Land surface fluxes such as Soil Moisture (SM) and Soil Temperature (ST) are very important variables for many applications that includes agriculture water management, weather and climate prediction, natural disasters etc. Both are important for understanding soil processes, hydrological balances as well as changes in microbial population. Mapping of the soil moisture content at various depth is crucial for the sustenance of water resources and also to understand about the development of crops in forms of quality and yield. With changing environmental conditions, there is a need of approaches for estimating SM and ST in various climatic and geographic situations. Towards this, Earth Observation datasets at higher resolutions from satellites such as Sentinel 1 and 2, could play an important role in the monitoring of SM and ST over the larger areas. For estimation of SM and ST, machine learning approaches could be effective. This research looked into the possibilities of using Earth Observation (EO) data of Sentinel-1 (S1) and Sentinel-2 (S2) simultaneously to estimate SM and ST by using the machine learning methods such as random forest (RF) and Support Vector Machines (SVM). The coefficient of correlation (r), root mean square error (RMSE), and Bias are utilized in model enactment for accuracy and comparative analysis of the models used. The overall analysis indicates that the SVM model (r = 0.85, RMSE = 2.54, Bias = −0.05) is the second most appropriate after the RF model (r = 0.89, RMSE = 2.34, Bias = 0) for estimating land surface fluxes (SM and ST).

中文翻译：

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使用机器学习技术评估用于陆地表面通量估计的光学/红外和微波数据集

土壤湿度 (SM) 和土壤温度 (ST) 等地表通量对于许多应用来说都是非常重要的变量，包括农业用水管理、天气和气候预测、自然灾害等。两者对于了解土壤过程、水文平衡都很重要作为微生物种群的变化。绘制不同深度的土壤湿度图对于维持水资源以及了解农作物的质量和产量发育至关重要。随着环境条件的变化，需要在各种气候和地理情况下估计 SM 和 ST 的方法。为此，哨兵 1 号和哨兵 2 号等卫星提供的更高分辨率的地球观测数据集可以在更大区域的地表卫星和卫星观测中发挥重要作用。对于 SM 和 ST 的估计，机器学习方法可能是有效的。本研究探讨了同时使用 Sentinel-1 (S1) 和 Sentinel-2 (S2) 的地球观测 (EO) 数据通过随机森林 (RF) 和支持向量等机器学习方法来估计 SM 和 ST 的可能性机器（SVM）。模型制定中使用相关系数 (r)、均方根误差 (RMSE) 和偏差，以确保所用模型的准确性和比较分析。总体分析表明，SVM 模型（r = 0.85，RMSE = 2.54，Bias = -0.05）是继 RF 模型（r = 0.89，RMSE = 2.34，Bias = 0）之后第二个最适合估计地表通量的模型（ SM 和 ST）。