Soil moisture estimation using ground scatterometer and Sentinel-1 data

Geeta T. Desai, Abhay N. Gaikwad
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Abstract

Soil moisture (SM) is a crucial criterion for agronomics and the management of water resources, particularly in areas where the socio-economic status and significant source of income depend upon agriculture and related sectors. This paper intends to estimate SM over the vegetative area using a generalized regression neural network (GRNN) and ground scatterometer and compare the results with SM retrieved using Sentinel-1 data. At the same time, random forest regression (RFR) and support vector regression (SVR) models are used for SM estimation. Correlation analysis results concluded that L-band HV-polarization at 300 incidence angle showed the highest correlation with the measured field parameters. This study investigated backscattering coefficients, VV/VH polarization ratio and polarization phase difference over wheat’s entire growth phase to estimate SM. The results indicate that the GRNN with backscattering coefficients and polarization ratio provided the highest accuracy compared to the random forest (RF) and SVR with the root mean square error of 0.093 over the Yavatmal District, Maharashtra, India.
利用地面散射计和 Sentinel-1 数据估算土壤湿度
土壤湿度(SM)是农艺学和水资源管理的一个重要标准,尤其是在社会经济地位和重要收入来源依赖于农业及相关部门的地区。本文旨在利用广义回归神经网络(GRNN)和地面散射计估算植被区的土壤水分,并将估算结果与利用哨兵-1 数据获取的土壤水分进行比较。同时,随机森林回归(RFR)和支持向量回归(SVR)模型也被用于估算SM。相关性分析结果表明,入射角为 300 的 L 波段 HV 极化与测得的场参数相关性最高。本研究调查了小麦整个生长阶段的反向散射系数、VV/VH 偏振比和偏振相位差,以估算 SM。结果表明,在印度马哈拉施特拉邦 Yavatmal 地区,与随机森林(RF)和 SVR 相比,带有反向散射系数和偏振比的 GRNN 的精度最高,均方根误差为 0.093。
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