[1]毕淼,詹培,何永坤,等.基于遥感数据与作物模型结合的重庆市水稻估产方法[J].江苏农业学报,2025,(05):893-904.[doi:doi:10.3969/j.issn.1000-4440.2025.05.008]
　BI Miao,ZHAN Pei,HE Yongkun,et al.Rice yield estimation method in Chongqing based on remote sensing data and crop model[J].,2025,(05):893-904.[doi:doi:10.3969/j.issn.1000-4440.2025.05.008]
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基于遥感数据与作物模型结合的重庆市水稻估产方法()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年05期
页码:
893-904
栏目:
农业信息工程
出版日期:
2025-05-31

文章信息/Info

Title:
Rice yield estimation method in Chongqing based on remote sensing data and crop model
作者:
毕淼1; 詹培1; 2; 何永坤1; 范莉1; 张建平1
(1.中国气象局气候资源经济转化重点开放实验室/重庆市气象科学研究所,重庆401147；2.南京信息工程大学生态与应用气象学院,江苏南京210044)
Author(s):
BI Miao1; ZHAN Pei1; 2; HE Yongkun1; FAN Li1; ZHANG Jianping1
(1.China Meteorological Administration Key Open Laboratory of Transforming Climate Resources to Economy/Chongqing Institute of Meteorological Sciences, Chongqing 401147, China；2.School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China)
关键词:
水稻; 作物模型; 遥感估产; 数据耦合
Keywords:
rice; crop models; remote sensing yield estimation; data combination
分类号:
S511;S127
DOI:
doi:10.3969/j.issn.1000-4440.2025.05.008
文献标志码:
A
摘要:
水稻是世界主要粮食作物,准确预测其产量对粮食安全和农业资源管理具有重要意义。本研究采用ORYZA(V3)作物模型和MODIS遥感数据,建立了遥感数据与作物模型结合的重庆市水稻估产模型。主要结论如下:使用水稻分期播种试验数据对模型中的作物参数进行校正,校正结果显示,模型对生育期的模拟误差低于5%,地上总生物量(WAGT)和穗生物量(WSO)模拟值与实测值之间的决定系数（R2）均超过0.970,归一化均方根误差(nRMSE)低于22.0%,提高了模型在重庆地区的适用性；通过多参数组合下的叶面积指数（LAI）与水稻单产的回归分析,建立了在最优结合日期(日序182，7月1日)下的LAI与水稻单产的回归模型,据此完成对全市2023年水稻单产估算,平均准确率达到87%,总体效果较好,尤其是对重庆市西部、中部、东南部等水稻主产区的预测精度更高。研究结果证实,将作物模型与遥感数据相结合,能够有效提升区域农作物产量估算的精度,在作物的产量预测领域展现出巨大的应用前景。
Abstract:
Rice is a primary food crop globally, and accurate prediction of its yield is of great significance for food security and agricultural resource management. This study used the ORYZA(V3) crop model and MODIS remote sensing data to establish a rice yield estimation model for Chongqing. The main conclusions were as follows: the crop parameters in the model were calibrated using rice staged sowing experiment data. The calibration results showed that the simulation error of the model for the growth period was less than 5%, and the determination coefficients (R2) between the simulated and measured values of total aboveground biomass (WAGT) and panicle biomass (WSO) exceeded 0.970. The normalized root mean square error (nRMSE) was less than 22.0%, which improved the applicability of the model in Chongqing. Through regression analysis of LAI and rice yield under multiple parameter combinations of the model, a regression model of LAI and rice yield under the optimal combination date (182nd day of the year, July 1) was established. Based on this, the estimation of rice yield in Chongqing in 2023 was completed with an average accuracy of 87%, and the overall effect was good, especially in the main rice producing areas such as the western, central, and southeastern regions, where the accuracy was higher. The research results confirm that combining crop models with remote sensing data can effectively improve the accuracy of regional crop yield estimation and show great application prospects in the field of crop yield prediction.

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备注/Memo

备注/Memo:
收稿日期：2024-08-17基金项目：国家自然科学基金面上项目(42175193)；中国气象局创新发展专项(CXFZ2023P016)；重庆市气象局青年基金项目(QNJJ202306)作者简介：毕淼(1996-),女,重庆万州人,硕士研究生,主要从事作物模型模拟研究。(E-mail)miaobi712@163.com通讯作者：詹培,(E-mail)peizhan@nuist.edu.cn
更新日期/Last Update: 2025-06-24