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Fractional vegetation cover (FVC) is an important land surface parameter for many environmental and climate-related modeling and agricultural applications. Incorporating vegetation growth information into FVC estimation process could effectively improve FVC estimation accuracy. Methods utilizing vegetation growth information from field measurement and coarse resolution FVC product have been developed recently to estimate site-scale and finer spatial resolution FVC, and achieved satisfactory performances. However, the computational efficiency of these methods is not satisfactory and they are only feasible for analyzing historical data containing a complete vegetation growth cycle. This letter developed a time-efficient FVC estimation method at Landsat scale based on temporally rich data from coarse spatial resolution Global LAnd Surface Satellite (GLASS) FVC, which facilitates development of a time-efficient dynamic vegetation growth model, and radiative transfer models linking Landsat 7 reflectance to FVC, and all combined in a probabilistic dynamic Bayesian network (DBN) framework. In addition, the proposed method is also suitable for real-time FVC estimation and has the potential to be applied on a larger scale. Validation results indicate that the performance of the proposed method is satisfactory ($R^{2} =0.889$ , RMSE = 0.0917) and comparable to previously developed inefficient but well-established FVC estimation method incorporating the vegetation growth model represented by modified Verhulst logistic equation ($R^{2} =0.884$ , RMSE = 0.0913). [ABSTRACT FROM AUTHOR]

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 ( R^\mathrm \mathbf 2 = 0.6942 , RMSE =0.0884), compared with the widely used dimidiate pixel model ( R^\mathrm \mathbf 2 = 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. [ABSTRACT FROM PUBLISHER]