Crop yield remote sensing estimating method and system

Abstract

The invention discloses a crop yield remote sensing estimating method and system. The method comprises the steps of adopting a crop growth model to conduct crop growth process simulation, for example, particularly choosing a novel crop growth model AquaCrop with less input parameters and initial conditions to conduct the crop growth process simulation, adopting a canopy covering degree as a link parameter between the crop growth model and multi-source multi-time remote sensing observation data, thus achieving a biological mechanism and the multi-source multi-time remote sensing observation data of comprehensive crop growth, and adopting the canopy covering degree as a crop yield remote sensing estimating scheme of the link parameter. Compared with quantitative inversion of a leaf area index or biomass, quantitative inversion of the canopy covering degree based on remote sensing observation data is more direct and more efficient, and has higher precision and universality, and thus the crop yield remote sensing estimating method provides basic theoretical guidance and technological method support for timely, fast, high-precision and high-universality remote sensing quantitative estimation of an area crop yield.

Description

technical field [0001] The invention belongs to the field of crop yield estimation in precision agriculture, and in particular relates to a crop yield remote sensing estimation method and system. Background technique [0002] Crop yield estimation has important theoretical research significance and practical application value for the formulation and implementation of field management strategies, food quality and safety assessment, and loss assessment of diseases, insect pests and meteorological disasters. It is a research hotspot in the field of precision agriculture. Compared with time-consuming and laborious ground sampling, remote sensing observation with temporal and spatial continuity can obtain crop observation data in a timely, accurate and large-scale manner, thus providing scientific data support for crop yield estimation. [0003] Crop yield remote sensing estimation methods are mainly divided into statistical and mechanism methods. Mechanism methods start from the...

AI Technical Summary

Problems solved by technology

In terms of link parameters between crop growth models and remote sensing observation data, the first two types of models usually use leaf area index as a link parameter, and the accuracy of remote sensing retrieval of leaf area index affects the subsequent crop yield estimation accuracy based on crop growth models; the third AquaCrop model (AquaCrop model) usually uses biomass as a link parameter, and the remote sensing retrieval accuracy of biomass affects the subsequent crop yield estimation accuracy based on crop growth models. However, by using remote sensing observation data to quantitatively retrieve leaf area index or biomass The yield estimation methods all have the defects of insufficient yield estimation efficiency and accuracy. Therefore, crop growth models still need to be further improved in terms of crop yield estimation efficiency and accuracy, so as to meet the realistic application requirements of crop remote sensing yield estimation for high efficiency and high precision.

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