions and proposes some potential research fields relevant to heterogeneity, scale, and uncertainty. II. H ETEROGENEITY AND S CALE IN R EMOTE S ENSING There is no unified definition of spatial heterogeneity, because it generally depends on the research object and its scale characteristics. Therefore, heterogeneity in remotesensing research should be clarified. Then, the spatial optimal sampling should be implemented to capture spatial heterogeneity based on multiscale observations. In addition, footprintscale observations and WSNs are recommended to overcome the scale gap between remote-sensing pixels and traditional single-point observations. Upscaling is used to aggregate multisource and multipoint observations and obtain representative ground-level data at the remote-sensing pixel scale. Then, reasonable evaluations of remote-sensing retrieval can be performed and used to improve remote-sensing algorithms. A. Spatial Heterogeneity Spatial heterogeneity is an intrinsic property of the land surface and is defined in Goodchild’s Second Law of Geography [5], which means the complexity of discontinuous category and the spatial variability in continuous land surface properties [6]. Heterogeneity can include self-organizational, structural, or stochastic characteristics. The spatial heterogeneity of a continuous variable can be quantified based on the degree of departure from randomness, e.g., variability, trend, and anisotropy, while that of a categorical variable can be quantified by using landscape and ecological features as references, e.g., diversity, isolation, and connectivity. In remote sensing, spatial heterogeneity is related not only to the spatial variations of surface variables and the size of the spatial domain/extent but also to the observation scale, namely, the spatial resolution of remote-sensing pixels. Thus, heterogeneity is a relative definition, which may become homogenous over a large spatial domain or at a coarse spatial resolution. Therefore, heterogeneity is positively associated with variability and negatively related to the spatial scale. B. Optimal Spatial Sampling Spatial heterogeneity is ubiquitous; however, the number of attainable ground-based observations is finite and restricted by costs and labor. Therefore, spatial sampling is an effective way of representing a population based on limited samples.

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The objectives of optimal spatial sampling are to capture the spatial heterogeneity of the land surface using a reasonable quantity of samples/observations with an appropriate spatial distribution and to provide an unbiased estimation of accurate ground-level data at the remote-sensing pixel scale. Two types of sampling methods exist: design-based and model-based methods. The model-based sampling method is widely used and based on the autocorrelation of variables in the geostatistical theory [7]. The important factors that affect spatial sampling include the magnitude, directionality, and stratification of landscape heterogeneity; spatial–temporal variations and trends in surface variables; unbiased optimal estimation; and multiscale sampling efficiency. Spatial sampling should not only benefit the estimation of variation functions but also minimize the estimation variance within a satisfactory accuracy. A precondition of such sampling methods could be the number of samples or the estimation variance. The prior information required for spatial sampling should be collected from static geographic maps, e.g., land-use maps, digital elevation models, or dynamic observations from remote sensors. In addition, stochastic simulations are often used to validate and evaluate sampling methods. C. Multiscale Observations Vereecken et al. [1] noted that few applicative data sets are available to understand spatial heterogeneity and validate the existing methods of scale transformation. To overcome this limitation, HiWATER designed and implemented a multisource and multiscale synergistic observation experiment to address complex problems such as heterogeneity, scaling, and uncertainty. The core strategy of this project is to jointly use airborne remote sensors (visible, infrared red, thermal red, microwave, and Lidar sensors) [1], flux observation matrix [17 automated meteorologic stations, 17 eddy covariance (EC) systems, and four large-aperture scintillometers (LAS)] [3], and ecohydrological WSNs (WATERNET, SoilNET, and BNUNET; 180 nodes in total) [4] to capture the multiscale heterogeneity (see Fig. 1) of surface water and heat fluxes/ variables, such as evapotranspiration, soil moisture, leaf area index (LAI), and land surface radiative temperature. These observations provide sufficient data to support the letters in this special stream. Each observation has a unique source area, which is the area having majority of contribution to the observation and is generally in the integral form at a certain extent. For convenience, the point scale can be defined as the scale at which the source area is sufficiently small that heterogeneity can be ignored, e.g., measurements collected using a portable soil moisture sensor. The scale of a remote-sensing pixel is a specific source area with shape that is typically square. For ground-based observations, the footprint measurements are recommended to match the scale of remote-sensing pixels, such as EC, LAS, and COsmic-ray Soil Moisture Observing System (COSMOS) methods. D. Spatial Upscaling Spatial upscaling is mathematically defined as the spatial averaging of discrete observations using numerical integration.

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Fig. 1.


Synergistic multiscale observation used in the HiWATER project.

Although various footprint-scale observations have become available with technological advancements, scale mismatch still exists between ground-based observations and remotesensing pixels. Therefore, it is often necessary to upscale multipoint or footprint observations to the remote-sensing pixel scale. According to the spatial autocorrelation and spatial stratification of surface variables, we can choose the most appropriate method, such as a simple random model, stratification sampling model, block kriging model, or mean of surface with nonhomogeneity model [8]. For a heterogeneous land surface, remote-sensing products and other auxiliary geographical information related to the study object or variable should be utilized to capture its spatial variations, structure, and pattern. Then, data fusion methods, such as regression kriging methods for linear problems [9] or the Bayesian method for data integration in nonlinear problems [10], can be used for spatial upscaling. In addition, wavelet analysis is widely used in signal processing and should be considered in upscaling procedures. In addition to spatial information, the dynamic variations in variables should be considered based on time-series analysis and spatiotemporal geostatistics [11].

a surface variable in remote sensing is defined as unbiased estimation; therefore, the mathematical expectation of the representative error at a specific scale is zero, and the associated uncertainty (secondary moment, e.g., variance) can be controlled within a user-defined range [12]. Previous validation methods focused on the quality of remote-sensing products, but neglected the representativeness of ground-based observations. We suggest that the representativeness of observations should be evaluated to quantitatively assess or qualitatively rank before validation. In many cases, some core stations are preferential for validation, especially when sparse or single-point observations are available. The triple collocation (TC) method was recently proposed to estimate uncertainty, including the relative bias and random error variance. The method requires three data sets with independent error structures, such as data from ground-based observations, remote-sensing products, land surface models, and data assimilation systems [13]. In addition to direct validation using ground observations, airborne remote-sensing products often act as a bridge for the validation. Such products can be corrected by the ground observations and then be aggregated to validate satellite remote-sensing products with large-scale gaps. In cases when ground observations are not available, remote-sensing products from different sensors and algorithms can be cross-validated. III. O UTCOMES F ROM THE S PECIAL S TREAMS According to the research themes, the letters in this special issue are related to optimal sampling methods, spatial heterogeneity analysis, scale transformation methods, uncertainty analysis, validation strategy of remote-sensing products, and considerations of heterogeneity in remote-sensing models. A. Spatial Sampling For a heterogeneous surface without a priori information, a hybrid method of spatial sampling optimization that considered both the parameter estimation accuracy of variation functions and the minimization of statistical variance was proposed to guide the distribution of the WSN in the middle reach of the Heihe River Basin [14]. Supported by the HiWATER observation data sets, various spatial sampling methods were developed to satisfy different requirements and applications. Such methods include universal cokriging for simultaneous multivariable sampling optimization [15] and stratified block kriging for decomposing a study area into smaller areas to address the surface heterogeneity [16].

E. Validation of Remote-Sensing Products

B. Representative Error of Observations

The validation can be performed to evaluate the quality and uncertainty of remote-sensing products and improve the retrieval and estimation methods of remote-sensing products. Although absolute true ground data sets are impossible to attain, such data sets can be optimally estimated via spatial upscaling and provide a reference for validating remotesensing products. The reference information associated with

The representative error of observations was analyzed in terms of the sources, scale dependence, and relationship with surface heterogeneity. A comparison of land surface radiative temperature observations from hemisphere viewing field and 22° viewing angle sensors showed that the representativeness of the hemisphere sensor was better than that of the 22° viewing angle sensor.

JIN et al.: UNDERSTANDING HETEROGENEITY OF SOIL MOISTURE AND EVAPOTRANSPIRATION

In addition, the spatial representativeness was better at night than in the daytime due to the uniform thermodynamic characteristics at night, and scale mismatch was obvious with surface heterogeneity. If 1 K was used as a threshold, the probability of successfully validating MODIS LST products using singlepoint observations was only 15.3% [17]. The scenario is then the same when using single-point observations to validate soil moisture. If 0.04 cm3 /cm3 was established as the threshold, only 21% of observations satisfied the validation requirements. Therefore, the WSN and COSMOS observations have recently been given considerable attentions [18], because they have many observation points and large observation footprint. The results of Zhu et al. [19] and Han et al. [20] demonstrated that COSMOS footprint observations are more suitable for the validation of remotesensing products. The uncertainty in surface flux observations, including both systematic and random error, is important for the closure of the surface energy balance and the validation of remote-sensing surface flux products. Wang et al. [21] quantitatively evaluated the observation error for EC based on a comparison experiment in the homogeneous Gobi surface and a flux observation matrix experiment [22] in a heterogeneous oasis area. Bai et al. [23] used numerical simulation to demonstrate that the remote-sensing validation pixels selected using the footprint model agreed well with flux observations in a given spatial domain, particularly over a heterogeneous surface based on high-resolution remote-sensing products. Because satellite remote-sensing pixels with moderate-to-coarse resolution cannot be fully covered by EC or LAS flux source areas, effective upscaling or measurement methods must be urgently developed at the remote-sensing scale. Such measurement methods include flux observation matrix or airborne EC system. C. Heterogeneity Analysis Geostatistical theory has been widely applied to analyze surface heterogeneity based on key parameters, such as the nugget, correlation length, range, and structural variance ratio. Yu and Ma [17] estimated the influence of spatial heterogeneity and randomness on the validation of MODIS LST products based on a semivariance function and noted that the scale mismatch due to surface heterogeneity would result in a high validation uncertainty. Although MODIS normalized difference vegetation index (NDVI) products have a 250-m spatial resolution, heterogeneity is a common phenomenon in the data [24]. D. Upscaling Methods Kriging is an interpolation method that considers the spatial variation in surface variables. In this special stream, researchers developed various upscaling methods to improve and extend the traditional kriging family. To upscale multipoint observations, Kang et al. [9] developed a regression kriging method to aggregate soil moisture observations from a WSN at the remote-sensing pixel scale. The auxiliary remote-sensing information, including ASTER TVDI, MODIS TVDI, and PLMR Tb data, can improve the

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interpolation accuracy and reflect detailed spatial patterns, especially when spatial continuity is destroyed by irrigation. The area-to-area regression kriging (ATARK) method was proposed to upscale the sensible heat flux at footprint scale observed using 17 EC sensors to an LAS footprint by using vegetation coverage, NDVI, and LST as ancillary spatial information. The multitemporal residual errors were used to construct a semivariation function because of the limited number of EC observations available. The ATARK method improved the underestimation issues associated with the area weighting method and the footprint weighting method [25]. Supported by multiscale observations from HiWATER, other upscaling methods were also proposed, including kriging with unequal observation errors [26], [27], weighted ATARK [28], spatiotemporal regression block kriging [11], and the integrated Priestley–Taylor equation method [3]. In addition, the Bayesian theory was used for upscaling, including the Bayesian maximum entropy [10] and Bayesian linear estimation [29]. E. Scale in Forward Models and Remote-Sensing Retrieval Heterogeneity is an intrinsic property of the land surface. Whether developing a forward radiative model or improving remote-sensing retrieval, surface heterogeneity should be considered. In addition, strategies are necessary to control and minimize the influence of observation error and model parameter uncertainty on remote-sensing retrieval. Cao et al. [30] developed a geometric optical model to simulate the distribution of the directional brightness temperature over mixed scenes of continuous cropland and roads. The simulation accuracy of mixed pixels was less than 1.1 K. To estimate soil moisture in heterogeneous pixels, Zhang et al. [31] used dual-angle PLMR brightness temperatures to decompose mixed pixels and separately estimated the soil moisture of the bare soil and under vegetation cover. Yin et al. [32] concluded that the scaling bias of the LAI remote-sensing product was mainly caused by spatial heterogeneity, specifically, textural and contextual effects. In addition, a correction method was proposed to remove the influence of spatial heterogeneity when using moderateto-low spatial-resolution remote-sensing images to estimate LAI. The method reduced the scaling bias from 26% to 2%. Wang et al. [33] analyzed the mechanisms of scaling effects based on the Taylor series expansion and found that the mean of the fraction of absorbed photosynthetically active radiation (FAPAR) at a fine resolution was always less than or equal to that at a coarse resolution. The nonuniformity of the vegetation distribution and the nonlinear retrieval algorithm were the key factors that resulted in scaling effects. Based on these results, a scaling transformation algorithm was developed to produce multiscale FAPAR. For soil moisture retrieval from the passive microwave brightness temperature, Li et al. [34] analyzed the influences of observation error, parameter uncertainty, and retrieval strategy on the retrieval uncertainty. The ways to control and reduce these uncertainties consisted of increasing the number of remote-sensing observations to avoid ill-pose inversion and

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realize multiparameter inversion, and estimating the initial guess value of model parameters from related a priori remotesensing information. A “bottom-to-top” validation of MODIS NDVI was recommended, and the TM NDVI, as a bridge data set, should be validated and corrected using ground-based observations and then averaged to evaluate the MODIS NDVI [24]. IV. C ONCLUSION This letter focused on surface heterogeneity, scaling, and uncertainty in remote-sensing research. New methods and concepts have been developed; however, large research gaps still exist. For example, we have limited knowledge regarding the interactions among mixed-pixel components, and no method exists to describe the error delivery process. Current remote-sensing validation work provides a compromise between theory and reality. R EFERENCES [1] H. Vereecken, R. Kasteel, J. Vanderborght, and T. Harter, “Upscaling hydraulic properties and soil water flow processes in heterogeneous soils,” Vadose Zone J., vol. 6, no. 1, pp. 1–28, Jan. 2007. [2] X. Li et al., “Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific objectives and experimental design,” Bull. Amer. Meteorol. Soc., vol. 94, no. 8, pp. 1145–1160, Aug. 2013. [3] S. Liu et al., “Upscaling evapotranspiration measurements from multisite to the satellite pixel scale over heterogeneous land surfaces,” Agricult. Forest Meteorol., vols. 230–231, pp. 97–113, Dec. 2016. [4] R. Jin et al., “A nested ecohydrological wireless sensor network for capturing the surface heterogeneity in the midstream areas of the Heihe River Basin, China,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 11, pp. 2015–2019, Nov. 2014. [5] M. F. Goodchild, “The fundamental laws of GIScience,” presented at the Summer Assembly Univ. Consortium Geograph. Inf. Sci., Pacific Grove, CA, USA, Jun. 2003. [6] H. Li and J. F. Reynolds, “On definition and quantification of heterogeneity,” Oikos, vol. 73, no. 2, pp. 280–284, Jun. 1995. [7] J.-F. Wang, A. Stein, B.-B. Gao, and Y. Ge, “A review of spatial sampling,” Spatial Statist., vol. 2, no. 1, pp. 1–14, Dec. 2012. [8] J.-F. Wang, G. Christakos, and M.-G. Hu, “Modeling spatial means of surfaces with stratified nonhomogeneity,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 12, pp. 4167–4174, Dec. 2009. [9] J. Kang, R. Jin, and X. Li, “Regression kriging-based upscaling of soil moisture measurements from a wireless sensor network and multiresource remote sensing information over heterogeneous cropland,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 92–96, Jan. 2015. [10] S. Gao, Z. Zhu, S. Liu, R. Jin, G. Yang, and L. Tan, “Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing,” Int. J. Appl. Earth Observat. Geoinf., vol. 32, no. 10, pp. 54–66, Oct. 2014. [11] J. Wang, Y. Ge, G. B. M. Heuvelink, and C. Zhou, “Upscaling in situ soil moisture observations to pixel averages with spatio-temporal geostatistics,” Remote Sens., vol. 7, no. 9, pp. 11372–11388, Sep. 2015. [12] X. Li, “Characterization, controlling, and reduction of uncertainties in the modeling and observation of land-surface systems,” Sci. China Earth Sci., vol. 57, no. 1, pp. 80–87, Jan. 2014. [13] C.-H. Su, D. Ryu, W. T. Crow, and A. W. Western, “Beyond triple collocation: Applications to soil moisture monitoring,” J. Geophys. Res. Atmos., vol. 119, no. 11, pp. 6419–6439, Jun. 2014. [14] J. Kang, X. Li, R. Jin, Y. Ge, J. Wang, and J. Wang, “Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China,” Sensor, vol. 14, no. 10, pp. 19095–19114, Oct. 2014.

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