Damage evaluation of soybean chilling injury based on Google Earth Engine(GEE)and crop modelling

作     者：CAO Juan ZHANG Zhao ZHANG Liangliang LUO Yuchuan LI Ziyue TAO Fulu 曹娟;张朝;张亮亮;骆玉川;李子悦;陶福禄

作者机构：State Key Laboratory of Earth Surface Processes and Resource Ecology/MEM&MoE Key Laboratory of Environmental Change and Natural HazardsFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Key Laboratory of Land Surface Pattern and SimulationInstitute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 100049China 

出 版 物：《Journal of Geographical Sciences》 (地理学报（英文版）)

年 卷 期：2020年第30卷第8期

页      面：1249-1265页

核心收录：

学科分类：0709[理学-地质学] 09[农学] 0901[农学-作物学] 0704[理学-天文学] 

基　　金：National Natural Science Foundation of China,No.41977405,No.41571493,No.31561143003 No.31761143006 National Key Research&Development Program of China,No.2017YFA0604703,No.2019YFA0607401 

主　　题：chilling injury Google Earth Engine(GEE) CROPGRO-Soybean soybean yield loss cold degree days(CDD) 

摘      要：Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain *** and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster ***,we propose a novel means to efficiently assess the impacts of chilling injury on *** chilling injury events were diagnosed in 1989,1995,2003,2009,and 2018 in Oroqen *** total,512 combinations scenarios were established using the localized CROPGRO-Soybean ***,we determined the maximum wide dynamic vegetation index(WDRVI)and corresponding date of critical windows of the early and late growing seasons using the GEE(Google Earth Engine)platform,then constructed 1600 cold vulnerability models on CDD(Cold Degree Days),the simulated LAI(Leaf Area Index)and yields from the CROPGRO-Soybean ***,we calculated pixel yields losses according to the corresponding vulnerability *** findings show that simulated historical yield losses in 1989,1995,2003 and 2009 were measured at 9.6%,29.8%,50.5%,and 15.7%,respectively,closely(all errors are within one standard deviation)reflecting actual losses(6.4%,39.2%,47.7%,and 13.2%,respectively).The above proposed method was applied to evaluate the yield loss for 2018 at the pixel ***,a sentinel-2A image was used for 10-m high precision yield mapping,and the estimated losses were found to characterize the actual yield losses from 2018 cold *** results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.