| 光谱学与光谱分析 |
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| Estimation of Vegetation Water Content from Landsat 8 OLI Data |
| ZHENG Xing-ming1, 2, DING Yan-ling1, ZHAO Kai1, 2*, JIANG Tao1, LI Xiao-feng1, 2, ZHANG Shi-yi1, LI Yang-yang1, WU Li-li1, SUN Jian3, REN Jian-hua1, ZHANG Xuan-xuan4 |
1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2. Changchun Jingyuetan Remote Sensing Test Site, Chinese Academy of Sciences, Changchun 130102, China
3. College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
4. Institute for Geo-Informatics & Digital Mine Research, Northeastern University, Shenyang 110819, China |
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Abstract The present paper aims to analyze the capabilities and limitations for retrieving vegetation water content from Landsat8 OLI (Operational Land Imager) sensor-new generation of earth observation program. First, the effect of soil background on canopy reflectance and the sensitive band to vegetation water content were analyzed based on simulated dataset from ProSail model. Then, based on vegetation water indices from Landsat8 OLI and field vegetation water content during June 1 2013 to August 14 2013, the best vegetation water index for estimating vegetation water content was found through comparing 12 different indices. The results show that: (1) red, near infrared and two shortwave infrared bands of OLI sensor are sensitive to the change in vegetation water content, and near infrared band is the most sensitive one; (2) At low vegetation coverage, solar radiation reflected by soil background will reach to spectral sensor and influence the relationship between vegetation water index and vegetation water content, and simulation results from ProSail model also show that soil background reflectance has a significant impact on vegetation canopy reflectance in both wet and dry soil conditions, so the optimized soil adjusted vegetation index (OSAVI) was used in this paper to remove the effect of soil background on vegetation water index and improve its relationship with vegetation water content; (3) for the 12 vegetation water indices, the relationship between MSI2 and vegetation water content is the best with the R-square of 0.948 and the average error of vegetation water content is 0.52 kg·m-2; (4) it is difficult to estimate vegetation water content from vegetation water indices when vegetation water content is larger than 2 kg·m-2 due to spectral saturation of these indices.
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Received: 2013-10-11
Accepted: 2014-02-14
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Corresponding Authors:
ZHAO Kai
E-mail: zhaokai@neigae.ac.cn
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