Adjustments to SIF Aid the Interpretation of Drought Responses at the Caatinga of Northeast Brazil

Abstract

Sun-Induced chlorophyll Fluorescence (SIF) relates directly to photosynthesis yield and stress but there are still uncertainties in its interpretation. Most of these uncertainties concern the influences of the emitting vegetation’s structure (e.g., leaf angles, leaf clumping) and biochemistry (e.g., chlorophyll content, other pigments) on the radiative transfer of fluorescent photons. The Caatinga is a large region in northeast Brazil of semiarid climate and heterogeneous vegetation, where such biochemical and structural characteristics can vary greatly even within a single hectare. With this study we aimed to characterize eleven years of SIF seasonal variation from Caatinga vegetation (2007 to 2017) and to study its responses to a major drought in 2012. Orbital SIF data from the instrument GOME-2 was used along with MODIS MAIAC EVI and NDVI. Environmental data included precipitation rate (TRMM), surface temperature (MODIS) and soil moisture (ESA CCI). To support the interpretation of SIF responses we used red and far-red SIF adjusted by the Sun’s zenith angle (SIF-SZA) and by daily Photosynthetically Active Radiation (dSIF). Furthermore, we also adjusted SIF through two contrasting formulations using NDVI data as proxy for structure and biochemistry, based on previous leaf-level and landscape level studies: SIF-Yield and SIF-Prod. Data was tested with time-series decomposition, rank correlation, spatial correlation and Linear Mixed Models (LMM). Results show that GOME-2 SIF and adjusted SIF formulations responded consistently to the observed environmental variation and showed a marked decrease in SIF emissions in response to a 2012 drought that was generally larger than the corresponding NDVI and EVI decreases. Drought sensitivity of SIF, as inferred from LMM slopes, was correlated to land cover at different regions of the Caatinga. This is the first study to show correlation between landscape-level SIF and an emergent property of ecosystems (i.e., resilience), showcasing the value of remotely sensed fluorescence for ecological studies.

Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology

“…Table 8 lists the predictors and model performance of past SIFrefactoring products. Comparatively, the model performance in this study reached a level on par with recent regional-scale SIF reconstruction products (Bontempo et al, 2020;Guo et al, 2020), i.e., our model R 2 for high and LLG regions reached 0.77 for the June-October period. In addition, we found that for the midlatitude forest and HLG, their R 2 could be increased further when using a more complex algorithm.…”

“…Similarly, Li and Xiao (2019) used EVI, PAR, vapor pressure deficit (VPD), and air temperature of 0.05 • MODIS to build a global seasonal scale regression model with an R 2 = 0.79, extending the discontinuous OCO-2 detection to 0.05 • global SIF. Bontempo et al (2020) used the 0.05 • MODIS-derived VIs, surface temperature, precipitation rate, soil moisture data, and surface reflectance, along with the rainfall rate and soil moisture product of the Tropical Rainfall Monitoring Mission (TRMM-TMPA), in a regional seasonal scale regression, thereby reconstructing the 0.5 • GOME-2 SIF in northeastern Brazil to 0.05 • , with an R 2 = 0.74. Gensheimer et al (2022) used NDVI, EVI, near-infrared reflectance of vegetation (NIRv), kernel normalized difference vegetation index (kNDVI), MODIS bands, and solar zenith angle (SZA), as well as MFs, to construct a convolutional neural network (CNN), which they named SIFnet (monthly regression at global scale), for non-US regions spanning 2018-2021.…”

AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America

“…The NAD layers from the AnisoVeg product have been used in previous studies to explore: the climate drivers of the Amazon forest greening (Wagner et al, 2017); the large-scale Amazon forest sensitivity to drought (Anderson et al, 2018); the structure and dominance of bamboo species in southwest Amazon (Dalagnol et al, 2018); the productivity in a flooded forest in eastern Amazon (Fonseca et al, 2019); the productivity and relationship with Sun-Induced Fluorescence over the Brazilian Caatinga biome (Bontempo et al, 2020); the relationships with leaf-age demography in central Amazon (Gonçalves et al, 2020); and the relationships with fire disturbance and SAR-based Vegetation Optical Depth in southern Amazon (Zhang et al, 2021).…”

“…Due to the improvements in cloud detection, aerosol retrieval and atmospheric correction, the MAIAC algorithm provides from 4 to 25% more high-quality retrievals than the traditional MOD09 product, with the largest estimate being observed for tropical regions (Lyapustin et al, 2021). Studies have used MODIS MAIAC observations with nadir-normalized geometry to assess Amazonian forests' structure, functioning, and impacts of environmental and climate change (Hilker et al, 2014;Wagner et al, 2017;Anderson et al, 2018;Dalagnol et al, 2018;Fonseca et al, 2019;Bontempo et al, 2020;Gonçalves et al, 2020;Zhang et al, 2021). For instance, such product provided reliable time series of surface reflectance data that allowed to identify largescale communities of bamboo species and their dynamics in the southwest Amazon .…”