High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems - da Costa, Luis Miguel [UNESP]; de Mendonça, Gislaine Costa [UNESP]; Araújo Santos, Gustavo André de; Moraes, José Reinaldo da Silva Cabral de [UNESP]; Colombo, Roberto; Panosso, Alan Rodrigo [UNESP]; La Scala, Newton [UNESP] (Elsevier, 2023)
The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015–2018. SIF spatial variability in each biome presented significant spatial variability structures with high R2 values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m−2 sr−1 μm−1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m−2 sr−1 μm−1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.
Keywords: Biome sensitivity; Photosynthesis; Physiognomic diversity; Remote sensing