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Remote Sens. 2017, 9(11), 1121; doi:10.3390/rs9111121

Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data

1,2,* , 1,2
,
1,2,3
,
4
and
1,2
1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
4
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Received: 6 August 2017 / Revised: 26 October 2017 / Accepted: 31 October 2017 / Published: 3 November 2017
(This article belongs to the Section Forest Remote Sensing)
View Full-Text   |   Download PDF [4034 KB, uploaded 8 November 2017]   |  

Abstract

Fractional vegetation cover (FVC), or green vegetation fraction, is an important parameter for characterizing conditions of the land surface vegetation, and also a key variable of models for simulating cycles of water, carbon and energy on the land surface. There are several types of FVC estimation models using remote sensing data, and evaluating their performance over a specific region is of great significance. Therefore, this study firstly evaluated three types of FVC estimation models using Landsat 7 ETM+ data in an agriculture region of Heihe River Basin, China, and then proposed a combination strategy from different individual models to improve the FVC estimation accuracy, which employed the multiple linear regression (MLR) and Bayesian model average (BMA) methods. The validation results indicated that the spectral mixture analysis model with three endmembers (SMA3) achieved the best FVC estimation accuracy (determination coefficient (R2) = 0.902, root mean square error (RMSE) = 0.076) among the seven individual models using Landsat 7 ETM+ data. In addition, the MLR and BMA combination methods could both improve FVC estimation accuracy (R2 = 0.913, RMSE = 0.063 and R2 = 0.904, RMSE = 0.069 for MLR and BMA, respectively). Therefore, it could be concluded that both MLR and BMA combination methods integrating FVC estimates from different models using Landsat 7 ETM+ data could effectively weaken the estimation errors of individual models and improve the final FVC estimation accuracy. View Full-Text
Keywords: fractional vegetation cover; pixel dimidiate model; spectral mixture analysis; combination; multiple linear regression; Bayesian model average; Landsat 7 ETM+ fractional vegetation cover; pixel dimidiate model; spectral mixture analysis; combination; multiple linear regression; Bayesian model average; Landsat 7 ETM+
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jia, K.; Li, Y.; Liang, S.; Wei, X.; Yao, Y. Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data. Remote Sens. 2017, 9, 1121.

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