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

OBREGON, MATIAS, PULIDO, MANUEL; LUCINI

2024 7th IEEE Biennial Congress of Argentina, ARGENCON 2024

Institute of Electrical and Electronics Engineers Inc.

Año: 2024

—Soil moisture is a variable of direct interest for agri-cultural production. However, its high spatio-temporal variabilitymakes estimation difficult. Remote sensors aboard satellitesenable for soil moisture measurements over large areas withgood spatial and temporal resolution. Despite these advantages,the measurements are limited, biased, and might be spurious,requiring corrections. This work proposes the evaluation of dataassimilation based on the Ensemble Kalman filter with perturbedobservations to estimate soil moisture in the Argentinian Pampasunder the extreme drought conditions of 2022. Two techniquesfor observational bias correction in soil moisture assimilation areevaluated: cumulative distribution matching and normal deviatecorrection. Due to the drought-induced moisture anomaly, thecumulative distribution matching corrections result in almostconstant corrected observations for the entire dry season inSMAP,, and in a less determinant way for SMOS. Despitethis, the cumulative distribution matching technique presentsthe lowest root mean squared error when compared to in-situmeasurements.