Soil Moisture Mapping in Vegetated Area Using Landsat and Envisat ASAR Data

Physical model is always complicated to estimate soil moisture content, while machine learning algorithms have potential advantages in retrieving information from remote sensing data. This paper takes the middle stream of Heihe River Basin in China as the study area. The neural network, one of the most common machine learning algorithms, is used to retrieve soil moisture from active microwave data and optical data. Landsat data and Envisat ASAR data covered the study area were acquired in July 2008. The neural networks were trained with ground truth data and input parameters extracted from remote sensing data including bands information, Normalized Difference Vegetation Index (NDVI), Brightness Index (BI), the dual polarizations (HH and VV) and the ratio (HH/VV). Compared to an existing result using an empirical model with purely Envisat data in the same area, this study showed a slightly better correlation between the measured and estimated soil moisture (R 2 =0.75). It also revealed that the model with multi-source data had a better performance than the one with only a single source data. Finally, the verified model was applied to the whole study area, and it demonstrated that this method has operational potential for estimating soil moisture under the vegetated area in the middle stream of Heihe River Basin.