A rice yield estimation method integrated with remote sensing and meteorology

Abstract

The invention relates to a method for estimating rice production with remote sensing and meteorological integration, comprising: inputting remote sensing image data of crops planted in a target area; using a maximum likelihood supervised classification algorithm to obtain classification information of crops planted in a required area; estimating the rice in the target area Planting area; use the meteorological monitoring data and FPAR photosynthetic effective radiation coefficient in the selected time period of the target area over the years to analyze the results of rice growth over the years; use remote sensing image data to generate the rice growth process curve of the year, and obtain the analysis results of the rice growth in the target area; According to the meteorological monitoring data of the target area, the disaster loss of the target area is evaluated and the disaster types are distinguished; the rice yield estimation model is established, and the total rice output of the target area is calculated. The invention can realize high-precision yield estimation of bulk crops and improve the accuracy of yield estimation results, so as to meet the professional requirements of agricultural production management, agricultural insurance, futures and other industrial applications.

Description

technical field [0001] 本发明属于农业遥感技术领域,涉及一种遥感及气象一体化水稻估产方法。 Background technique [0002] 从古至今,如何为我国粮食安全及农产品贸易提供及时准确的作物长势、灾害损失定量评估、产量预估等信息,一直是国家生存与发展的重大问题。而今,上至国家决策层面,下至农业保险、期货市场及农户,在农作物生长过程中都希望尽可能精确估计作物产量。而传统的农作物估产主要采用农学模式和气象模式,进行人工区域调查,速度慢、工作量大、成本高,且不利于范围农作物的时空动态监测。 [0003] 随着遥感技术的发展,农业遥感估产以及进入了一个全新的时期,农作物遥感估产建立了作物光谱和产量之间的关系,利用光谱来获取作物的生长信息。农业部、国家气象局、中国科学院均对农作物遥感估产技术开展了大范围的研究和应用。但是,农作物产量的形成受气象、土壤、水分等多种因素的影响,因此目前采用单一的卫星遥感数据或气象卫星数据的监测精度只能到70%-80%,误差来自于遥感数据本身的误差、数据解译过程误差等,单一遥感手段都都不足以满足高精度、专业化的农作物估产应用需求。因此需要通过多源遥感信息融合和相互验证来提高农作物遥感估产的精度。 Contents of the invention [0004] 发明所要解决的课题是,如何利用气象遥感监测手段提高农作物遥感估产的精度,实现高精度的大宗农作物估产。 [0005] 用于解决课题的技术手段是,本发明一种遥感及气象一体化水稻估产方法,包括以下步骤: [0006] 步骤1、输入目标区域内农作物种植的遥感影像数据; [0007] 步骤2、根据不同地物及各农作物之间的光谱特征差异,利用最大似然监督分类算法获取所需区域的农作物种植分类信息; [0008] 步骤3、根据农作物种植分类信息分类别统计像元个数,及根据遥感影像数据中的空间分辨率计算单个像元面积,并相乘估算出目标区域内水稻种植面积; [0009] 步骤4、利用目标区域历年选取时间段内的气象监测数据和FPAR光合作用有效辐射系数,进行历年水稻长势分析结果; [0010] 步骤5、以遥感影像数据生成当年水稻生长过程曲线,通过比较当年与历年间的水稻长势差异,基于历年水稻长势分析结果利用ND...

AI Technical Summary

Problems solved by technology

However, the formation of crop yields is affected by various factors such as weather, soil, and moisture. Therefore, the monitoring accuracy of a single satellite remote sensing data or meteorological satellite data can only reach 70%-80%, and the error comes from the remote sensing data itself. errors, data interpretation process errors, etc., a single remote sensing method is not enough to meet the application requirements of high-precision and specialized crop yield estimation

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