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Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO2 (xCO2) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO2 is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO2 (xCO2), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m−2 sr−1 μm−1 and 0.8 W m−2 sr−1 μm−1, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t−1 with low NDVI and xCO2 values, alongside SIF values lower than 0.4 W m−2 sr−1 μm−1 and greater than 1 W m−2 sr−1 μm−1. These findings underscore the potential of integrating SIF, xCO2, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops. [ABSTRACT FROM AUTHOR]

Background: The recent studies of the variations in the atmospheric column-averaged CO2 concentration ( X CO 2 ) above croplands and forests show a negative correlation between X CO 2 and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO2. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on X CO 2 above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2. Results: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual X CO 2 cycle. The daily model of X CO 2 estimated from Qg and RH predicts daily X CO 2 with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). Conclusion: The obtained results imply that a significant part of daily X CO 2 variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing, it is now possible to identify and characterize greenhouse gas emissions under deferment land uses. Given the above, this study aimed to characterize the spatial–temporal variability and the main factors controlling the average atmospheric CO2 column (Xco2) in the macroregion of Ribeirão Preto (MRP), São Paulo, a significant sugarcane producer in Brazil. We obtained remote sensing data from January 2015 to December 2018. The variables used were Xco2 and sun-induced fluorescence of chlorophyll (SIF) by NASA's Orbiting Carbon Observatory-2 satellite (OCO-2), relative humidity (RH), global radiation (Qg), and the average temperature at 2 m (T2m) by the NASA-POWER platform, and leaf area index (LAI) and evapotranspiration by Penman–Monteith (ET) by MODIS sensor. We evaluated the data in trimester's averages, where descriptive statistics, Pearson correlation and linear regression have been applied. The spatial distribution was made by the inverse distance weighted (IDW). The minimum (390.40 ± 0.41 ppm) and maximum (394.75 ± 0.34 ppm) mean of Xco2 was observed in the first quarter of 2015 and third quarter of 2017. The Xco2 obtained negative correlations with the SIF (−0.81), LAI (−0.81), RH (−0.74), ET (−0.84), and Qg (−0.51). Hotspots and coldspots of Xco2 tend to vary over the years. We conclude that the temporal variation of Xco2 above sugarcane areas in southern Brazil is well represented by a periodic function. Our results indicate photosynthesis and soil exposure after harvest are factors that could act as source and sink of CO2. [ABSTRACT FROM AUTHOR]

The optimization of conservationist production systems, whose goal is to increase carbon stocks and reduce greenhouse gas emissions, is considered one of the greatest challenges faced by agriculture nowadays. Therefore, this study aimed to assess the variation of soil CO 2 emission (FCO 2 ) and its relationship with soil attributes under long-term no-tillage systems with different successions of summer and winter crop sequences. Treatments consisted of combinations of three summer and two winter crop sequences. Summer sequences were maize monocrop (MM), soybean monocrop (SS), and soybean-maize intercrop (SM), while winter crops were crotalaria and maize. FCO 2 showed no difference among summer sequences (p > 0.05). For winter crops, however, the soil under crotalaria crop residues presented higher FCO 2 values (1.03 ± 0.027 μmol m -2  s -1 ) when compared to that under maize crop residues (0.94 ± 0.027 μmol m -2  s -1 ). Soil moisture presented the greatest influence on the temporal variation of FCO 2 , being correlated in the summer sequences MM (r = 0.79; p < 0.0001) and SS (r = 0.70; p = 0.002), as well as in the winter crops crotalaria (r = 0.78; p < 0.0001) and maize (r = 0.66; p = 0.005). In the Oxisol under no-tillage for >14 years, the spatial variation of FCO 2 was explained by the soil physical attributes total porosity, macroporosity, microporosity, and soil temperature. The soil under crotalaria crop residues as a winter crop had an improvement in soil physical attributes, leading to a more aerated environment and hence a higher CO 2 production process. However, the winter crops crotalaria (38.65 ± 0.08 Mg ha -1 ) and maize (38.14 ± 0.09 Mg ha -1 ) also provided a higher carbon stock on this tropical soil. Maize monocrop (41.13 ± 0.11 Mg ha -1 ) as a summer crop under no-tillage system also promoted higher carbon stocks on this tropical soil. A strategy to optimize no-tillage systems in terms of FCO 2 reduction and increase in soil carbon stock is related to the adoption of crop cultivation that includes legumes and grasses under intercropping and succession. Therefore, our results suggested that the summer sequences used in this study might contribute to reducing FCO 2 and that both winter crops influenced the increased soil carbon stock., (Copyright © 2019 Elsevier B.V. All rights reserved.)