Assessment of the smap brightness temperature downscaling algorithm using active-passive observations over a tropical forest in Mexico

Abstract

Continuous monitoring of physical parameters such as soil moisture is crucial to improve food sustainability and risks mitigation. To estimate soil moisture, there are direct and nodirect measurement methodologies; each of these ones has its own advantages and disadvantages. Therefore, no-direct measurement methodologies have the ability to get wide spatial coverage of inaccessible places than other tools. Satellite remote sensing can be used to obtain soil moisture estimations by mean of microwave sensors (radar and radiometer). The SMAP satellite collected simultaneous active/passive observations from April to July 2015. The radar collected fine resolution images of 3 km per pixel simultaneously with Radiometer images with a spatial resolution of 36 km per pixel, approximately. However, passive observations are more sensitive to soil moisture changes than active observations; as a result, it is necessary to take advantage of these properties (sensitive and fine resolution) through a downscaling algorithm that gives us a medium spatial image with 9 km per pixel. The algorithm was implemented over a Mexican tropical forest in the Calakmul Biosphere Reserve in Campeche, Southern Mexico.