A crop disaster loss rapid assessment method

Abstract

The invention provides a crop disaster loss rapid assessment method, which is based on a crop model of a DSSAT system and a GLUE parameter estimation tool, utilizes historical data of crops in a disaster area, obtains leaf area indexes and yields of the crops under various scenes based on crop model simulation, and further constructs a regression equation. And then based on a satellite image of Google Enth (GEE), substituting the leaf area index of each pixel on the satellite image into a linear regression equation to obtain the yield of each pixel. And finally, comparing the yield of the disaster year of the disaster area with the yield of the last year to obtain the relative yield loss. According to the method, a large amount of ground observation data is not needed, only a small amountof experimental data is needed for calibration, the evaluation efficiency is improved, the time cost of disaster loss evaluation is reduced, and a guarantee is provided for disaster prevention and reduction. According to the method, quantitative assessment of disaster loss is achieved, the yield reduction rates of different spatial scales can be reliably estimated, and the quality of disaster assessment is improved.

Description

technical field [0001] The invention relates to the technical field of agricultural remote sensing, in particular to a cross-scale and quantitative evaluation method for crop disaster losses based on a remote sensing data processing platform Google EarthEngine (GEE) and a crop model, in particular to a method for quickly evaluating crop disaster losses. Background technique [0002] Agriculture and weather are closely related. The whole process of crop growth is in the natural environment, and the formation of its yield is extremely vulnerable to the coercion and interference of unfavorable factors such as meteorological disasters. Severe disasters can lead to a sharp decline in grain production or even extinction. At present, the assessment of agricultural meteorological disasters is mainly divided into two categories: risk assessment and impact assessment. Risk means the possibility of disaster occurrence and damage. The technical basis of assessment is risk analysis tech...

AI Technical Summary

Problems solved by technology

This method is based on the assumption that crop yield reduction is caused by chilling damage, and the evaluation effect on single disaster loss is poor, and dynamic evaluation cannot be performed.

2) Based on the historical observation data, construct the regression equation of disaster index and output, and extrapolate the disaster loss, but the disaster index is very regional, it is difficult to expand and apply to other regions, and it cannot meet the loss assessment below the county level

The combination of remote sensing and crop models can realize continuous yield simulation at different spatial resolutions, solve the regional scale application problem of station crop models, and has been widely used, but there are two key problems as follows: 1) Although the statistical model established by remote sensing index Simple and easy to use, but the statistical model based on single-phase vegetation index and measured yield is limited to a specific time, place and year

2) Although the assimilation of remote sensing data damage estimation based on crop model has achieved certain results, the model is not easy to popularize due to the large amount of input data and complex calculation process, and the data collection is very complicated and the operation efficiency is low

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