First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

Abstract

Abstract:The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ • ) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson's correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m 3 /m 3 for the ascending data, and are 0.012, 0.026, and 0.055 m 3 /m 3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

“…This result was expected due to the nature of the study site, which is characterized by low value of surface roughness [22,67]. This is in accordance with several previous studies which have found that, for agricultural surfaces, the exponential function provides the best match between predicted and SAR backscatters [82][83][84][85]. Generally speaking, in spite of the slight underestimation, one can state that the simulations of IEM by using the measured L are very satisfying with respect to what have been reported in previous works [20,67,80,81,86].…”

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

“…The calibration of empirical model parameters is typically achieved by fitting a sample of satellite observations and ground measurements (for example, by minimising a cost function as described by Bai et al (2017)). When analytical model inversion is not possible, it is common to use look up tables or machine learning approaches.…”

Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

“…e above-mentioned models are commonly applied to bare soil and cannot be applied directly in vegetation cover areas, due to the multiple scattering effects of vegetation canopies [14]. e water cloud model (WCM), a semiempirical forward model, generally assumes that the vegetation canopy is a uniform layer of a cloud of water droplets and has been widely used to separate out the contribution of vegetation backscatter [15][16][17][18]. To minimize the effects of vegetation, many researchers have attempted to utilize optical remote sensing to obtain additional vegetation information [19][20][21].…”