Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (9): 1879-1892.doi: 10.11821/dlxb202009005

• Climate and Ecological Environment • Previous Articles     Next Articles

Damage evaluation on soybean chilling injury based on Google Earth Engine (GEE) and crop growth model

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

  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 Resources Research, CAS, Beijing 100101, China
  • Received:2019-06-15 Revised:2020-04-08 Online:2020-09-25 Published:2020-11-25
  • Contact: ZHANG Zhao E-mail:caojuan@mail.bnu.edu.cn;zhangzhao@bnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41977405);National Natural Science Foundation of China(31561143003);National Natural Science Foundation of China(41571493);National Natural Science Foundation of China(41621061)

Abstract:

The frequent occurrence of chilling injury has serious impacts on national-level food security, and it mainly affects the grain yield in Northeast China. Timely and accurate measures are desirable to assess the large-scale impacts, which are the prerequisites for disaster reduction and production recovery. Therefore, we propose a novel method to efficiently assess the impact of chilling injury on soybean. Inner Mongolia is taken as a case study. The specific chilling injury events was diagnosed to occur in 1989, 1995, 2003, 2009, and 2018. The 512 combinations of cold and field management simulation scenarios were established based on the localized CROPGRO-Soybean model. Furthermore, we constructed 1600 cold vulnerability models including CDD (Cold Growth Days), simulated LAI (Leaf Area Index) and yields from the CROPGRO-Soybean model. Finally, we extracted the maximum wide dynamic vegetation index (WDRVI) and corresponding date of the critical windows of early and late growing seasons in the GEE (Google Earth Engine) platform, converted the WDRVI into actual LAI of soybean pixel, and calculated the pixel yield and losses according to the corresponding vulnerability models. The findings show that the localized CROPGRO-Soybean model can accurately simulate the growth and development processes of soybean under different cold scenarios. The soybean yields were reduced due to changes in cold stress during the whole growth period (a decrease of 1-3 ℃), which were greater than those from the local cooling treatments (the temperature of 0 ℃ for 5 consecutive days which are randomly generated during four growth periods). Moreover, simulated historical yield losses in 1989, 1995, 2003, and 2009 were 9.6%, 29.8%, 50.5%, and 15.7%, respectively, which were very close (all errors were within one standard deviation) to the actual losses (6.4%, 39.2%, 47.7%, and 13.2%, respectively). The above proposed method was applied to evaluate the yield loss of 2018 at a pixel scale. Specifically, sentinel-2A image was used for 10 m high-precision yield mapping, and the estimated losses well characterized the actual yield losses from 2018 cold event. The results highlighted that our proposed method can efficiently and accurately assess the chilling injury impact on soybean at different spatial scales. The novel method is also effective for efficient assessment of the impacts of different disasters on other crops.

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