Soil Organic Carbon (SOC) is a measure of the total carbon content of the soil and is a vital soil health indicator. Over the decades, SOC has been estimated using sampling followed by rigorous laboratory-based testing methods. Spaceborne Microwave/Synthetic Aperture RADAR (SAR) remote sensing has proven to be a versatile tool for various soil study applications. However, there have been very few studies conducted for SOC estimation using SAR remote sensing. This study utilises time-series, C-band remotely sensed SAR data from Sentinel-1 A satellite for SOC estimation and compared the performances of Random Forest (RF) and Ordinary Least Squares (OLS) Regression models over agricultural areas of Rupnagar district of Punjab in India. A set of 96 soil samples were collected from 32 different agricultural field locations in Rupnagar district between November 2019 to January 2020. SAR backscatter of Vertically emitted and Vertically received (VV) and Vertically emitted and Horizontally received (VH) polarisation channels, from Sentinel-1, soil moisture, electrical conductivity, pH, temperature and SOC from the laboratory-based testing methods were used as regression parameters. The RF regression gave a Root Mean Square Error (RMSE) of 0.78 and R² statistics of 0.887, while the OLS method performed better with an RMSE of 0.53 and an R² value of 0.907. It was also observed that the backscatter from VV and VH polarisation channels, when used synergistically with field data, have the highest Feature Importance (FI) score in both RF and OLS regression models for SOC estimation.

土壤有机碳 (SOC) 是衡量土壤总碳含量的指标，是一项重要的土壤健康指标。几十年来，SOC 已通过采样和严格的基于实验室的测试方法进行估算。星载微波/合成孔径雷达 (SAR) 遥感已被证明是用于各种土壤研究应用的多功能工具。然而，使用 SAR 遥感进行 SOC 估计的研究很少。本研究利用来自 Sentinel-1 A 卫星的时间序列、C 波段遥感 SAR 数据进行 SOC 估计，并比较了随机森林 (RF) 和普通最小二乘 (OLS) 回归模型在旁遮普省 Rupnagar 区农业区的性能在印度。2019 年 11 月至 2020 年 1 月期间，从 Rupnagar 区的 32 个不同农田位置收集了一组 96 个土壤样本。垂直发射和垂直接收 (VV) 以及垂直发射和水平接收 (VH) 极化通道的 SAR 反向散射，来自 Sentinel- 1、土壤水分、电导率、pH、温度和SOC来自基于实验室的测试方法被用作回归参数。RF 回归给出了 0.78 的均方根误差 (RMSE) 和 R²统计量为 0.887，而 OLS 方法表现更好，RMSE 为 0.53，R ²值为 0.907。还观察到，当与现场数据协同使用时，来自 VV 和 VH 偏振通道的反向散射在用于 SOC 估计的 RF 和 OLS 回归模型中具有最高的特征重要性 (FI) 分数。