Development And Evaluation of a New Temperature Effect Removal Algorithm for AMSR2 Satellite Soil Moisture Product using Brightness Temperature

Soil moisture (SM) is a crucial hydrological variable that connects land surface and atmospheric processes. Accurate soil moisture monitoring is necessary for understanding energy, water cycles, and ecological system processes. Satellite-based microwave remote sensing is an effective method for obtaining information on land surface hydrology around the globe. Moreover, satellite soil moisture products such as SMAP have been reported with temperature dependence error which is caused by the so-called "temperature effect (TE)," as same as in-situ soil water content (SWC) data, which was measured using the dielectrically measured method. This study aims to remove TE in AMSR2 Level 2 soil moisture products.
In this study, a data-driven method to remove the temperature effect in in-situ SWC data was improved to allow the direct use of satellite products of land surface temperature or brightness temperature measurements. The Mongolia site was selected, and the SWC and soil temperature data at 3 cm depth from 5th September 2016 to 31st October 2019 were used as the reference for developing and evaluating the new TE removal algorithm. To fit the large satellite footprint, areal mean SWC and soil temperature were prepared using the Thiessen polygon method.
According to the results of the newly developed removal algorithm of the temperature effect error, the difference between average ascending and descending crossing time data was eliminated by directly using brightness temperature. The correlation coefficient (R) was used to evaluate the agreement between ascending and descending after using the newly developed TE removal method. According to the results of R, the corrected AMSR2 products exhibited the best performance over the original AMSR2 products in the Mongolia network region, with a relatively higher R-value. This finding reveals that the correction improved the correlation between in-situ and satellite soil water content values at each of the chosen sites and shows that the corrected AMSR2 SWC values better agree with the corrected in-situ measurements.