Provincial-level range plot scale rapid data assimilation yield prediction method based on set sampling

Abstract

The invention belongs to the field of agricultural remote sensing, and relates to a provincial-level range plot scale rapid data assimilation yield prediction method based on set sampling, which specifically comprises the steps of obtaining a provincial-level crop spatial distribution map based on remote sensing data of a time sequence and crop sample points; obtaining a posterior sample set of the key parameters based on a site LAI and yield calibration WOFOST model, inputting the posterior sample set of the key parameters and the meteorological data of the whole growth period into the WOFOSTmodel, and generating an LAI trajectory set and a per unit yield set in the growth period corresponding to the site; inverting the reflectivity data into LAI based on a PROSAIL model, and obtaining an LAI track range in a growth period; and according to the LAI track range, carrying out inverse distance weighting on the unit yield corresponding to each obtained LAI track, wherein the yield obtained by weighted summation is the unit yield of rapid assimilation. According to the method, large-area crop unit yield prediction in a provincial-level range can be carried out at a high-resolution land parcel scale of 10 meters, the assimilation speed is high, 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...

AI Technical Summary

Problems solved by technology

The current traditional grid-by-grid assimilation strategy is difficult to meet the actual needs of real-time yield forecasting

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