2022年克鲁伦河流域10米分辨率植被覆盖度月度数据集

doi:10.19788/j.issn.2096-6369.100032

Abstract

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.

Data summary:

Item	Description
Dataset name	A 10-m Fractional Vegetation Cover Monthly Dataset of the Kherlen River Basin in 2022
Specific subject area	Land resources and information technology
Research topic	Fractional Vegetation Cover
Time range	2022
Geographical scope	Kherlen River Basin，Mengolia
Spatial resolution	10 m
Data types and technical formats	.tif
Dataset structure	The dataset contains 12 images; the content is monthly images of vegetation cover data in the Kherlen River Basin for the year 2022.
Volume of dataset	148 GB
Key index in dataset	Fractional Vegetable Cover Index
Data accessibility	https://cstr.cn/17058.11.sciencedb.agriculture.00026 https://doi.org/10.57760/sciencedb.agriculture.00026
Financial support	Research and development on remote sensing monitoring and assessment technology of non-point source pollution in Kherlen River Basin under the National Key Research and Development Program (2021YFE0102300)

Key words: Kherlen River Basin, machine learning, deep learning, BiLSTM, fractional vegetable cover

NIU BoWen, FENG QuanLong, ZHANG Yu, GAO BingBo, SUKHBAATAR Chinzorig, FENG AiPing, YANG JianYu. A 10-m Fractional Vegetation Cover Monthly Dataset of the Kherlen River Basin in 2022[J].Journal of Agricultural Big Data, 2025, 7(1): 59-68.

Figures/Tables 8

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References 17

[1]	赵文慧, 陈妮, 闫瑞, 等. 近20年来北洛河流域植被覆盖度随地形因子变化特征探究. 水土保持研究, 2016, 23(4): 10-14.
[2]	陈妮, 李谭宝, 张晓萍, 等. 北洛河流域植被覆盖度时空变化的遥感动态分析. 水土保持通报, 2013, 33(3): 206-210.
[3]	程佳蕊, 毛德华, 玉山, 等. 1980—2020年克鲁伦河流域草地的时空变化. 水土保持通报, 2022, 42(3):296-303.
[4]	冯权泷, 陈泊安, 李国庆, 等. 遥感影像样本数据集研究综述. 遥感学报, 2022, 26(4): 589-605.
[5]	冯权泷, 牛博文, 朱德海, 等. 土地利用/覆被深度学习遥感分类研究综述. 农业机械学报, 2022, 53(3): 1-17.
[6]	冯权泷, 牛博文, 朱德海, 等. 2019年全国农业塑料大棚遥感分类数据集. 中国科学数据, 2021, 6(4): 153-170.
[7]	VERGER A, BARET F, WEISS M.GEOV2/VGT: Near real time estimation of global biophysical variables from VEGETATION-P data[C]//MultiTemp 2013:7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Banff, AB, Canada,. 2013. DOI: 10.1109/Multi-Temp.2013.6866023.
[8]	LIANG S, CHENG J, JIA K, et al. The global land surface satellite (GLASS) product suite. Bulletin of the American Meteorological Society. 2021, 102: E323-37. DOI: 10.1175/BAMS-D-18-0341.1.
[9]	XIAO J, MOODY A. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment. 2005, 98: 237-250. DOI: 10.1016/j.rse.2005.07.011.
[10]	BRIAN J, TATEISHI R, KOBAYASHI T. Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers. Remote Sensing, 2012, 4:2619-2634. DOI:10.3390/rs4092619.
[11]	LIU D, YANG L, JIA K, et al. Global fractional vegetation cover estimation algorithm for VIIRS reflectance data based on machine learning methods. Remote Sensing. 2018, 10:1648. DOI: 10.3390/rs10101648.
[12]	TU Y, JIA K, LIANG S, et al. Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model. International Journal of Digital Earth, 2018, 13: 487-503. DOI: 10.1080/17538947.2018.1531438.
[13]	YU R, LI S, ZHANG B, et al. A deep transfer learning method for estimating fractional vegetation cover of sentinel-2 multispectral images. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5. DOI: 10.1109/LGRS.2021.3125429.
[14]	LIN X, CHEN J, LOU P, et al. Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features. Plant Methods. 2021, 17:1-18. DOI: https://doi.org/10.1186/s13007-021-00796-5.
[15]	CHEN J, HUANG R, YANG Y, et al. Multi-scale validation and uncertainty analysis of GEOV3 and MuSyQ FVC products: a case study of an alpine grassland ecosystem. Remote Sensing. 2022: 5800. DOI: 10.3390/rs14225800.
[16]	ZHONG G, CHEN J, HUANG R, et al. High spatial resolution fractional vegetation coverage inversion based on UAV and Sentinel-2 data: A case study of Alpine Grassland. Remote Sensing, 2023, 15: 4266. DOI:10.3390/rs15174266.
[17]	LIU D, JIA K, XIA M, et al. Fractional vegetation cover estimation algorithm based on recurrent neural network for MODIS 250 m reflectance data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 6532-6543. DOI: 10.1109/JSTARS.2021.3075624.

特征指数	计算公式
NDVI	NDVI = (NIR−RED)/(NIR+RED)
EVI	EVI = 2.5×(NIR−RED)/(NIR+6×RED−7.5×BLUE+1)
SAVI	SAVI = (1+L)×(NIR−RED)/(NIR+RED+L)
RVI	RVI=NIR/RED
DVI	DVI=NIR−RED
GVI	GVI = (-0.2848×im_B2) + (-0.2435×im_B2) + (-0.5436×im_B4) + (0.7243×im_B8) + (0.0840×im_B11) + (-0.1800×im_B12)
LSWI	LSWI = (NIR − SWIR)/(NIR + SWIR)
GNDVI	GNDVI = (NIR − G)/(NIR + G)
GRVI	GRVI = NIR/G
GDVI	GDVI = NIR − G
NDBI	NDBI = (SWIR−NIR)/(SWIR+NIR)

A 10-m Fractional Vegetation Cover Monthly Dataset of the Kherlen River Basin in 2022

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