Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from microwave remote sensing techniques are typically complex due to inherent difficulty in characterizing the interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave radiation interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. On the other hand it may possible to use non-parametric methods like neural networks, fuzzy logic and multiple regressions to retrieve soil moisture distributions. In this study we use these methods to retrieve soil moisture from microwave remote sensing data. The fuzzy logic and neural network performed better when compared to multiple regression models. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significant impact on soil moisture retrievals with root mean square error (RMSE) being reduced by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than that from bare soil areas.