Open AccessDerivation of the Green Vegetation Fraction of the Whole China from 2000 to 2010 from MODIS Data
Author(s) -
Xiaosong Li,
Zhang Jin
Publication year - 2016
Publication title -
earth interactions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.309
H-Index - 38
ISSN - 1087-3562
DOI - 10.1175/ei-d-15-0010.1
Subject(s) - normalized difference vegetation index , environmental science , hyperspectral imaging , vegetation (pathology) , soil water , remote sensing , soil science , geology , climate change , medicine , oceanography , pathology
The green vegetation fraction Fg, which represents the horizontal density of live vegetation, is an important parameter for the study of global energy, carbon, hydrological, and biogeochemical cycling. A common method of calculating Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI, , and full vegetation NDVI, . However, many uncertainties exist for the determination of these parameters at large scales. The present study investigates how and determination can impact Fg calculations for all of China, based on different land-cover datasets, hyperspectral data, and soil type classification maps. The results show the following: 1) The regional ChinaCover dataset, with higher accuracy and more detailed classification, is preferable for calculating Fg in China, compared with the most commonly used MOD12Q1 dataset, although it would not lead to too much difference in values. 2) The soil NDVI from Hyperion datasets shows that soils have highly variable NDVI values (0.006–0.2), and 79.36% of the area studied has a much larger NDVI value than the commonly used value of 0.05. Therefore, the dynamic values with different soil types are much better for Fg calculation than the invariant value (0.05), which would yield a significant overestimation of Fg, especially for areas with low vegetation coverage. 3) A high-quality Fg dataset for China from 2000 to 2010 was established with and parameters based on MOD13Q1 250-m NDVI data.