Mapping Spatially Seamless Fractional Vegetation Cover over China at a 30-m Resolution and Semimonthly Intervals in 2010-2020 Based on Google Earth Engine

Fractional vegetation cover(FVC)is a critical biophysical parameter that characterizes the status of terrestrial ecosystems.The spatial resolutions of most existing FVC products are still at the kilometer level.However,there is growing demand for FVC products with high spatial and temporal resolutions in remote sensing applications.This study developed an operational method to generate 30-m/15-day FVC products over China.Landsat datasets were employed to generate a continuous normalized difference vegetation index(NDVI)time series based on the Google Earth Engine platform from 2010 to 2020.The NDVI was transformed to FVC using an improved vegetation index(VI)-based mixture model,which quantitatively calculated the pixelwise coefficients to transform the NDVI to FVC.A comparison between the generated FVC,the Global LAnd Surface Satellite(GLASS)FVC,and a global FVC product(GEOV3 FVC)indicated consistent spatial patterns and temporal profiles,with a root mean square deviation(RMSD)value near 0.1 and an R^(2) value of approximately 0.8.Direct validation was conducted using ground measurements from croplands at the Huailai site and forests at the Saihanba site.Additionally,validation was performed with the FVC time series data observed at 151 plots in 22 small watersheds.The generated FVC showed a reasonable accuracy(RMSD values of less than 0.10 for the Huailai and Saihanba sites)and temporal trajectories that were similar to the field-measured FVC(RMSD values below 0.1 and R2 values of approximately 0.9 for most small watersheds).The proposed method outperformed the traditional VIbased mixture model and had the practicability and flexibility to generate the FVC at different resolutions and at a large scale.

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

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.