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Rapid assessment of maize chilling damage based on GEE

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Environmental Change and Natural Disaster, MOE, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：zhangliangliang@mail.bnu.edu.cn
  • Introduction：张亮亮，1997年生，女，博士研究生，研究方向为农业灾害。E-mail: zhangliangliang@mail.bnu.edu.cn
ZHANG Liangliang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Environmental Change and Natural Disaster, MOE, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：zhangzhao@bnu.edu.cn
  • Introduction：张朝，1971年生，女，教授，研究方向为农业灾害和国家粮食安全。E-mail: zhangzhao@bnu.edu.cn
ZHANG Zhao1*,  
  • Affiliation：

    Key Laboratory of Environmental Change and Natural Disaster, MOE, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

CAO Juan1,  
  • Affiliation：

    Key Laboratory of Environmental Change and Natural Disaster, MOE, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

LI Ziyue1,  
  • Affiliation：

    Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

TAO Fulu2

résumé

Extensive, timely, and accurate mapping of yield losses is critical and prerequisite in disaster prevention and reduction, agricultural insurance, and food security. Given the coarse resolution, poor generalization ability, low timeliness, and weak operability of traditional loss assessment method, we propose a new approach called Multiscale Disaster Loss Assessment (MDLA) by coupling crop model with remote sensing to assess yield loss rapidly with satellite images. A series of disaster scenarios was simulated using a calibrated crop model. Related results, including final yield and crop growing state variable LAI, were inputted into disaster datasets. A susceptibility model of disaster was then constructed. Finally, pixel-by-pixel yield loss was evaluated on the basis of the susceptibility model combined with high-resolution image with gridded disaster indices within the Google Earth Engine (GEE) platform.The new method was used to assess the impacts of chilling injury on maize by applying carefully calibrated CERES-Maize in Oroqen, Inner Mongolia Autonomous Region. We constructed the cold susceptibility model, which properly characterized the cold damage on maize yield, including three independent variables, LAI in two growing season windows and a cold index (cold degree days), and yield loss. We further mapped pixel-based maize yield losses together with Sentinel-2 data. Mapping results showed that CERES-Maize, once calibrated, can appropriately simulate the growth and development state of maize under various management and weather conditions with a phenology bias of < 3.3% and yield NRMSE of < 8.9%. Furthermore, impacts of chilling injury varies in cold type and occurring time due to the high susceptibility of maize at the peak growing period (emergence-silking and silking-graining filling). The MDLA method successfully estimated significant losses during cold years with an accuracy of 11.4%. Moreover, the recent cold event (occurred at 2018/08/09) reduced the maize yield by 23.7% and affected 1.86 × 104 ha of growing areas. The occurrence of more than 25% yield loss in high-altitude regions indicated that low temperature is a major threat on crop production in northeastern China.Our results indicated that MDLA is consistent with statistical regression, crop model simulation, and assimilation technology. Moreover, the advantages of MDLA are presented as follows: (1) The impact of disaster is appropriately characterized by combining remote sensing observation with simulated physiological states in crop models. (2) Processing the satellite image within the GEE platform significantly reduces the computing time of loss assessment. (3) Multiscale losses are mapped in a dynamic and operable way. This type of mapping can be performed not only in large-scale areas but also the county- or even field-scale regions. Our study can help decision-makers in reasonably preventing agricultural disasters and maintaining steady grain production while providing a more practical means for operational agricultural insurance.

mots-clés

remote sensing;Multiscale Disaster Loss Assessment (MDLA);Google Earth Engine（GEE）;crop model;chilling injury index;maize

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