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Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data ( R^\mathrm \mathbf 2 = 0.6942 , RMSE =0.0884), compared with the widely used dimidiate pixel model ( R^\mathrm \mathbf 2 = 0.7034 , RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale. [ABSTRACT FROM PUBLISHER]

Forest cover information is essential for natural resource management and for climate change studies. In this paper, the fractional forest cover (FFC) in Northeast China was estimated using neural networks (NNs) based on the Global Inventory Modeling and Mapping Studies (GIMMS3g) Normalized Difference Vegetation Index (NDVI) data with 8-km resolution from 1982 to 2011. Furthermore, the relationship between FFC and two key climatic parameters (temperature and precipitation) was also analyzed. The validation results indicated a satisfactory performance (\bf R^\bf 2 = \bf 0.81, \bf RMSE = \bf 11.7\%) of the FFC estimation method using NNs and time-series GIMMS3g NDVI data. The temporal and spatial characteristics of FFC changes were analyzed. The forest cover had a slightly decreasing trend during the study period for the entire Northeast China region. However, there were two distinct periods with opposite trends in the FFC change. The FFC had first increased from 1982 to 1998 (\bf 0.391\%\ \bf year^\bf - 1), and then decreased from 1998 to 2011 (\bf - 0.667\%\ \bf year^\bf - 1). The correlation analysis between the FFC and the climatic variations suggested that temperature and precipitation were not the decisive factors on controlling FFC changes in most of the Northeast China regions, and active forest disturbance might be the more important factor for FFC change in Northeast China. [ABSTRACT FROM PUBLISHER]

Presents corrections to affiliation information in the paper, “Image classification using rapideye data: Integration of spectral and textual features in a random forest classifier,” (Zhang, H., et al),IEEE J. Sel. Topics Appl. Earth Observ. Remote Ses., vol. 10, no. 12, pp. 5334–5349, Dec. 2017. [ABSTRACT FROM AUTHOR]