2000-2018年全球500米植被覆盖度8天数据

植被覆盖度(Fractional Vegetation Cover，FVC) 定义为绿色植被在地面的垂直投影面积占统计区总面积的百分比。植被覆盖度是刻画地表植被覆盖的一个重要参数，在全球变化研究、地表过程模拟和水文生态模型中发挥着重要作用。因此，研究和生产完整的长时间序列、高时间分辨率的全球FVC气候数据集对于提升我国全球变化遥感对地观测能力、提高地球系统模式和全球变化研究水平具有重要的科学意义和现实意义。
本研究在全球高空间分辨率样本数据生产的基础上，研究基于机器学习算法的全球长时间序列、高时间分辨率的FVC反演方法和FVC气候数据集生产，为开展全球变化应用研究提供基础数据。本研究FVC数据集的时间分辨率为8天，2000年-2020年为0.5km。

Fractional Vegetation Cover (FVC) is defined as the percentage of the total area of a statistical region occupied by the vertical projected area of green vegetation on the ground surface. As a critical parameter characterizing surface vegetation coverage, FVC plays a vital role in global change research, surface process simulation, and hydrological-ecological models. Therefore, developing and producing a complete global FVC climate dataset with long-time series and high temporal resolution holds significant scientific and practical value for enhancing China's remote sensing earth observation capabilities for global change studies, as well as advancing the development of Earth System Models and global change research.

Based on the production of global high-spatial-resolution sample data, this study investigates the machine learning algorithm-based retrieval method for global long-time series, high-temporal-resolution FVC, and the production of the FVC climate dataset, providing foundational data for global change application research. The temporal resolution of the FVC dataset developed in this study is 8 days, with a spatial resolution of 0.5 km for the period from 2000 to 2020.

搜集汇总

背景与挑战

背景概述

该数据集提供了2000年至2018年全球范围的植被覆盖度（FVC）8天合成数据，空间分辨率为500米，基于MOD09A1反射率产品，采用机器学习算法生成。植被覆盖度是表征地表植被覆盖的关键参数，对全球变化研究和生态模型具有重要意义。数据集经过验证，准确性较高（R²=0.7878，RMSE=0.1221），优于其他类似产品，适用于长期气候和生态分析。