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侯英雨,何亮,靳宁,郑昌玲,刘维,张蕾.中国作物生长模拟监测系统构建及应用[J].农业工程学报,2018,34(21):165-175.DOI:10.11975/j.issn.1002-6819.2018.21.020
中国作物生长模拟监测系统构建及应用
投稿时间:2018-05-08  修订日期:2018-08-10
中文关键词:  模型  气象  遥感  作物长势监测  农业气象灾害  产量预报  同化
基金项目:国家自然科学基金项目(41705095);公益性行业(气象)科研专项(GYHY201506001)资助
作者单位
侯英雨 1. 国家气象中心北京 100081 
何亮 1. 国家气象中心北京 100081 
靳宁 2. 西北农林科技大学黄土高原土壤侵蚀与旱地农业国家重点实验室杨陵 712100 
郑昌玲 1. 国家气象中心北京 100081 
刘维 1. 国家气象中心北京 100081 
张蕾 1. 国家气象中心北京 100081 
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中文摘要:该文系统阐述了中国作物生长模拟监测系统(Crop Growth Simulating and Monitoring System in China, CGMS-China)的构建方法及其在国家级农业气象业务中的应用。CGMS-China是基于WOFOST、Oryza2000、WheatSM、ChinaAgroys 4个作物模型构建的系统,在作物长势监测评估、农业气象灾害影响评估、作物产量预报等农业气象业务中均有应用。该系统可进行作物长势监测、产量预报、农业气象灾害影响评估。利用CGMS-China模拟输出的地上生物量、叶面积指数、穗质量,建立作物长势评估指标,可对小麦、玉米、水稻进行实时长势监测与评估。通过CGMS-China对2014年8月中旬华北黄淮夏玉米的干旱产量损失评估和2016年6月22日早稻高温热害的产量损失预估表明,CGMS-China对农业气象灾害影响评估的效果较好。利用CGMS-China对2014年冬小麦主产省进行产量预报,各省的平均预报相对误差为7%。与此同时,在CGMS-China中利用遥感数据同化方法,对山西洪洞县进行产量预报,预报相对误差小于11%。该系统在国家级农业气象业务中具有良好的应用前景。
Hou Yingyu,He Liang,Jin Ning,Zheng Changling,Liu Wei,Zhang Lei.Establishment and application of crop growth simulating and monitoring system in China[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(21):165-175.DOI:10.11975/j.issn.1002-6819.2018.21.020
Establishment and application of crop growth simulating and monitoring system in China
Author NameAffiliation
Hou Yingyu 1. National Meteorological Center, Beijing 100081, China
 
He Liang 1. National Meteorological Center, Beijing 100081, China
 
Jin Ning 2. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China 
Zheng Changling 1. National Meteorological Center, Beijing 100081, China
 
Liu Wei 1. National Meteorological Center, Beijing 100081, China
 
Zhang Lei 1. National Meteorological Center, Beijing 100081, China
 
Key words:models  meteorology  remote sensing  crop growth monitoring  agro-meteorological disaster  yield prediction  assimilation
Abstract: Agro-meteorological services can provide a strong guarantee for agricultural disaster prevention and reduction, national food security and sustainable development of agriculture. In this paper, we systematically described a Crop Growth Simulating and Monitoring System in China (CGMS-China). The system was established based on 4 crop models, i.e. WOFOST, Oryza2000, WheatSM, ChinaAgrosys. The CGMS-China could be applied to national agro-meteorological services. The CGMS-China includes database layer, model layer and application layer. In the model layer, 4 crop models were integrated by application program interface. They were driven by daily-scale data. The WOFOST model was for winter wheat and maize simulation, the WheatSM was for winter wheat simulation, Oryza2000 was for rice simulation and ChinaAgrosys was for remote sensing data assimilation. The data assimilation method included SCE-UA, particle swarm optimization, and so on. The CGMS-China was used for crop growth monitoring, agro-meteorological disaster assessment and crop yield forecast. The crop growth monitoring was based on leaf are index, aboveground biomass and dry weight of storage organs. The agro-meteorological disaster was estimated based on yield reducing rate. The yield could be predicted by relative yield prediction method based on aboveground biomass or yield in the CGMS-China system. The output of CGMS-China for crop growth monitoring could be used for comparison with those in last 5 years, last year, and normal year. The case study in Tailai, Heilongjiang and Fuxin, Liaoning showed that the CGMS-China was a reliable agro-meteorological service product with good quality for crop growth monitoring, crop yield forecast and yield loss assessments of agro-meteorological disasters. Crop growth assessment index was established using outputs of CGMS-China which included aboveground biomass, leaf area index and weight of storage organs. They were applied to real-time monitoring of wheat, maize and rice growth. The drought assessment was also conducted by the CGMS-China system. The CGMS-China performed well at yield loss assessment of spring maize caused by drought in the middle of August, 2014 and yield loss assessment of early rice caused by heat stress on the 22nd June, 2016. The comparison of real-time monitoring and simulating could well reflect the crop growth during the drought events. The CGMS-China was used to predict winter wheat yield in 2014 in China. The average forecast relative error was 7% and the relative error in most provinces (autonomous region) was less than 10%. In the meantime, application of remote sensing assimilation with crop model was also introduced in this paper. The relative error used CGMS-China combined with remote sensing data assimilation was less than 11% in Hongtong county, Shanxi province, China. Finally, we discussed the future directions of application of crop model in agro-meteorological services. In sum, the CGMS-China can provide services well in crop growth development simulation, meteorological disaster monitoring and yield prediction.
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