Assessing the combination of passive and active microwave satellite observations (1.4 to 36 GHz) to estimate above ground biomass (AGB) globally

A Neural Network inversion method has been developed to estimate Above Ground Biomass (AGB) on a global scale using multiple microwave satellite observations from both passive and active instruments. The reference dataset is the AGB from the European Agency (ESA) Climate Climatology Initiative (CCI). The study evaluates the potential of each observation type individually and in combination, from current Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), and Advanced SCATterometer (ASCAT) satellite instruments. Additionally, auxiliary data from Normalized Difference Vegetation Index (NDVI from MODIS), land surface temperature (LST from ERA5), and soil moisture (SM from ERA5) are incorporated and evaluated into the retrieval process alongside microwave observations.Findings confirm that passive 1.4 GHz observations exhibit the highest sensitivity to AGB compared to other passive measurements up to 36 GHz. In contrast, active observations at 6 GHz demonstrate limited potential for AGB estimation when used in isolation, at least within the framework of this study. However, combining microwave observations between 1.4 and 36 GHz yields strong results compared to the CCI AGB dataset. The inclusion of NDVI, LST, and SM further enhances performance, achieving an R2 of 0.88 globally and an RMSE of 30 Mg/ha, as compared to the CCI AGB.The combination of passive microwave observations at 18 and 36 GHz, supplemented with auxiliary data, shows promise for assessing global AGB. With these passive microwave data available since the 1990s, long-term AGB dynamics could be estimated.