基于数据同化系统的作物产量预测研究进展
Research progress on crop yield prediction based on data assimilation system
赵钰 1杨武德 2段丹丹 3冯美臣 2王超2
作者信息
- 1. 山西农业大学农学院,山西 晋中 030801;北京市农林科学院信息技术研究中心,北京 100097
- 2. 山西农业大学农学院,山西 晋中 030801
- 3. 北京市农林科学院信息技术研究中心,北京 100097
- 折叠
摘要
数据同化系统融合了遥感数据和作物生长模型的优势,是实时监测农业生产状况的有力手段.本文在简要介绍作物产量遥感估测方法的基础上,重点对数据同化算法的发展情况、多源遥感数据在数据同化上的应用潜力、数据同化系统的不确定性以及数据同化系统的尺度效应4方面进行论述.并且针对农业应用现状,提出未来应充分挖掘多源遥感数据、多作物生长模型集合和数据算法的优势,最终实现以机理模型为纽带的作物估产模式,并为制定田间管理策略、规划粮食产业布局和制定进出口贸易政策提供有力的数据和技术支撑.
Abstract
Data assimilation system integrates the advantages of remote sensing data and crop growth models,providing a powerful means for real-time monitoring of agricultural production conditions.This paper,built upon a brief introduction to remote sensing methods for crop yield estimation,specifically focused on four aspects:the development of data assimilation algorithms,the application potential of multi-source remote sensing data for data assimilation,the uncertainty of data assimilation system,and the scale effects of data assimilation system.In addition,to address the current state of agricultural applications,future efforts should thoroughly explore the advantages of multi-source remote sensing data,multi-crop growth model ensembles,and data algorithms.The ultimate goal is to establish a crop yield estimation model centered around mechanism model,thereby providing robust data and technical support for the formulation of sound field management strategies,the planning of cereal industry layouts,and the establishment of import and export trade policies.
关键词
产量/数据同化系统/多作物生长模型集合/多源遥感数据Key words
yield/data assimilation system/multi-crop growth model ensembles/multi-source remote sensing data引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据