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Grassland resources guarantee the balance of ecosystems and the sustainable development of animal husbandry. Spatial information is essential for grass resource management in pastoral areas, which can be extracted quickly on a large-scale by using remote sensing data. However, most conventional methods are based on the grid pixels of remote sensing images. The spatial information based on these regular units inevitably has the phenomenon of mixed pixels, which leads to the unreliability of grassland resource information with irregular spatial heterogeneity. To resolve this problem, this article takes the spatial mapping of fractional vegetation cover (FVC) as a typical target task of information extraction of grassland resources and proposes a geoparcel-based spatial prediction method in which irregular geographic objects from high spatial resolution remote sensing images, i.e., grassland geoparcels, are used as basic mapping units instead of traditional regular units. This change can make the spatial expression of mapping closer to the reality of grassland due to the fine spatial structure. Moreover, multisource spatial data can be integrated together in the regression-based procedure of prediction through the geoparcel as a unified spatial benchmark. A case experiment of Abag Banner, Inner Mongolia, China, shows that the proposed method can achieve good FVC mapping results. The spatial prediction of FVC based on grassland geoparcels is verified to be effective when a random forest regression is used in the modeling. In comparison with traditional regular grid-based methods, the proposed method achieves higher accuracy with 11.89&#x0025; in relative root mean squared error (&#x0025;RMSE), and 0.86 in determination coefficient of regression (<italic>R</italic><sup>2</sup>). It has advantages in sensing the tiny spatial heterogeneity of FVC at the boundary of grassland change. The formalized procedure potentially promotes the development of spatial mapping technology for grassland resources.

Geoparcel-Based Spatial Prediction Method for Grassland Fractional Vegetation Cover Mapping