A Soil Moisture Retrieval Algorithm Based on Meteosat Imagery

Global carbon budget studies are currently dominated by temperature analysis since the importance of this meteorological variable on photosynthesis processes and soil carbon dynamics. Yet, the strong coupling between the carbon and hydrological cycles is a longstanding acquisition of the biogeophysical sciences. To take the important aspect of water limitation in carbon studies into account, water availability of vegetation is to be estimated firstly. Evidently soil moisture is an essential part of plant water availability. We present a strategy to retrieve soil moisture content from optical and thermal information using coarse resolution METEOSAT imagery with a robust pre-processing chain and an operational capacity: the integral METEOSAT Processing Chain (iMETEOSAT-Chain). Soil moisture is derived using the concept of thermal inertia, whereby the ratio of albedo with the difference of day and night land surface temperatures is combined. A soil moisture saturation index is calculated from thermal inertia data and processed by a 1 order Markov filter which converts surface values to soil moisture values of a 1 m soil profile. For the region of interest of Europe, for the growing season of 1997, we present soil moisture time series validated against EUROFLUX tower measurements. With spatial values of soil moisture content on a regional/continental scale, water limitation can then be incorporated into continental/global carbon budgeting.