Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (8): 1249-1265.doi: 10.1007/s11442-020-1780-1

• Research Articles • Previous Articles     Next Articles

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

CAO Juan1, ZHANG Zhao1,*(), ZHANG Liangliang1, LUO Yuchuan1, LI Ziyue1, TAO Fulu2,3   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology/MEM&MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-03-10 Accepted:2020-05-12 Online:2020-08-25 Published:2020-10-25
  • Contact: ZHANG Zhao
  • About author:Cao Juan, PhD Candidate, specialized in agricultural disaters and agricultural insurance
  • Supported by:
    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)


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.

Key words: chilling injury, Google Earth Engine (GEE), CROPGRO-Soybean, soybean, yield loss, cold degree days (CDD)