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

doi:10.1007/s11442-020-1780-1

	Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling
	Cao, Juan 1; Zhang, Zhao 1; Zhang, Liangliang 1; Luo, Yuchuan 1; Li, Ziyue 1; Tao, Fulu 2,3
刊名	JOURNAL OF GEOGRAPHICAL SCIENCES
	2020-08-01
卷号	30 期号:8 页码:1249-1265
关键词	chilling injury Google Earth Engine (GEE) CROPGRO-Soybean soybean yield loss cold degree days (CDD)
ISSN号	1009-637X
DOI	10.1007/s11442-020-1780-1
通讯作者	Zhang, Zhao(zhangzhao@bnu.edu.cn)
英文摘要	Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields. Timely and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster reduction. Therefore, we propose a novel means to efficiently assess the impacts of chilling injury on soybean. Specific chilling injury events were diagnosed in 1989, 1995, 2003, 2009, and 2018 in Oroqen community. In total, 512 combinations scenarios were established using the localized CROPGRO-Soybean model. Furthermore, 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 model. Finally, we calculated pixel yields losses according to the corresponding vulnerability models. The 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 scale. Specifically, 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 events. The results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.
资助项目	National Natural Science Foundation of China[41977405] ; National Natural Science Foundation of China[41571493] ; National Natural Science Foundation of China[31561143003] ; National Natural Science Foundation of China[31761143006] ; National Key Research & Development Program of China[2017YFA0604703] ; National Key Research & Development Program of China[2019YFA0607401]
WOS关键词	MAIZE
WOS研究方向	Physical Geography
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000547895400003
资助机构	National Natural Science Foundation of China ; National Key Research & Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/158406]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Zhao
作者单位	1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, MEM&MoE Key Lab Environm Change & Nat Hazards, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Cao, Juan,Zhang, Zhao,Zhang, Liangliang,et al. Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2020,30(8):1249-1265.
APA	Cao, Juan,Zhang, Zhao,Zhang, Liangliang,Luo, Yuchuan,Li, Ziyue,&Tao, Fulu.(2020).Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling.JOURNAL OF GEOGRAPHICAL SCIENCES,30(8),1249-1265.
MLA	Cao, Juan,et al."Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling".JOURNAL OF GEOGRAPHICAL SCIENCES 30.8(2020):1249-1265.