基于DSSAT作物模型的中美大豆主产区单产模拟与验证  

Simulations and validations of the soybean yields per unit area using DSSAT crop model in the major soybean producing areas of China and America

作　　者：徐春萌 田芷源 陈威[3,4] 刘佳佳 白洁[1] Xu Chunmeng;Tian Zhiyuan;Chen Wei;Liu Jiajia;Bai Jie(Beijing Jiage Tiandi Technology Company,Ltd.,Beijing 100190,China;Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China;Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,China;Key Laboratory of Agricultural Information Service Technology,Ministry of Agriculture and Rural Affairs,Beijing 100081,China)

机构地区：[1]北京佳格天地科技有限公司,北京100190 [2]中国科学院南京土壤研究所,南京210008 [3]中国农业科学院农业信息研究所,北京100081 [4]农业农村部农业信息服务技术重点实验室,北京100081

出　　处：《农业工程学报》2021年第3期132-139,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：农业农村部农业信息服务技术重点实验室开放基金项目(CAAS-AII NYXXJSFW 2019-001)。

摘　　要：开展基于作物模型的大面积作物产量估测研究,可以为及时掌握全球重点地区农作物的生产情况提供数据支撑。该研究以大豆为监测作物,选取中国吉林省和美国爱荷华州作为研究区域,基于DSSAT作物估产模型中的SOYGRO大豆模型,利用分辨率为0.5°×0.5°的生育期气象要素以及500 m×500 m绿色叶绿素植被指数,进行遥感数据融合作物模型估测大豆单位产量的模拟与验证研究。结果显示,2008-2017年,美国爱荷华州大豆单位产量模拟值的平均误差为16.8%,均方根误差为762.8 kg/hm^(2),平均偏差为107.2 kg/hm^(2);中国吉林省大豆单位产量估测的平均误差为36.3%,均方根误差为1088.4 kg/hm^(2),平均偏差为-237.9 kg/hm^(2)。在县域尺度下,大豆单位产量模拟值与调查值的拟合度较好,尤其在产量较低的年份,其中美国爱荷华州的产量相关系数最高可达0.78,中国吉林省的相关系数偏小,为0.59,表明对美国爱荷华州大豆单位产量的估测精度优于中国吉林省。研究所建立的大豆单位产量估测技术路线,可以为中美两国主产区作物单位产量的大面积有效估测提供参考。Crop models have been widely studied for simulating crop growth and yield production at the field scale.However to upscale simulate crop yield from the field scale to regional scale is a key component for monitoring crop growth over large areas,especially in global main agricultural regions.In this study,a DSSAT-SOYGRO model coupled with remote sensing data was utilized to simulate soybean yield in Jilin Province,China and Iowa State,the U.S.,using the meteorological data during the growth period at 0.5°×0.5°resolution,and Green Chlorophyll Vegetation Index(GCVI)at 500 m×500 m resolution.The specific procedure was listed:1)The parameters of soybean cultivars were determined in these two research regions,according to their historical meteorological data(total rainfall in June–August,mean solar radiation in June–August,and the average daytime maximum temperature in August),recorded growth stages(planted,emerged,blooming,setting pods,dropping leaves,harvested date),application rate of nitrogen fertilizer(50-300 kg/hm^(2)),and yields on site.2)Random simulations were conducted for daily Leaf Area Index(LAI)spanning a range of sites,years,and nitrogen fertilizer application.The generated data was used to train a multi-linear regression model,according to the soybean cultivars,where stored data results in a coefficients table for later use.3)The regression equations were applied to estimate soybean yields during 2008-2017,based on the actual measured data from remote sensing data,with the spatial resolution of 500 m×500 m.The estimated soybean yields were also compared with the survey statistics.The results showed that:in Iowa State,the U.S.,the soybean yields were 2139-4766 kg/hm^(2),showing a larger range than the survey yield with 3002-3991 kg/hm^(2),where the Mean Percentage Error(MPE)was 16.8%,Root Mean Square Error(RMSE)was 762.8 kg/hm^(2),and Mean Bias Error(BSE)was 107.2 kg/hm^(2);whereas,in Jilin Province,China,the soybean yields were 1653-2766 kg/hm^(2),which was closed to the survey yield with 1997-

关 键 词：遥感 作物 模型 大豆 产量 绿色叶绿素植被指数 

分 类 号：S127[农业科学—农业基础科学] TP79[自动化与计算机技术—检测技术与自动化装置]