Abstract:
Monitoring the effects of water availability on vegetation globally using satellites is important for applications such as drought early warning, precision agriculture, and food security as well as for more broadly understanding relationships between water and carbon cycles. In this global study, we compare anomalies from TROPOspheric Monitoring Instrument (TROPOMI) solar induced fluorescence (SIF) and other satellite-based reflectance indicators, such as the normalized difference vegetation index (NDVI). We focus specifically on anomalies resulting from changes in water availability on timescales of weeks. Water availability anomalies are captured by variations in root-zone soil moisture (RZM) that extends to about 1 m depth, derived with the Global Modeling and Assimilation Office (GMAO) land surface model driven by assimilated meteorology. The TROPOMI SIF data record is relatively short (about 2 years), so a longer-term climatology of SIF is created using machine learning with reflectance data from the MODerate-resolution Imaging Spectroradiometer (MODIS) from which TROPOMI SIF anomalies over the past few years are computed. SIF contains information about vegetation structure as well as physiology related to actual photosynthesis, while reflectance-based indices are related to potential photosynthesis. TROPOMI data has unique daily global coverage (though thick clouds may obscure SIF observations) and a pixel footprint of the order of 7 km2.