Soil Moisture Deficit Estimation Through SMOS Soil Moisture and MODIS Land Surface Temperature

Abstract Soil moisture acts as a key variable in the exchanges of water and energy from land surface to atmosphere. The work presented here showed the first-time comprehensive evaluation of land surface temperature (LST) derived from moderate resolution imaging spectroradiometer (MODIS) and soil moisture from soil moisture and ocean salinity (SMOS) for prediction hydrological soil moisture deficit (SMD). The main focus of the work relies on the application of adaptive neuro fuzzy inference system (ANFIS) for evaluating its capabilities towards SMD estimation. The performances between estimated and ANFIS simulated SMD are assessed in terms of Nash-Sutcliffe Efficiency, root mean square error, and the percentage of bias. The performance statistics revealed a good agreement between benchmark and ANFIS estimated SMD from MODIS as compared to the SMOS. This present study has strong supportive evidence that there are SMOS soil moisture and MODIS LST products for different hydrometeorological studies. The assessment of the product with respect to this peculiar evidence is an important step for successful development of hydrometeorological model and forecasting system.

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