DOI: 10.57760/sciencedb.agriculture.00026
title: A 10-m Fractional Vegetation Cover Monthly Dataset of the Kherlen River Basin in 2022
title: 2022年克鲁伦河流域10米分辨率植被覆盖度月度数据集

description: 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. Conventional vegetation cover inversion methods, such as the pixel-dichotomization method, linear regression method, and random forest regression model, always stumble in discerning subtle spectral disparities and overlook the intricate nonlinear associations among high-dimensional features. Due to this, The BiLSTM model, rooted in deep learning techniques, endeavors to accurately generate the vegetation cover in this watershed. Firstly, a comprehensive feature dataset is assembled using Sentinel-2 data, integrated with spectral indices and elevation data. This feature dataset effectively captures a range of vegetation characteristics such as chlorophyll content, water status, and terrain attributes. Subsequently, this feature dataset undergoes division into training and testing subsets to comprehensively assess the performance of four models: BiLSTM, Random Forest Regression, Multilayer Perceptron, and LSTM. Then, the BiLSTM model achieved the best performance with an R2 and RMSE of 0.716 and 0.103, respectively. It is then utilized to generate a monthly vegetation cover dataset for 2022 for the Kherlen River Basin. This dataset consists of 12 months of vegetation cover inversion results for the Kherlen River Basin, and the data have been completed with operations such as merging and mask extraction. Moreover, it is a valuable resource for monitoring surface vegetation growth and assessing ecosystem vitality in the Kherlen River Basin, thereby furnishing essential data support for ecological studies within the watershed.
description: 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. Conventional vegetation cover inversion methods, such as the pixel-dichotomization method, linear regression method, and random forest regression model, always stumble in discerning subtle spectral disparities and overlook the intricate nonlinear associations among high-dimensional features. Due to this, The BiLSTM model, rooted in deep learning techniques, endeavors to accurately generate the vegetation cover in this watershed. Firstly, a comprehensive feature dataset is assembled using Sentinel-2 data, integrated with spectral indices and elevation data. This feature dataset effectively captures a range of vegetation characteristics such as chlorophyll content, water status, and terrain attributes. Subsequently, this feature dataset undergoes division into training and testing subsets to comprehensively assess the performance of four models: BiLSTM, Random Forest Regression, Multilayer Perceptron, and LSTM. Then, the BiLSTM model achieved the best performance with an R2 and RMSE of 0.716 and 0.103, respectively. It is then utilized to generate a monthly vegetation cover dataset for 2022 for the Kherlen River Basin. This dataset consists of 12 months of vegetation cover inversion results for the Kherlen River Basin, and the data have been completed with operations such as merging and mask extraction. Moreover, it is a valuable resource for monitoring surface vegetation growth and assessing ecosystem vitality in the Kherlen River Basin, thereby furnishing essential data support for ecological studies within the watershed.