Comparative Study of Fractional Vegetation Cover Estimation Methods Based on Fine Spatial Resolution Images for Three Vegetation Types

High-accuracy estimates of fractional vegetation cover (FVC) are vital for regional-scale vegetation growth monitoring. In this context, a question worth exploring is whether FVC estimation methods developed at 300-m to 1-km spatial resolution are suitable for finer spatial resolution satellite images. This study compared the performances of three types of algorithms [i.e., the pixel dichotomy model (PDM) based on either the normalized difference vegetation index (NDVI) or an index of near-infrared reflectance of vegetation (NIRv), the gap probability theory (GPT), and linear spectral mixture analysis (LSMA)] based on FVC ground measurements and fine resolution reference maps from the Validation of Land European Remote sensing Instrument (VALERI) project and the ImagineS field campaigns. For all vegetation types, the FVC estimates from the GPT method showed the best consistency with ground measurements of FVC [root mean square error (RMSE) = 0.17 and bias (BIAS) = 0.05]. For forest types, the PDM method based on NDVI also showed satisfactory results with ground measurements (RMSE = 0.17 and BIAS = 0.11). For sparse grasses, the PDM method based on NIRv showed better agreement with ground measurements (RMSE = 0.15 and BIAS = 0.01). This study provides a reference for selecting the method of FVC estimation with fine spatial resolution images.