Soil Moisture Retrieval Using Polarimetric SAR Data and Experimental Observations in an Arid Environment

Abstract

Soil moisture is a critical component for Earth science studies, and Synthetic Aperture Radar (SAR) data have high potential for retrieving soil moisture using backscattering models. In this study, polarimetric SAR (PALSAR: Phased Array type L-band Synthetic Aperture Radar) data and polarimetric decompositions including span, entropy/H/alpha, and anisotropy, in combination with surface properties resulting from field and laboratory measurements, are used to categorize the natural surface condition and discriminate the backscatter parameter in the test site for applying the inversion soil moisture retrieval. The work aims to introduce the better of two examined models in the research for soil moisture retrieval over the bare land and sparse vegetation in arid regions. After soil moisture retrieval using the two different models, the results of comparison and validation by field measurement of soil moisture have shown that the Oh model has a more realiable accuracy for soil moisture mapping, although it was very difficult to find the best model due to different characteristics in land cover. It seems the inversion model, with the field observation and polarimetric SAR data, has a good potential for extracting surface natural conditions such as surface roughness and soil moisture; however, over- and under-estimation are observed due to land cover variability. The estimation of accurate roughness and moisture data for each type of land cover can increase the accuracy of the results.

Sub-Shrub Components Change the Soil Water Storage Response to Daily Precipitation and Air Temperature in the Loess Plateau

“…The Dubois model is a semi-empirical model used to retrieve surface soil moisture with vegetation cover [10], and Dave et al [11] modified the Dubois model to estimate soil moisture for a winter wheat crop, finding a better estimation in the early and mature stages of crop growth. Gharechelou et al [12] compared the Dubois model and Oh model with observational soil moisture data and found that the Oh model showed more accurate results in a dry environment, which is more suitable for a wider range of surface roughness and shorter wavelengths, while the Dubois model overestimated the values. However, these models were traditionally applied in remote sensing applications for soil moisture retrieval, lacking observational data analyses.…”

Estimation of Soil Electric Properties and Water Content Through PolSAR Target Decomposition

“…Soil moisture influences meteorological and climatic processes, although surface soil moisture only constitutes 0.0012% of all water available on the earth [5]. Soil moisture is important in modeling the ecosystem dynamics and the biogeochemical cycles but it has not had widespread application in modeling these processes because it is a variable that is very difficult to measure a spatially comprehensive basis [6]. The soil moisture gives also important information for agriculture irrigation.…”

“…The use of radar data to retrieve soil moisture is of considerable importance in many domains, including agriculture, hydrology, and meteorology [6]. Despite many advantages that can be derived from the knowledge of soil moisture distribution, the measurement of soil moisture has a few limitations.…”