A method for reconstructing snow-covered area in mountainous regions based on machine learning

By combining high temporal resolution optical remote sensing data and topographic data with the XGBoost model based on machine learning, the problem of high spatiotemporal resolution in monitoring snow cover area in mountainous areas was solved, and snow cover area reconstruction with a high resolution of 30 meters was achieved, which met the needs of hydrological models.

CN120125641BActive Publication Date: 2026-04-07NORTHWEST INST OF ECO ENVIRONMENT & RESOURCES CAS
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Patent Information

Application Number
CN202510178291.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-02-18
Publication Date
2026-04-07
Estimated Expiration
2045-02-18

AI Technical Summary

Technical Problem

Existing technologies are insufficient to achieve high spatiotemporal resolution snow cover area monitoring in mountainous areas, and traditional methods cannot meet the spatial details of snow cover distribution and the time series continuity requirements of hydrological simulation in mountainous areas.

Method used

A machine learning-based approach was adopted, using the XGBoost model combined with high temporal resolution optical remote sensing data and terrain data. A sample dataset was constructed through terrain correction and snow cover recognition algorithms, and the model was trained to reconstruct the 30-meter high-resolution snow cover area.

Benefits of technology

It has enabled efficient and accurate monitoring of snow cover area in mountainous areas, improved the accuracy and spatiotemporal resolution of snow cover area data, and met the needs of hydrological models.

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Abstract

This invention discloses a machine learning-based method for reconstructing snow cover area in mountainous areas, comprising: acquiring and preprocessing optical remote sensing data and topographic data; the optical remote sensing data includes MOD09GA data and Landsat8OLI data; the preprocessing includes topographic correction and snow cover identification; using the snow cover area generated from the Landsat8OLI data using a snow cover identification algorithm as the ground truth label, and constructing a sample dataset based on the preprocessed MOD09GA data and topographic data; dividing the sample dataset into a training set and a validation set at a 4:1 ratio to train and validate an XGBoost model; the XGBoost model improves prediction performance by combining multiple decision trees; and using the trained XGBoost model to predict snow cover area on new input data. This method can generate daily 30-meter (m) snow cover area data, which has important research significance and value for mountainous snow cover monitoring, hydrological process simulation, and ecosystem evolution.
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