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Remote Sens. 2017, 9(7), 661; https://doi.org/10.3390/rs9070661

Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin

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3,4,* and 1,2,3,8,*
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State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
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Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
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Institute of RS and GIS, Peking University, Beijing 100871, China
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Beijing Key Laboratory of Spatial Information Integration & Its Applications, Beijing 100871, China
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Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78712, USA
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Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA
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Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
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Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA
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Authors to whom correspondence should be addressed.
Received: 10 April 2017 / Revised: 16 June 2017 / Accepted: 21 June 2017 / Published: 27 June 2017
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
View Full-Text   |   Download PDF [4009 KB, uploaded 28 June 2017]   |  

Abstract

Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model’s strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. View Full-Text
Keywords: rainfall interception; RS-Gash analytical model; high resolution; remote sensing rainfall interception; RS-Gash analytical model; high resolution; remote sensing
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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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Cui, Y.; Zhao, P.; Yan, B.; Xie, H.; Yu, P.; Wan, W.; Fan, W.; Hong, Y. Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin. Remote Sens. 2017, 9, 661.

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