Wheat yield remote sensing prediction method combining phenological parameters

By collecting canopy hyperspectral reflectance, LAI, and SPAD values ​​during the key growth stages of wheat, and constructing a multivariate model using PCA and PLSR, the problems of indirect spectral-yield relationship and band redundancy in wheat yield remote sensing prediction were solved, improving prediction accuracy and model stability, and supporting real-time and dynamic monitoring of wheat yield.

CN121278367APending Publication Date: 2026-01-06WUXI UNIV
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Patent Information

Application Number
CN202511412375.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-29
Publication Date
2026-01-06

AI Technical Summary

Technical Problem

Existing remote sensing methods for predicting wheat yield suffer from indirectness in the spectrum-yield relationship, band redundancy and overfitting risks, and a lack of physiological mechanism support, resulting in poor extrapolation ability and insufficient stability of the models, making it difficult to meet the needs of modern agriculture for large-scale, dynamic, and real-time monitoring.

Method used

A remote sensing method for wheat yield prediction based on combined phenological parameters was adopted. By simultaneously collecting canopy hyperspectral reflectance data, leaf area index (LAI), and SPAD values ​​during key growth periods, and combining principal component analysis (PCA) and correlation analysis, a multivariate yield prediction model was constructed using partial least squares regression (PLSR). LAI and SPAD were introduced as independent variables to optimize input variables and reduce band redundancy.

Benefits of technology

It significantly improves the accuracy of wheat yield prediction, enhances the physiological basis and stability of the model, reduces band redundancy and overfitting risks, and achieves non-destructive, repeatable dynamic tracking and real-time yield prediction.

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Abstract

The invention discloses a wheat yield remote sensing prediction method combining phenological parameters, which comprises the following steps: S1, performing field observation in a key growth period of winter wheat, synchronously collecting canopy hyperspectral reflectivity data, leaf area index (LAI) and SPAD value of each observation sample point, and recording wheat grain yield of the corresponding sample point; s2, performing noise reduction preprocessing on the acquired canopy hyperspectral data, and extracting sensitive spectral parameters by combining principal component analysis (PCA) and correlation analysis methods; s3, taking the sensitive spectrum parameters, LAI and SPAD values as independent variables, taking the wheat grain yield as a dependent variable, and adopting partial least squares regression (PLSR) to construct a multivariable yield prediction model; and S4, performing wheat yield prediction on an independent test sample or regional scale remote sensing data by using the trained model. The method overcomes the defect that only yield sensitive spectrum parameters are used for predicting the effect, and accurate estimation of the model is achieved.
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