Remote sensing-based estimation of rice yields using various models:A critical review
作者机构：School of EnvironmentUniversity of AucklandAucklandNew Zealand School of Biological SciencesUniversity of AucklandAucklandNew Zealand
出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)
年 卷 期：2021年第24卷第4期
页 面：580-603页
核心收录：
学科分类：083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 09[农学] 0804[工学-仪器科学与技术] 0903[农学-农业资源与环境] 0816[工学-测绘科学与技术] 081602[工学-摄影测量与遥感] 0901[农学-作物学] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程]
基 金：This work is supported by New Zealand Ministry of Foreign Affairs and Trade PhD Scholarship and the University of Auckland’s Postgraduate Research Student Support Ministry of Foreign Affairs and Trade,New Zealand,University of Auckland
主 题：Process-based crop model data assimilation empirical model geospatial applications remote sensing rice yield mapping yield estimation
摘 要：Reliable estimation of region-wide rice yield is vital for food security and agricultural ***-scale models have increased our understanding of rice yield and its estimation under theoretical environmental ***,they offer little infor-mation on spatial variability effects on farm-scale *** Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional *** research used RS with rice models for reliable yield *** several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize *** paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice ***,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS ***,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield ***,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling *** rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change *** studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.
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