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Spectral Model for Soybean Yield Estimate Using MODIS/EVI Data 被引量:2
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作者 Anibal Gusso jorge ricardo ducati +2 位作者 Mauricio Roberto Veronez Damien Arvor Luiz Gonzaga da Silveira Jr. 《International Journal of Geosciences》 2013年第9期1233-1241,共9页
Attaining reliable and timely agricultural estimates is very important everywhere, and in Brazil, due to its characteristics, this is especially true. In this study, estimations of crop production were made based on t... Attaining reliable and timely agricultural estimates is very important everywhere, and in Brazil, due to its characteristics, this is especially true. In this study, estimations of crop production were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) images. The objective was to evaluate the coupled model (CM) performance of crop area and crop yield estimates based solely on MODIS/EVI as input data in Rio Grande do Sul State, which is characterized by high variability in seasonal soybean yields, due to different crop development conditions. The resulting production estimates from CM were compared to official agricultural statistics of Brazilian Institute of Geography and Statistics (IBGE) and the National Company of Food Supply (CONAB) at different levels from 2000/2001 to 2010/2011 crop years. Results obtained with CM indicate that its application is able to generate timely production estimates for soybean both at municipality and local levels. Validation estimates with CM at State level obtained R2 = 0.95. Combining all cropping years at municipality level, estimates were highly correlated to official statistics from IBGE, with R2 = 0.91 and RMSD = 10,840 tons. Spatially interpolated comparisons of yield maps obtained from the CM estimates and IBGE data also showed visual similarity in their spatial distribution. Local level comparisons were performed and presented R2 = 0.95. Implications of this work point out that time-series analysis of production estimates are able to provide anticipated spatial information prior to the soybean harvest. 展开更多
关键词 Remote Sensing Coupled Model Soy Yield FORECAST Satellite Images
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Monitoring Heat Waves and Their Impacts on Summer Crop Development in Southern Brazil
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作者 Anibal Gusso jorge ricardo ducati +2 位作者 Mauricio Roberto Veronez Victor Sommer Luiz Gonzaga da Silveira Junior 《Agricultural Sciences》 2014年第4期353-364,共12页
Periods in the soybean summer cycle that are sensitive to the occurrence of high temperatures were studied. An analysis was performed on the variability of soybean yields associated with crop canopy temperatures durin... Periods in the soybean summer cycle that are sensitive to the occurrence of high temperatures were studied. An analysis was performed on the variability of soybean yields associated with crop canopy temperatures during key development periods. A land surface temperature (LST) data series from MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua satellite was processed between 2003 and 2012 that covered the entire state of Rio Grande do Sul, in Brazil. Enhanced vegetation index (EVI) data from MODIS on the Terra satellite were used to monitor the LST during different phenological stages. Spatially interpolated maps of soybean yield distributions were generated using data obtained from Instituto Brasileiro de Geografia e Estatística (IBGE) at state and municipality levels. The results indicate that canopy-LST occurrence in mid-February, during the grain filling, is most correlated to yield reduction (R2 = 0.82 and RMSD = 14.4%). At the state level, the average yield is 2003 kg·ha-1 with a standard deviation of 308 kg·ha-1. The overall average of the canopy-LST is 305.0 K (31.8°C) with a standard deviation of 1.9 K. The slope of the downward linear relationship between canopy-LST and yield was -28.7%. These results indicate that monitoring heat wave events can provide important information for characterising agriculture vulnerability. 展开更多
关键词 Land Surface Temperature SOYBEAN Remote Sensing CANOPY Vegetation Index MODIS Data
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