Abstract
High spatial resolution and high temporal frequency fractional vegetation cover (FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite (HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index (NDVI) was acquired by using the continuous correction (CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product (GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors (MEs) of forest, cropland, and grassland were −0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809, root-mean-square deviation (RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude (RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.
Highlights
Fractional vegetation cover (FVC) is defined as the ratio of the vertically projected area of vegetation to the whole area (Yan et al, 2012)
We proposed a retrieval algorithm for green FVC estimation at high spatial resolution and high temporal frequency by the combination of fine resolution images and high temporal frequency images
We chose the multi-year averaged Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series as the background field and the HJ-1 NDVI as the highresolution inputs for the CC data assimilation method for each vegetation region and each land cover type
Summary
Fractional vegetation cover (FVC) is defined as the ratio of the vertically projected area of vegetation to the whole area (Yan et al, 2012). There are three main methods for estimating FVC using remote sensing data (Xiao and Moody, 2005; Jiapaer et al, 2011; Yan et al, 2012; Jia et al, 2015), including: (i) empirical models, (ii) pixel unmixing models, and (iii) physical models. Among these methods, the pixel unmixing model estimates FVC at a subpixel level by decomposing a pixel into at least two portions: (a) green vegetation and (b) non-green background. The VI-based mixture model is the most widely used linear unmixing model in high resolution FVC estimation due to its simple model form and computational efficiency when processing large datasets (Gutman and Ignatov, 1998; Zeng et al, 2000; Lu et al, 2003; Montandon and Small, 2008; Wu et al, 2014)
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