Department of Engineering and Exact Sciences, São Paulo State University Faculty of Agrarian and Veterinary Sciences (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900, Jaboticabal, São Paulo, Brazil.
Advanced Campus Porto Franco, Federal Institute of Education, Science and Technology of Maranhão - IFMA, Rua Custódio Barbosa, no 09, Centro, Porto Franco, Maranhão, 65970-000, Brazil; Center of Agricultural, Natural and Literary Sciences, State University of the Tocantins Region of Maranhão (UEMASUL), Av. Brejo do Pinto, S/N - Brejo do Pinto, Estreito, Maranhão, 65975-000, Brazil.
Environ Res. 2023 Feb 1;218:114991. doi: 10.1016/j.envres.2022.114991. Epub 2022 Dec 9.
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 R 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 sr μm) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m sr μm, 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.
利用高分辨率数据集和降雨数据,基于巴西 06 个具有代表性的生态系统(亚马逊、卡廷加、塞拉多、大西洋森林、潘塔纳尔和潘帕斯),对卫星的一些 SIF 特征进行了研究,研究时间跨度为 2015 年至 2018 年。在所有研究年份中,每个生物群落的 SIF 空间变异性都表现出显著的空间变异性结构,R 值较高(>0.6,高斯模型)。降雨图与 SIF 的空间分布呈正相关,且相似,能够解释 SIF 空间变异性的 40%以上。亚马逊生物群系表现出较高的 SIF 值(>0.4 W m sr μm)和较高的年降雨量(约 2000 mm),而卡廷加生物群系的 SIF 值和降雨量最低(<0.1 W m sr μm,降雨量约 500 mm)。SIF 与降雨之间的线性关系在大多数生物群系中都是显著的(除了潘塔纳尔),并且表现出不同的敏感性,例如在卡廷加,SIF 主要受到降雨的影响,而在亚马逊,SIF 受降水事件的影响较小。我们认为,这里呈现的特征表明,SIF 可能会受到巴西某些生物群系降雨变化的高度影响。将降雨与 SIF 相结合,可以发现巴西生物群系之间的差异和相似之处,从而提高我们对这些生态系统如何受到气候变化和恶劣天气条件影响的理解。