Spatial series approach to estimate soil moisture over wheat fields from a single SAR image
Year: 2025
Type: article
Abstract: The coupling of soil moisture (SM) with other factors, such as surface roughness and vegetation coverage, impedes the generation of large-scale high-precision SM products. Eliminating the effects of vegetation and roughness based on backscattering intensity ratios (BIRs) from time-series data is the primary approach for addressing this problem. However, this method is susceptible to the low temporal resolution of SAR images, leading to unstable retrieval results. In this study, we propose to decouple SM from other factors using a spatial series approach (SSA), which requires only a single synthetic aperture radar (SAR) image and utilizes the BIRs of spatial series points to retrieve SM. The core idea is to employ BIRs to implicitly compensate for vegetation and roughness effects, thereby avoiding explicit parameter estimation with inherent biases. For this purpose, spatial series points are selected based on the incidence angle, volume scattering power, canopy dominant orientation, and roughness. Then, the BIRs of these points are used for SM retrieval. Experiments are conducted using L-band UAVSAR data acquired at different times. The experimental results show that the root mean square error (RMSE) of SM retrieved by the SSA is around 10 %, and the correlation coefficient exceeds 0.7. This approach holds value by expanding the observational dimensions and enhancing SM monitoring capabilities, particularly in scenarios where time-series SAR data acquisition is unfeasible. Future integration with temporal-series methods could establish a spatiotemporal-series inversion framework, which would markedly advance large-scale SM retrieval research. • Spatial series approach (SSA) is proposed to retrieve SM using intensity ratios from spatially homogeneous pixels. • SSA expands SAR available observation dimensions enabling future spatiotemporal SM inversion frameworks. • Comprehensive validation demonstrates SSA's effectiveness for SM retrieval.
Source: Agricultural Water Management
Authors: Wentao Han, Mingxu Wang, Yangyang Cao, Zhengdong Luo, Cui Zhou, Jianjun Zhu, Haiqiang Fu, Qinghua Xie
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Life on land
Open Access status: gold
Estimated APC paid: $3,070
OpenAlex ID: W4415281059
