Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/207845
Title: Estimating Fractional Vegetation Cover From Landsat-7 ETM&x002B; Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model
Authors: Xiaoxia Wang;Kun Jia;Shunlin Liang;Qiangzi Li;Xiangqin Wei;Yunjun Yao;Xiaotong Zhang;Yixuan Tu
Year: 2017
Publisher: IEEE
Abstract: Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.
URI: http://localhost/handle/Hannan/207845
volume: 55
issue: 10
More Information: 5539,
5546
Appears in Collections:2017

Files in This Item:
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7964787.pdf2.37 MBAdobe PDF
Title: Estimating Fractional Vegetation Cover From Landsat-7 ETM&x002B; Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model
Authors: Xiaoxia Wang;Kun Jia;Shunlin Liang;Qiangzi Li;Xiangqin Wei;Yunjun Yao;Xiaotong Zhang;Yixuan Tu
Year: 2017
Publisher: IEEE
Abstract: Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.
URI: http://localhost/handle/Hannan/207845
volume: 55
issue: 10
More Information: 5539,
5546
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7964787.pdf2.37 MBAdobe PDF
Title: Estimating Fractional Vegetation Cover From Landsat-7 ETM&x002B; Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model
Authors: Xiaoxia Wang;Kun Jia;Shunlin Liang;Qiangzi Li;Xiangqin Wei;Yunjun Yao;Xiaotong Zhang;Yixuan Tu
Year: 2017
Publisher: IEEE
Abstract: Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE = 0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.
URI: http://localhost/handle/Hannan/207845
volume: 55
issue: 10
More Information: 5539,
5546
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7964787.pdf2.37 MBAdobe PDF