Satellite images are widely used for crop yield estimation,but their coarse spatial resolution means that they often fail to provide detailed information at thefield scale.Recently,a new generation of high-resolution ...Satellite images are widely used for crop yield estimation,but their coarse spatial resolution means that they often fail to provide detailed information at thefield scale.Recently,a new generation of high-resolution satellites and CubeSat platforms has been launched.In this study,satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables,and the improvement in wheat yield estimation was evaluated.Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression.Nine vegetation indices(NDVI,GNDVI,MSAVI2,MTVI2,MTCI,reNDVI,SAVI,EVI and WDVI)and spectral bands were tested.During the model training,the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data.However,combining environmental data with the PlanetScope data realized the highest estimation accuracy.For the validated models,adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy,and the results were slightly better with the Sentinel-2 data than with the PlanetScope data.Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing.展开更多
基金supported by the University of Szeged Open Access Fund[grant no 6077]Ministry of Innovation and Technology of Hungary through the National Research,Development and Innovation Fund Project[grant no TKP2021-NVA].
文摘Satellite images are widely used for crop yield estimation,but their coarse spatial resolution means that they often fail to provide detailed information at thefield scale.Recently,a new generation of high-resolution satellites and CubeSat platforms has been launched.In this study,satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables,and the improvement in wheat yield estimation was evaluated.Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression.Nine vegetation indices(NDVI,GNDVI,MSAVI2,MTVI2,MTCI,reNDVI,SAVI,EVI and WDVI)and spectral bands were tested.During the model training,the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data.However,combining environmental data with the PlanetScope data realized the highest estimation accuracy.For the validated models,adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy,and the results were slightly better with the Sentinel-2 data than with the PlanetScope data.Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing.
