Abstract
In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements. Therefore, with the DTDC backscatter observations, four equations could be established using forward models, while three unknowns (the two consecutive soil moisture values and one roughness parameter) could be solved simultaneously by minimizing a cost function. The algorithm was tested with a series of Sentinel-1 dual-channel (VV + VH) data over croplands (sugarcane and cassava) of Northeast Thailand with an upscaling resolution of 1 km. Results show that the proposed algorithm could well capture the temporal change of soil moisture with root-mean-square errors within 0.06 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> when ignoring days with precipitation, and could achieve a similar spatial pattern of soil moisture as detected from the Soil Moisture Active Passive mission, indicating the Sentinel-1 might be a proper tool for agricultural water management.
Highlights
A S AN important element of terrestrial water resources, soil moisture is an essential parameter for agricultural water management, especially for northeastern Thailand which has frequently suffered from floods and droughts
To obtain the optimal values of parameter-b and singlescattering albedo for different vegetation types in the C-band, this letter used the proposed forward modeling combined with measured soil moisture data to simulate the backscattering observed by Sentinel-1, and the root mean square error (RMSE) between simulated backscatter and the actual value was calculated
This letter proposed a soil moisture retrieval method that makes use of the information contained in time-series synthetic aperture radar (SAR) data
Summary
A S AN important element of terrestrial water resources, soil moisture is an essential parameter for agricultural water management, especially for northeastern Thailand which has frequently suffered from floods and droughts. The backscatter information observed by SAR is related to soil moisture, and or even more to vegetation (quantity, structure), surface roughness, soil physical properties (structure, composition, etc.), and radar system parameters (frequency, incidence angle, and polarization) [3]. Like other remote sensing issues, retrieval of soil moisture through SAR backscatter observations is a typical “ill-posed” problem (the number of observations always less than the number of unknowns). How to use SAR data to estimate soil moisture more accurately, especially to improve the retrieval accuracy over vegetated regions, has been receiving much attention [4], [5]
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