中国30米纯像元归一化植被指数图集（2014）_用于植被覆盖度估算

本研究利用多角度遥感数据，生成了中国全域30米空间分辨率的纯植被像元归一化植被指数（Vv）和纯土壤像元归一化植被指数（Vs）地图。这些像元级的Vv和Vs地图可与植被指数（VI）模型结合，支持不同空间分辨率和大范围区域的植被覆盖度（FVC）快速精确估算。 产品采用多角度算法（MultiVI）生成，该算法有效解决了Vv和Vs固有的空间变异性问题，与传统统计方法相比显著提高了FVC估算精度。在不同实验场地与实地测量FVC验证显示，估算的FVC均方根偏差（RMSD）约为0.1，表明产品具有较高的可靠性。This study employed multi-angle remote sensing data to generate 30-meter spatial resolution maps of the Normalized Difference Vegetation Index for pure vegetation pixels (Vv) and pure soil pixels (Vs) across the entire territory of China. These pixel-level Vv and Vs maps can be integrated with vegetation index (VI) models to enable rapid and accurate estimation of fractional vegetation cover (FVC) in large-scale regions with diverse spatial resolutions. The product was developed using the multi-angle algorithm (MultiVI), which effectively resolves the inherent spatial variability of Vv and Vs, and notably enhances the accuracy of FVC estimation compared to traditional statistical methods. Validation against field-measured FVC at multiple experimental sites demonstrates that the root mean square deviation (RMSD) of the estimated FVC is approximately 0.1, confirming the high reliability of this product.

搜集汇总

背景与挑战

背景概述

该数据集是基于2014年多角度遥感数据生成的中国30米分辨率纯像元归一化植被指数（NDVI）图集，专门用于植被覆盖度（FVC）估算。它通过MultiVI算法直接估计纯植被和纯土壤的NDVI值，解决了空间变异性问题，相比传统方法提高了精度，均方根偏差约为0.1，适用于大尺度生态监测和植被分析。