Abstract

Due to improper agricultural and soil management, there has been a drop in crop yield over the last few years and food security has become a major issue. For a country like India, with a huge population to cater, the problem becomes more serious. Since the inception of remote sensing in scientific agriculture management, optical remote sensing along with field data has been used for soil health monitoring and mapping. SAR or microwave remote sensing has an all-weather and high temporal data availability which has found applications for various domains. For soil health studies of multiple soil classes and sub-classes of same type, both aerial and spaceborne SAR remote sensing is currently in use for a multitude of monitoring and parameter modelling approaches. This study utilizes Sentinel 1 A, C-band SAR remote sensing data with VV and VH polarization channels for surface soil moisture estimation for alluvial soil and its sub-types in Rupnagar of Punjab state in India. While Index based OLS Regression method for soil moisture estimation was done using backscatter from Sentinel-1A SAR data, it was validated using Normalized Differential Moisture Index (NDMI) generated from Sentinel 2 optical datasets. This approach though did not consider the actual soil moisture data from field yet gave a low Root Mean Squared Error (RMSE) of 0.5 and R2-statistics of 0.72 (72%) in training and testing phases. The Index based OLS (Ordinary Least Squares) Regression method for soil moisture estimation aims to establish a technique for cases when field data is either not available or the study area is not easily accessible. In the statistical approach with field data, the same OLS model, when replaced by on-field surface soil moisture data gave a RMSE of 1.9 and R2-statistics of 0.968 and 0.948 in training and testing phases respectively at 97.5% confidence level. The study is significant for using freely available optical and SAR remote sensing data parameters synergistically, in a simplified manner for surface soil moisture estimation. The results have comparable accuracies given by studies using commercial data and complex modelling approaches.

摘要

由于农业和土壤管理不当，过去几年农作物产量下降，粮食安全已成为主要问题。对于印度这样一个人口众多的国家来说，问题变得更加严重。自科学农业管理中的遥感开始以来，光学遥感与现场数据一起被用于土壤健康监测和制图。SAR 或微波遥感具有全天候和高时间数据可用性，已在各个领域得到应用。对于同一类型的多个土壤类别和子类别的土壤健康研究，航空和星载 SAR 遥感目前都用于多种监测和参数建模方法。本研究使用 Sentinel 1 A，印度旁遮普邦鲁普纳格尔冲积土及其亚型表层土壤水分估算的 C 波段 SAR 遥感数据，具有 VV 和 VH 偏振通道。虽然用于土壤水分估计的基于指数的 OLS 回归方法是使用 Sentinel-1A SAR 数据的反向散射完成的，但它使用从 Sentinel 2 光学数据集生成的归一化差分水分指数 (NDMI) 进行了验证。这种方法虽然没有考虑来自田间的实际土壤水分数据，但在训练和测试阶段给出了 0.5 的低均方根误差 (RMSE) 和 0.72 (72%) 的 R2 统计量。用于土壤水分估算的基于指数的 OLS（普通最小二乘）回归方法旨在为现场数据不可用或研究区域不易访问的情况建立一种技术。在使用现场数据的统计方法中，相同的 OLS 模型在被现场地表土壤水分数据取代时，在训练和测试阶段的 RMSE 为 1.9，R2 统计量分别为 0.968 和 0.948，置信度为 97.5%。该研究对于协同使用免费提供的光学和 SAR 遥感数据参数，以简化的方式进行地表土壤水分估计具有重要意义。使用商业数据和复杂建模方法的研究给出的结果具有相当的准确性。该研究对于协同使用免费提供的光学和 SAR 遥感数据参数，以简化的方式进行地表土壤水分估计具有重要意义。使用商业数据和复杂建模方法的研究给出的结果具有相当的准确性。该研究对于协同使用免费提供的光学和 SAR 遥感数据参数，以简化的方式进行地表土壤水分估计具有重要意义。使用商业数据和复杂建模方法的研究给出的结果具有相当的准确性。