Regional crop yield estimation method based on set assimilation strategy

Abstract

The invention discloses a regional crop yield estimation method based on a set assimilation strategy. The method comprises the steps of obtaining remote sensing LAI data of a prediction year Y and previous M years in a research region; for the (Y-1) th year, carrying out difference assimilation to obtain a plurality of grid scale simulation yields, a plurality of corresponding groups of correlation coefficients r and a root-mean-square error rmse; calculating a fitting index F according to r and rmse, and obtaining a plurality of fitting indexes in total; selecting a maximum fitting index as an optimal result, and recording a corresponding assimilation weight as an optimal assimilation weight Hop _ y-1 of the (y-1) th year; s3, repeating the steps S2 to S5 for y-2, y-3-y-M years to obtainoptimal assimilation weights Hop _ y-2, Hop _ y-3-Hop _ y-M of each corresponding year, and calculating the optimal assimilation weights Hop _ y-3,-Hop _ y-M of each corresponding year; and expandingan assimilation weight value interval Hlow-Hup, and then performing simulation yield estimation on the prediction year Y by utilizing the assimilation weight value interval Hlow-Hup.

Description

technical field [0001] The invention relates to the technical field of agricultural remote sensing, in particular to a regional crop yield estimation method based on a collective assimilation strategy. Background technique [0002] Crop models often face the problem of insufficient input data when estimating crop yield at the regional scale. Surface characteristics, near-surface environment, and crop management measures often have obvious spatial differences, and some necessary model input data, such as crop phenology data, meteorological data, and crop management information, are all recorded at the station scale, which leads to crop models being applied to At the regional scale, it is difficult to obtain enough data to represent the spatial heterogeneity of key factors such as initial conditions, crop parameters, and growth processes. Satellite remote sensing data provide continuous monitoring data of large-scale surface information, which can reflect the spatial continui...

AI Technical Summary

Problems solved by technology

In fact, due to the many factors affecting the assimilation weight and the inter-annual variation is complex, the optimal assimilation weight cannot be directly deduced through historical experience, which greatly reduces the practicability of the assimilation technology

At present, the inability to know the optimal assimilation weight a priori is a major limitation for the practical application of the assimilation yield estimation technology. How to break through this limitation, carry out assimilation yield estimation under the premise of unknown optimal assimilation weight and obtain yield estimation results with sufficient accuracy is still a challenge. Technical gaps, urgent need to break through

Images