Soil Moisture Estimation in the Argentinian Pampas Using Data Assimilation of SMAP and SMOS Retrievals

Soil moisture is a variable of direct interest for agricultural production. However, its high spatio-temporal variability makes estimation difficult. Remote sensors aboard satellites enable for soil moisture measurements over large areas with good spatial and temporal resolution. Despite these advantages, the measurements are limited, biased, and might be spurious, requiring corrections. This work proposes the evaluation of data assimilation based on the Ensemble Kalman filter with perturbed observations to estimate soil moisture in the Argentinian Pampas under the extreme drought conditions of 2022. Two techniques for observational bias correction in soil moisture assimilation are evaluated: cumulative distribution matching and normal deviate correction. Due to the drought-induced moisture anomaly, the cumulative distribution matching corrections result in almost constant corrected observations for the entire dry season in SMAP, and in a less determinant way for SMOS. Despite this, the cumulative distribution matching technique presents the lowest root mean squared error when compared to in-situ measurements.