The Impact of National Land Cover and Soils Data on SMOS Soil Moisture Retrieval Over Canadian Agricultural Landscapes

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To ensure sustainable agriculture production, the availability of water in the right quantity and at the right time is critical, with extremes in availability resulting in severe impacts on the agricultural sector. Delivery of timely and accurate soil moisture data can play a vital role in monitoring the status of available water reserves for this sector. Passive microwave sensors, such as the Soil Moisture and Ocean Salinity (SMOS), are well suited for monitoring vast landscapes given their all-weather capabilities, large spatial footprint, frequent revisit, and the sensitivity of microwave emissions to the soil dielectric. This study examines the impact of exploiting Canadian soil and land cover datasets in the retrieval of soil moisture from SMOS over an agricultural area in the province of Manitoba (Canada). Results demonstrate that global datasets that are integrated within the current SMOS processor perform adequately when field measured soil moisture is compared to estimates of soil moisture by SMOS ( ${{\rm R}^2}$ of 0.70 ( ${\rm p} ) and root-mean-square error (RMSE) of 7.15% with a negative (dry) bias of $-0.05\%$ ). Overall, this study showed that ingesting high-quality national datasets into the SMOS soil moisture retrieval algorithm did not fully resolve the underestimation of soil moisture, suggesting that further investigation is required to understand this bias. Also, several approaches were evaluated to improve statistical field-derived soil moisture representation in the validation of SMOS soil moisture retrieval and it is clear that good representation of soil moisture as a function of soil textures is crucial to accurately validate SMOS soil moisture products.