农业大数据学报2025，Vol.7Issue(1)：59-68,10.DOI:10.19788/j.issn.2096-6369.100032
2022年克鲁伦河流域10米分辨率植被覆盖度月度数据集
A 10-m Fractional Vegetation Cover Monthly Dataset of the Kherlen River Basin in 2022
摘要
Abstract
Precisely obtaining the Fractional Vegetable Cover(FVC)at the river basin scale is of immense importance for delving into the ecological environment,wetland health,and ecological conservation strategies within watersheds.The Kherlen River Basin is an important ecological area across the border between China and Mongolia.It has high biodiversity and is essential for supporting and maintaining the balance of ecosystems in the region.Thus,this dataset focuses on the Kherlen River Basin,leveraging Sentinel-2 multispectral remote sensing imagery with a spatial resolution of 10 m to derive FVC with high precision.The dataset provides vegetable cover data to support the ecological protection of the Kherlen River Basin.In order to overcome the problem,traditional vegetation coverage inversion methods,such as pixel dichotomy,linear regression,and random forest regression models,could be more effective in mining subtle differences between spectral features and finding complex nonlinear relationships between high-dimensional features.To estimate the vegetation coverage more accurately in the watershed,this paper compares the performance of four models:the Bidirectional Long Short-Term Memory(BiLSTM)model based on deep learning,Random Forest Regression,Multilayer Perceptron,and LSTM,to determine the optimal data processing method.The feature data used are based on Sentinel-2 multispectral data,integrating spectral indices and elevation data.The vegetation-related information reflected includes chlorophyll content,moisture status,and topography.This feature dataset is further divided into training and testing sets.The comparison results show that BiLSTM achieved an R2 of 0.716 and an RMSE of 0.103,indicating the best overall performance.This model generated a monthly vegetation coverage dataset for the Kherlen River Basin in 2022,comprising vegetation coverage inversion results for 12 months.All data have undergone operations such as mosaicking and mask extraction.This dataset can assess the vegetation growth status and ecosystem health of the Kherlen River Basin and support ecological protection research in related watersheds.关键词
克鲁伦河流域/机器学习/深度学习/BiLSTM/植被覆盖度Key words
Kherlen River Basin/machine learning/deep learning/BiLSTM/fractional vegetable cover引用本文复制引用
牛博文,冯权泷,张毓,高秉博,SUKHBAATAR Chinzorig,冯爱萍,杨建宇..2022年克鲁伦河流域10米分辨率植被覆盖度月度数据集[J].农业大数据学报,2025,7(1):59-68,10.基金项目
国家重点研发计划项目克鲁伦河流域面源污染遥感监测与评估技术研发2021YFE0102300). （）
