Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model. Issue 4 (2nd April 2020)

• Record Type: Journal Article
• Authors: Tu, Yixuan; Jia, Kun; Liang, Shunlin; Wei, Xiangqin; Yao, Yunjun; Zhang, Xiaotong
• Is Part Of: International journal of digital earth. Volume 13:Issue 4(2020)
• Page Start: 487
• Page End: 503
• Publication Date: 2020-04-02
• Subjects: Dynamic Bayesian network -- fractional vegetation cover -- global land surface satellite -- radiative transfer model -- dynamic vegetation model; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2018.1531438
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: A fractional vegetation cover (FVC) estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed, which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarse-resolution FVC pixel were all assumed to have the same vegetation growth model. However, this assumption does not hold over heterogeneous areas, meaning that the method cannot be applied to large regions. Therefore, this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite (GLASS) FVC product. The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data. Then, independent dynamic vegetation models were built for each finer-resolution pixel. Finally, the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale. Validation results indicated that the proposed method ( R 2 = 0.7757, RMSE = 0.0881) performed better than either the previous method ( R 2 = 0.7038, RMSE = 0.1125) or a commonly used method involving look-up table inversions of the PROSAIL model ( R 2 = 0.7457, RMSE = 0.1249).