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

作     者：Nizom Farmonov Khilola Amankulova József Szatmári Jamol Urinov Zafar Narmanov Jakhongir Nosirov LászlóMucsi 

作者机构：Department of GeoinformaticsPhysical and Environmental GeographyUniversity of SzegedSzegedHungary Tashkent Institute of Irrigation and Agricultural Mechanization EngineersNational Research UniversityTashkentUzbekistan Department of Land resourcesCadastre 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 ResearchKarshiUzbekistan 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：847-867页

核心收录：

学科分类：0708[理学-地球物理学] 09[农学] 0903[农学-农业资源与环境] 0816[工学-测绘科学与技术] 0901[农学-作物学] 

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] 

主　　题：Remote sensing BBCH-scale MSAVI2 PlanetScope random forest 

摘      要：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.