Yield Forecasting Method of Provincial-level Block-scale Data Assimilation Based on Ensemble Sampling

Abstract

The invention belongs to the field of agricultural remote sensing, and relates to a provincial-level block-scale rapid data assimilation yield prediction method based on collective sampling, specifically: obtaining a provincial-level crop spatial distribution map based on time-series remote sensing data and crop sample points; LAI and yield calibrate the WOFOST model, obtain the posterior sample set of key parameters, input it and the meteorological data of the entire growth period into the WOFOST model, and generate the LAI trajectory set and yield set during the growth period corresponding to the station; based on the PROSAIL model, the reflection The rate data was inverted into LAI, and the range of LAI trajectory during the growth period was obtained; according to the range of LAI trajectory, the yield corresponding to each LAI trajectory was inversely weighted, and the yield obtained by weighted sum was the yield of rapid assimilation. The method of the invention can predict the yield of crops in a large area at the provincial level with a high-resolution block scale of 10 meters, and the assimilation speed is fast and the efficiency is high.

Description

technical field [0001] The invention belongs to the field of agricultural remote sensing, and in particular relates to a provincial-level block-scale rapid data assimilation yield prediction method based on collective sampling. Background technique [0002] Traditional crop yield estimation methods mainly include statistical survey methods, crop model-based forecast methods, and agrometeorological forecast methods. These methods are difficult to achieve high-precision estimation of regional crop yield due to their inherent limitations. The estimation method based on satellite remote sensing technology has unique advantages in regional crop yield estimation by virtue of its spatial continuity and temporal dynamic characteristics. At the same time, the combination of remote sensing technology and crop growth models based on crop photosynthesis, respiration, transpiration, nutrition and other mechanism processes can achieve the purpose of regional high-precision yield estimati...

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