Surface Soil Moisture Estimation Taking into Account the Land Use and Fractional Vegetation Cover by Multi-Source Remote Sensing

Rencai Lin, Xiaohua Xu (), Xiuping Zhang, Zhenning Hu, Guobin Wang, Yanping Shi, Xinyu Zhao and Honghui Sang
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Rencai Lin: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Xiaohua Xu: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Xiuping Zhang: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Zhenning Hu: School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
Guobin Wang: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Yanping Shi: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Xinyu Zhao: School of Soil and Water Conservation, Nanchang Institute of Technology, Nanchang 330099, China
Honghui Sang: School of Soil and Water Conservation, Nanchang Institute of Technology, Nanchang 330099, China

Agriculture, 2025, vol. 15, issue 5, 1-24

Abstract: Surface soil moisture (SSM) plays a pivotal role various fields, including agriculture, hydrology, water environment, and meteorology. To investigate the impact of land use types and fractional vegetation cover (FVC) on the accuracy of SSM estimation, this study conducted a comprehensive analysis of SSM estimation performance across diverse land use scenarios (e.g., multiple land use combinations and cropland) and varying FVC conditions. Sentinel-2 NDVI and MOD09A1 NDVI were fused by the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) to obtain NDVI with a temporal resolution better than 8 d and a spatial resolution of 20 m, which improved the matching degree between NDVI and the Sentinel-1 backscattering coefficient ( σ 0 ). Based on the σ 0 , NDVI, and in situ SSM, combined with the water cloud model (WCM), the SSM estimation model is established, and the model of each land use and FVC is validated. The model has been applied in Handan. The results are as follows: (1) Compared with vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization is more sensitive to soil backscattering ( σ soil 0 ). In the model for multiple land use combinations (Multiple-Model) and the model for the cropland (Cropland-Model), the R 2 increases by 0.084 and 0.041, respectively. (2) The estimation accuracy of SSM for the Multiple-Model and Cropland-Model is satisfactory (Multiple-Model, RMSE = 0.024 cm 3 /cm 3 , MAE = 0.019 cm 3 /cm 3 , R 2 = 0.891; Cropland-Model, RMSE = 0.023 cm 3 /cm 3 , MAE = 0.018 cm 3 /cm 3 , R 2 = 0.886). (3) When the FVC > 0.75, the accuracy of SSM by the WCM is low. It is suggested the model should be applied to the cropland where the FVC ≤ 0.75. This study clarified the applicability of SSM estimation by microwave remote sensing (RS) in different land uses and FVCs, which can provide scientific reference for regional agricultural irrigation and agricultural water resources management. The findings highlight that the VV polarization-based model significantly improves SSM estimation accuracy, particularly in croplands with FVC ≤ 0.75, offering a reliable tool for optimizing irrigation schedules and enhancing water use efficiency in agriculture. These results can aid in better water resource management, especially in regions with limited water availability, by providing precise soil moisture data for informed decision-making.

Keywords: water cloud model; Sentinel-1; NDVI; ESTARFM; vegetation water content (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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