2001年-2019年全球八天500米聚集指数产品数据集

借助地面实测叶面积指数和高空间分辨率影像数据，通过对不同BRDF模型和SZA估算的聚集指数进行对比分析，提出了估算聚集指数的最佳BRDF模型和SZA。针对植被稀疏地区聚集指数难以估算的难题，提出利用倾斜观测数据估算聚集指数的方法，较好的解决了植被稀疏地区聚集指数估算的问题。并基于本研究提出的改进算法，开展了全球聚集指数产品的生产，获得了2001-2019年全球8天500米聚集指数产品。

By utilizing ground-measured Leaf Area Index (LAI) and high-spatial-resolution remote sensing imagery data, this study conducted comparative analysis on clumping index values retrieved via various Bidirectional Reflectance Distribution Function (BRDF) models and Solar Zenith Angles (SZA), and proposed the optimal BRDF model and SZA for clumping index estimation. To tackle the challenge of unreliable clumping index estimation in sparse vegetation areas, this study put forward a novel method for clumping index estimation using oblique observation data, which effectively addressed the estimation difficulty in such regions. Furthermore, based on the improved algorithm proposed in this research, the production of global clumping index products was carried out, and a global 8-day 500-meter resolution clumping index product spanning from 2001 to 2019 was generated.

搜集汇总

背景与挑战

背景概述

该数据集是2001年至2019年全球范围的植被聚集指数产品，空间分辨率为500米，时间分辨率为八天，总存储量570.00GB。它基于改进的BRDF模型和太阳天顶角估算方法生产，特别解决了植被稀疏地区的指数估算难题，主要用于全球碳循环和植被研究。