4.7 Article

Spatial, temporal, and spectral variations in albedo due to vegetation changes in China's grasslands

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ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.03.020

Keywords

Albedo; Greenness; Grassland; Climate change; China

Funding

  1. Strategic Priority Research Program of Chinese Academy of Sciences (CAS) [XDA19040301]
  2. Key Research Program of Frontier Sciences of Chinese Academy of Sciences (CAS) [QYZDB-SSW-DQC005]
  3. National Natural Science Foundation of China [41701501, 41501054]
  4. Thousand Youth Talents Plan

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Changes in Earth's albedo due to vegetation dynamics, snow cover, and land cover change have attracted much attention. However, the effects of vegetation dynamics on albedo have not been comprehensively documented according to its spatial (regional), temporal (within growing season), and spectral (visible, near-infrared, and shortwave) characteristics. This study examined the effects of vegetation greenness on albedo from 2000 to 2014 in China's grasslands, which have considerable intra- and inter-annual variations, using remote sensing-based albedo and two-band Enhanced Vegetation Index (EVI2) data. Generally, we found an insignificant negative correlation between the shortwave (SW) albedo and EVI2 for grasslands in China. However, the visible (VIS) albedo was more sensitive to changes in vegetation greenness than near-infrared (NIR) albedo in China's grasslands. The relationship between the NIR albedo and EVI2 was more complicated, especially in the Tibetan Plateau (TP), where the correlation was negative in the early growing season and positive in the late growing season; while the correlation between the NIR albedo and EVI2 was always negative in main part of Inner Mongolia (IM). The different albedo-EVI2 relationships in IM and TP may be related to differences in soil albedos. The higher sensitivity of the SW albedo to vegetation greenness change in IM, the stronger effect on land surface radiation budget. Our finding about vegetation-induced changes in albedo differ in space, time and spectral bands is expected to contribute to the improvement of land surface models.

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