中国区域30m/15天植被覆盖度数据集（2010-2022）

高时空分辨率的植被覆盖度产品存在着广泛的应用需求。本数据集提供了中国区域2010-2022年时空连续的30m/每半月植被覆盖度（FVC）产品。该FVC产品基于像元二分法生成，首先利用Landsat系列数据使用谐波模型构造30m/15天时空连续的植被指数（NDVI）数据集，随后基于逐像元的NDVI至FVC的转换系数，基于像元二分模型生成30m/15天FVC产品。与怀来和塞罕坝不同植被类型样方获取的地面实测FVC相比，本FVC数据集RMSD低于0.1，R方0.8左右。与全国22个小流域的时间连续观测FVC相比，本FVC产品与地面实测FVC具有相似的时间曲线（大多数流域下RMSD低于0.1，R方接近0.9）。与其他主流FVC产品进行交叉验证，结果表明本FVC与GLASS FVC和GEOV3 FVC具有较为一致的时空分布模式。

There is a wide range of application requirements for high spatiotemporal resolution vegetation fraction cover (FVC) products. This dataset provides spatiotemporally continuous 30 m/15-day vegetation fraction cover (FVC) products for China from 2010 to 2022. This FVC product is generated based on the dimidiate pixel model: first, a spatiotemporally continuous 30 m/15-day Normalized Difference Vegetation Index (NDVI) dataset was constructed using the harmonic model with Landsat series data, then combined with pixel-wise conversion coefficients from NDVI to FVC, the 30 m/15-day FVC product was generated via the dimidiate pixel model. Compared with in-situ FVC measured in quadrats of different vegetation types in Huailai and Saihanba, the root mean square deviation (RMSD) of this FVC dataset is less than 0.1, with a coefficient of determination (R²) of approximately 0.8. Compared with temporally continuous in-situ FVC observations from 22 small watersheds across China, this FVC product exhibits similar temporal curves to ground-measured FVC values, with RMSD below 0.1 and R² close to 0.9 in most watersheds. Cross-validation against other mainstream FVC products indicates that this FVC dataset has relatively consistent spatiotemporal distribution patterns with GLASS FVC and GEOV3 FVC.

搜集汇总

背景与挑战

背景概述

该数据集提供了中国区域2010-2022年时空连续的30米空间分辨率和15天时间分辨率的植被覆盖度（FVC）产品，基于像元二分法利用Landsat数据生成，并通过地面实测验证显示较高精度（RMSD低于0.1，R方约0.8），与其他主流FVC产品具有一致的时空分布模式，适用于高分辨率植被监测研究。