xCO2 temporal variability above Brazilian agroecosystems: A remote sensing approach - Morais Filho, Luiz Fernando Favacho [UNESP]; Meneses, Kamila Cunha de [UNESP]; Santos, Gustavo André de Araújo [UNESP]; Bicalho, Elton da Silva [UNESP]; Rolim, Glauco de Souza [UNESP]; La Scala, Newton [UNESP] (Elsevier, 2021)
Agriculture and soil management practices are closely related to CO2 emissions in crop fields. These practices directly interfere on the carbon dynamics between the land and atmosphere. In this study, we investigated the temporal variability of the column-averaged dry-air mole fraction of atmospheric CO2 (xCO2), solar-induced chlorophyll fluorescence (SIF), and the normalized difference vegetation index (NDVI) in areas with the main agroecosystems in southern-central Brazil as a way to understand if and how crops cycle and agricultural management could be associated with the temporal variability of NDVI, SIF and xCO2. The study was carried out in areas corresponding to the three agroecosystems': sugarcane (Pradópolis, State of São Paulo, Brazil), cropland with soybean-corn succession (Santo Antônio do Paraíso, State of Paraná, Brazil), and grassland (Águas Claras, State of Mato Grosso do Sul, Brazil). Air temperature, precipitation, NDVI, and SIF and xCO2 were retrieved from NASA-POWER, NASA-GIOVANNI, SATVeg-EMBRAPA, and OCO-2, respectively, during a two-year study. Trends were removed from the NDVI, SIF, and xCO2 time series applying the regression method. A negative correlation between SIF and xCO2 was found in sugarcane and cropland areas, but in grasslands, no correlation showed up. Higher SIF values were observed in grassland (2.24 W m-2 sr-1 μm-1), and lower xCO2 values were observed above grains, which varied from 396.8 to 404.2 ppm. Both xCO2 and SIF followed more a seasonal pattern in sugarcane and annual crops, but over pasture this presented an unusual pattern related to higher precipitation events. Our results indicate a potential use of SIF and xCO2 which could help identifying potential sources and sinks of the main additional greenhouse gas over agricultural areas.
Keywords: Climate changes; Climate-smart agriculture; OCO-2; Remote sensing; SIF