4.7 Article

Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm

期刊

REMOTE SENSING OF ENVIRONMENT
卷 255, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.112276

关键词

Data assimilation; Crop growth model; Yield simulation; Four-dimensional expansion; Variable time window

资金

  1. National Natural Science Foundation of China [41921001, 41871353, 41801286, 41871358, 61661136006]
  2. Young Elite Scientists Sponsorship Program by CAST [2018CAASS04]
  3. Open Project Fund for Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs [201708]
  4. Fundamental Research Funds for Central Nonprofit Scientific Institution [1610132019026]
  5. Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS)
  6. Outstanding Talents and Innovative Team of Agricultural Scientific Research, Ministry of Agriculture and Rural Affairs
  7. STFC [ST/N006798/1, ST/V001388/1] Funding Source: UKRI

向作者/读者索取更多资源

A novel EnSRF assimilation algorithm was proposed to improve the accuracy of regional crop yield estimation. The algorithm, combined with the WOFOST crop model and remotely sensed data, effectively simulated winter wheat yield and selected the optimal grid size for regional estimation. The system demonstrated good performance at both single-point and regional levels, proving the feasibility and effectiveness of the proposed algorithm in simulating crop yield over a large area.
To further improve the accuracy of regional crop yield estimation based on data assimilation, a novel EnSRF assimilation algorithm based on a variable time window and four-dimensional extension (VW-4DEnSRF) was proposed. In this research, taking Hengshui City of Hebei Province as the study area and winter wheat as the research crop, based on the WOFOST crop model and the proposed VW-4DEnSRF algorithm, a crop yield assimilation system was successfully constructed after parameter sensitivity analysis and parameter calibration of the crop model. Supported by the field-measured crop yield data and based on the effective validation of the yield assimilation system at a single point scale and in a typical experimental area, the scale optimization of grid size for regional yield estimation was effectively selected. Finally, combining the WOFOST model and inverted remotely sensed LAI, the regional winter wheat yield simulation under the optimal grid size of 500 m was carried out effectively through comparison with the field-measured yield data and official statistical yield data at the county level. Among them, the R-2, adjusted R-2 and RMSE between the simulated yield and ground-measured yield were 0.481, 0.471 and 801.4 kg.ha(-1), respectively. The mean value of the estimated yield of winter wheat in Hengshui City was 6787 kg.ha(-1), and the RMSE and RE between the estimated yield and official yield were 416.7 kg.ha(-1) and 4.56%, respectively. These above results showed that the crop yield assimilation system based on the WOFOST model and proposed VW-4DEnSRF algorithm had good performances at both the single-point level and regional level, which proved that the proposed algorithm was feasible and effective at simulating crop yield over a large area

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据