A method to estimate soil moisture from Airborne Hyperspectral Scanner (AHS) and ASTER data: Application to SEN2FLEX and SEN3EXP campaigns

In this paper the soil moisture is estimated at airborne level and at satellite level by combining remotely sensed images with in situ measurements. At airborne level we process high-resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor, and at satellite level we compute images acquired with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study has been accomplished in the framework of two field campaigns in the Barrax region (Spain): the SEN2FLEX (SENtinel-2 and FLuorescence EXperiment) campaign which was developed in July of 2005 and the SEN3EXP (Sentinel-3 Experiment) campaign which was carried out in June of 2009. The methodology proposed considers the correlation between the surface temperature, the Normalized Difference Vegetation Index (NDVI) and the emissivity. With this methodology the soil moisture from AHS data can be obtained with a Root Mean Square Error (RMSE) of 0.05 m3/m3 compared with ground measurements and from ASTER images with a RMSE of 0.06 m3/m3.

► In this paper the soil moisture is estimated from AHS and ASTER images.
► Results suggest that inclusion of emissivity should improve soil moisture retrievals.
► With this methodology the soil moisture can be estimated with a RMSE of 0.05 m3/m3.