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Peng Zhao
Institute of RS and GIS, Peking University, Beijing 100871, China

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Journal article
Published: 27 June 2017 in Remote Sensing
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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.

ACS Style

Yaokui Cui; Peng Zhao; Binyan Yan; Hongjie Xie; Pengtao Yu; Wei Wan; Wenjie Fan; Yang Hong. 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 Sensing 2017, 9, 661 .

AMA Style

Yaokui Cui, Peng Zhao, Binyan Yan, Hongjie Xie, Pengtao Yu, Wei Wan, Wenjie Fan, Yang Hong. 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 Sensing. 2017; 9 (7):661.

Chicago/Turabian Style

Yaokui Cui; Peng Zhao; Binyan Yan; Hongjie Xie; Pengtao Yu; Wei Wan; Wenjie Fan; Yang Hong. 2017. "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 Sensing 9, no. 7: 661.

Journal article
Published: 07 April 2016 in Remote Sensing
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Mountainous areas with rugged terrains are widely distributed around the world. Remotely sensed values of the fraction of absorbed photosynthetically active radiation (FAPAR) suffer from the effect of rugged terrain. In this study, the effect of rugged terrain was incorporated into the FAPAR model based on recollision probability (FAPAR-P), which was improved in two aspects: calculating the sky viewing factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting the interception probability according to the slope and aspect of each pixel. The newly developed model is called FAPAR-PR (FAPAR-P Model for Rugged Terrain Area). Two study areas were chosen to validate the proposed model: the Dayekou watershed in Gansu Province, and Weichang in Hebei Province, China. The FAPAR values derived from the models were compared with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., Cambridge, UK). The validation results show that the FAPAR-PR model is applicable to rugged terrain areas, and it achieves a high level of accuracy. The FAPAR retrieval at different scales was also conducted to estimate the effect of terrain on the FAPAR-P and FAPAR-PR models. In our chosen study area, the effect of rugged terrain was significant in fine resolution pixels, but it was not obvious at larger scales, as the effects of slope and aspect were partly eliminated by the upscaling of the digital elevation model.

ACS Style

Peng Zhao; Wenjie Fan; Yuan Liu; Xihan Mu; Xiru Xu; Jingjing Peng. Study of the Remote Sensing Model of FAPAR over Rugged Terrains. Remote Sensing 2016, 8, 309 .

AMA Style

Peng Zhao, Wenjie Fan, Yuan Liu, Xihan Mu, Xiru Xu, Jingjing Peng. Study of the Remote Sensing Model of FAPAR over Rugged Terrains. Remote Sensing. 2016; 8 (4):309.

Chicago/Turabian Style

Peng Zhao; Wenjie Fan; Yuan Liu; Xihan Mu; Xiru Xu; Jingjing Peng. 2016. "Study of the Remote Sensing Model of FAPAR over Rugged Terrains." Remote Sensing 8, no. 4: 309.

Journal article
Published: 18 November 2015 in Remote Sensing
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Albedo characterizes the radiometric interface of land surfaces, especially vegetation, and the atmosphere. Albedo is a critical input to many models, such as crop growth models, hydrological models and climate models. For the extensive attention to crop monitoring, a physical albedo model for crops is developed based on the law of energy conservation and spectral invariants, which is derived from a prior forest albedo model. The model inputs have been efficiently and physically parameterized, including the dependency of albedo on the solar zenith/azimuth angle, the fraction of diffuse skylight in the incident radiance, the canopy structure, the leaf reflectance/transmittance and the soil reflectance characteristics. Both the anisotropy of soil reflectance and the clumping effect of crop leaves at the canopy scale are considered, which contribute to the improvement of the model accuracy. The comparison between the model results and Monte Carlo simulation results indicates that the canopy albedo has high accuracy with an RMSE < 0.005. The validation using ground measurements has also demonstrated the reliability of the model and that it can reflect the interaction mechanism between radiation and the canopy-soil system.

ACS Style

Jingjing Peng; Wenjie Fan; Xiru Xu; Lizhao Wang; Qinhuo Liu; Jvcai Li; Peng Zhao. Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants. Remote Sensing 2015, 7, 15536 -15560.

AMA Style

Jingjing Peng, Wenjie Fan, Xiru Xu, Lizhao Wang, Qinhuo Liu, Jvcai Li, Peng Zhao. Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants. Remote Sensing. 2015; 7 (11):15536-15560.

Chicago/Turabian Style

Jingjing Peng; Wenjie Fan; Xiru Xu; Lizhao Wang; Qinhuo Liu; Jvcai Li; Peng Zhao. 2015. "Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants." Remote Sensing 7, no. 11: 15536-15560.