Abstract
Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.
Highlights
Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes, in water and energy cycles, as it impacts the total volume of water following on the earth’s surface and beneath the surface [1]
We considered in situ soil moisture as a function of the variables mentioned above, as seen in Equations (3) and (4): mv = f(slp, aspct, rel, flacc, σVV, normalized difference vegetation index (NDVI)), (3)
There is some indifferent unexplained variability of the measured values that do not match the overall terrain and soil trends. These are due to short-range micro terrain variability not shown by the digital elevation model (DEM) or to the differences in the soil properties that we could not map at this scale
Summary
Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes, in water and energy cycles, as it impacts the total volume of water following on the earth’s surface and beneath the surface [1]. Several soil and landscape properties influence SM. Even though a large number of local and regional soil moisture networks are operating worldwide, they lack common standards (e.g., observed variables, sensor types, and sensor setup, etc.) and the generated data are often not freely available [3]. In situ soil moisture observations are collected from various networks distributed all over the globe, harmonized in terms of the sampling interval, units, and data format, and made freely available to the public through a web portal [6]
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