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Combining PlanetScope and Sentinel-2 images with environmental data for improved wheat yield estimation

International Journal of Digital Earth 2023年第1期16卷 847-867页

作者： Nizom Farmonov Khilola Amankulova József Szatmári jamol urinov Zafar Narmanov Jakhongir Nosirov LászlóMucsi Department of Geoinformatics Physical and Environmental GeographyUniversity of SzegedSzegedHungary Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research UniversityTashkentUzbekistan Department of Land resources Cadastre and GeoinformaticsKarshi Institute of Irrigation and Agrotechnology“TIIAME”National Research UniversityKarshiUzbekistan Soil Science Department of the Kashkadarya Region regional division of the Institute of Soil Science and Agrochemical Research KarshiUzbekistan

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 ***,a new generation of high-resolution satellites and CubeSat platforms has been *** 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 *** yield data from a combine harvester were used to train and validate a yield estimation model based on random forest *** vegetation indices(NDVI,GNDVI,MSAVI2,MTVI2,MTCI,reNDVI,SAVI,EVI and WDVI)and spectral bands were *** the model training,the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope ***,combining environmental data with the PlanetScope data realized the highest estimation *** 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 of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing.

关键词： Remote sensing BBCH-scale MSAVI2 PlanetScope random forest

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