Vegetation and climate: initial concepts about the relation between the sun-induced chlorophyll fluorescence (SIF) and the soil water content (SWC)

Climate conditions directly impact on vegetation growth, this is quite clear; yet, “how vegetation functioning alters climate?” is an open query rising many questions. Understanding the impact of vegetation functioning on climate is important to better comprehend climatological processes and thus to improve models, due to the influence of plants on the carbon and water cycles. For decades it was impossible to have information about plant functioning in the (regional to global) scale of climatological models, nevertheless, the advent of satellite-based remote sensing (RS) methods for the retrieval of sun-induced chlorophyll fluorescence (SIF, as a proxy of photosynthesis) made it feasible. For instance, using satellite SIF and precipitation data, Green et al. (2017; DOI 10.1038/ngeo2957) recently provided one of the first contributions in such direction. The authors reported that regional biosphere-atmosphere feedbacks can explain up to 30% of precipitation variance, mainly because of the role of plants in the regulation of the water flux from the soil towards the atmosphere. As a complement to such studies with focus on the vegetation-atmosphere link, in two recent studies we analyzed the relation that SIF has with the soil water content (SWC) at airborne and satellite scales. At airborne scale we found that (i) the SIF-SWC relation is crop- and growth stage-dependent, and that (ii) SIF showed a faster response to water limitations compared to conventional (reflectance-based) RS products. On the satellite level we found a strong impact of the SIF-SWC relation on the gross primary productivity (GPP) during a heat wave at European scale. With these contributions from the RS area, we aim to provide novel information that can help the meteorological research community to better understand how vegetation functioning can alter climatological processes, with potential applications in the improvement of climate models.