A Time-Efficient Fractional Vegetation Cover Estimation Method Using the Dynamic Vegetation Growth Information From Time Series GLASS FVC Product

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).