Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign

Abstract

Heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of evapotranspiration (ET) (or latent heat flux, LE) estimated from remote sensing satellite data. However, most of the current research uses statistical methods in the mixed pixel to correct the ET or LE estimation error, and there is a lack of research from the perspective of the remote sensing model. The method of using frequency distributions or generalized probability density functions (PDFs), which is called the "statistical-dynamical" approach to describe the heterogeneity of land surface characteristics, is a good way to solve the problem. However, in attempting to produce an efficient PDF-based parameterization of remotely sensed ET or LE, first and foremost, it is necessary to systematically understand the variables that are most consistent with the heterogeneity (i.e., variability for a fixed target area or landscape, where the variation in the surface parameter value is primarily concerned with the PDF-based model) of surface turbulence flux. However, the use of PDF alone does not facilitate direct comparisons of the spatial variability of surface variables. To address this issue, the objective of this study is to find an indicator based on PDF to express variability of surface variables. We select the dimensionless or dimensional consistent coefficient of variation (CV), Gini coefficient and entropy to express variability. Based on the analysis of simulated data and field experimental data, we find that entropy is more stable and accurate than the CV and Gini coefficient for expressing the variability of surface variables. In addition, the results of the three methods show that the variability of the leaf area index (LAI) is greater than that of the land surface temperature (LST). Our results provide a suitable method for comparing the variability of different variables.

Evaluation of the Wheat Crop and Production Estimation Based on Remote Sensing

“…Specific indices, calculated based on satellite images, are important for the assessment of the spatial variability of the territory and the classification of areas (Li et al, 2018;Popescu et al, 2020;Singh et al, 2022), the description of the vegetation and agricultural crops (Weiss et al, 2020), the description of some phenomena in crops, such as plant lodging, the presence of weeds, diseases or plant pests (Chauhan et al, 2019;Guan et al, 2022).…”

Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements

“…SM controls evaporation, water balance, and profoundly affects the partitioning of land surface energy [6][7][8][9][10]. All of this relevance makes SM known as one of the "Essential Climate Variables" [11]. Accurate measurement of SM, hence, has greatly promoted its application in drought monitoring [12], agriculture applications [13,14], and climate predictions [15,16].…”

Recent Progress in Quantitative Land Remote Sensing in China

“…Although 40 articles cannot comprehensively characterize different aspects of quantitative land remote sensing in China, they clearly represent the current level of research in this area by Chinese scientists. These papers are related to various satellite data products, such as incident solar radiation [38][39][40], chlorophyll fluorescence [41], surface directional reflectance [42][43][44], aerosol optical depth [45], albedo [46,47], land surface temperature [48][49][50], upward longwave radiation [51], leaf area index [52][53][54][55], fractional vegetation cover [56], forest biomass [57], precipitation [58], evapotranspiration [59][60][61], freeze/thaw [62], snow cover [63], vegetation productivity [64][65][66][67][68], phenology [69,70], biodiversity indicators [71], drought monitoring [72], forest disturbance [55], air-quality monitoring [73], sensor design [74], and sampling strategy [75] for validation with in situ measurements. Most of these papers are based on optical-thermal remotely-sensed observations, but a few papers are also based on microwave [62,63] and Lidar…”