您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Integrating UAV and satellite LAI data into a modified DSSAT-rapeseed model to improve yield predictions

文献类型： 外文期刊

作者： Wang, Chufeng 1 ; Ling, Lin 1 ; Kuai, Jie 3 ; Xie, Jing 4 ; Ma, Ni 5 ; You, Liangzhi 1 ; Batchelor, William D. 7 ; Zhang, Jian 1 ;

作者机构： 1.Huazhong Agr Univ, Macro Agr Res Inst, Coll Resource & Environm, 1 Shizishan St, Wuhan 430070, Peoples R China

2.Minist Agr, Key Lab Farmland Conservat Middle & Lower Reaches, Wuhan 430070, Peoples R China

3.Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China

4.Huazhong Agr Univ, Coll Sci, Wuhan 430070, Peoples R China

5.Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430062, Peoples R China

6.Int Food Policy Res Inst, Washington, DC USA

7.Auburn Univ, Biosyst Engn Dept, Auburn, AL 36849 USA

8.Minist Agr & Rural Affairs, Key Lab Biol & Genet Improvement Oil Crops, Wuhan 430062, Peoples R China

9.Minist Agr & Rural Affairs, Key Lab Crop Physiol & Prod, Wuhan 430062, Peoples R China

关键词： Unmanned aerial vehicle remote sensing; Satellite image; Leaf area index; Assimilation; Actual farmland; DSSAT

期刊名称：FIELD CROPS RESEARCH （ 影响因子：6.4； 五年影响因子：6.6 ）

ISSN： 0378-4290

年卷期： 2025 年 327 卷

页码：

收录情况： SCI

摘要： Context: Yield estimation in the fall is crucial for effective pre-winter management of winter rapeseed. Integrating remotely sensed leaf area index (LAI) with crop models has great potential for improving simulations of crop yields. Objective: The objective of this study was to modify the DSSAT-Rapeseed model and by integrating LAI adjustments from satellite and unmanned aerial vehicle (UAV) images to improve the accuracy of rapeseed yield predictions at early stages from both experimental plots and actual farm fields. Methods: A new pest definition, called "target LAI," was created in the COGRO048.PST file within the pest module of DSSAT. The DSSAT model was then modified to adjust leaf weight, leaf area, and leaf nitrogen content based on remotely sensed target LAI. Field investigations and UAV-derived LAI data from two years and two experimental stations were used to calibrate model parameters through a trial-and-error method, selecting the parameter set that minimized the error between model outputs (e.g., LAI and crop yield) and observations. The model's performance was tested with yield data from a different year at the same stations, using pre-winter LAI assimilated through the Ensemble Kalman Filter (EnKF). For actual farm fields, dynamic LAI data from Sentinel2A was integrated with the modified DSSAT model for yield simulation and compared with ground measurements. Results: By assimilating LAI into the modified DSSAT model, the mean absolute error (MAE) for yield simulation was reduced from 452 to 234 kg/ha in the experimental plot and from 443 to 259 kg/ha in actual farm fields compared to the original DSSAT model. Conclusions: Integrating UAV and satellite LAI during pre-winter into the modified DSSAT model using data assimilation (EnKF) improved the rapeseed yield prediction.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台