Abstract
The fractional vegetation cover (FCover) is an essential biophysical variable and plays a critical role in the carbon cycle studies. Existing FCover products from satellite observations are spatially incomplete and temporally discontinuous, and also inaccurate for some vegetation types to meet the requirements of various applications. In this study, an operational method is proposed to calculate high-quality, accurate FCover from the Global LAnd Surface Satellite (GLASS) leaf area index (LAI) product to ensure physical consistency between LAI and FCover retrievals. As a result, a global FCover product (denoted by TRAGL) were generated from the GLASS LAI product from 2000 to present. With no missing values, the TRAGL FCover product is spatially complete. A comparison of the TRAGL FCover product with the Geoland2/BioPar version 1 (GEOV1) FCover product indicates that these FCover products exhibit similar spatial distribution pattern. However, there were relatively large discrepancies between these FCover products over equatorial rainforests, broadleaf crops in East-central United States, and needleleaf forests in Europe and Siberia. Temporal consistency analysis indicates that TRAGL FCover product has continuous trajectories. Direct validation with ground-based FCover estimates demonstrated that TRAGL FCover values were more accurate (RMSE = 0.0865, and R2 = 0.8848) than GEOV1 (RMSE = 0.1541, and R2 = 0.7621).
Highlights
The fractional vegetation cover (FCover), defined as the fraction of green vegetation as seen from the nadir of the total statistical area, is a canopy-intrinsic variable that depends only on the canopy structural attributes and plays a critical role in climate and hydrologic modeling, natural hazards monitoring, and soil erosion risk assessment [1,2]
FCover product and demonstrated that the Geoland2/BioPar version 1 (GEOV1) FCover product presented the higher percentage of missing values at high latitudes in the northern hemisphere, with a wide variability as a function of the period of the year, mainly due to snow coverage changes along the year as well as increase in observations under dark conditions, above the polar circle in winter, and the equatorial region presents a large fraction of gaps as a consequence of the higher cloudiness [19]
This paper aims to develop an operational method to generate a high-quality global FCover product from the Global LAnd Surface Satellite (GLASS) leaf area index (LAI) data to ensure physical consistency between LAI and FCover retrievals
Summary
The fractional vegetation cover (FCover), defined as the fraction of green vegetation as seen from the nadir of the total statistical area, is a canopy-intrinsic variable that depends only on the canopy structural attributes and plays a critical role in climate and hydrologic modeling, natural hazards monitoring, and soil erosion risk assessment [1,2]. Satellite observations provide the only feasible way to estimate FCover at regional and global scales. Many algorithms have been developed to retrieve FCover from satellite remote sensing data [3,4,5]. Three types of algorithms are employed, empirical methods, spectral mixture analysis (SMA). FCover and vegetation indices or specific spectral reflectance to retrieve FCover from remote sensing data. They are calibrated for distinct vegetation types using field measurements and concurrently acquired satellite images [8,9,10,11,12]. The empirical methods are computationally efficient in operating with large amounts of data and widely used in FCover estimation on a regional scale. The limitation of Remote Sens. 2016, 8, 337; doi:10.3390/rs8040337 www.mdpi.com/journal/remotesensing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
