Land cover is a fundamental component of crucial importance in the earth sciences. To date, many excellent international teams have created a variety of land cover products covering the entire globe. To provide a reference for researchers studying the Arctic, this paper evaluates four commonly used land cover products. First, we compare and analyze the four land cover products from the perspectives of land cover type, distribution and spatial heterogeneity. Second, we evaluate the accuracy of such products by using two sets of sample points collected from the Arctic region. Finally, we obtain the spatial consistency distribution of the products by means of superposition analysis. The results show the following: (a) among the four land cover products, Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy (63.5%) in the Arctic region, GlobeLand30 has an overall accuracy of 62.2% and the overall accuracy of the Global Land Cover by the National Mapping Organization (GLCNMO) is only 48.8%. When applied in the Arctic region, the overall accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) is only 29.5% due to significant variances. Therefore, MODIS and GLCNMO are not recommended in Arctic-related research as their use may lead to major errors. (b) An evaluation of the consistency of the four products indicates that the classification of the large-scale homogeneous regions in the Arctic yields satisfactory results, whereas the classification results in the forest–tundra ecotone are unsatisfactory. The results serve as a reference for future research. (c) Among the four products, GlobeLand30 is the best choice for analyzing finely divided and unevenly distributed surface features such as waters, urban areas and cropland. Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy, and its classification accuracy is relatively higher for forests, shrubs, sparse vegetation, snow/ice and water. GlobeLand30 and CCI-LC do not vary much from each other in terms of overall accuracy. They differ the most in the classification accuracy of shrub-covered land; CCI-LC performed better than GlobeLand30 in the classification of shrub-covered land, whereas the latter obtained higher accuracy than that of the former in the classification of urban areas and cropland.

High-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial resolution CBERS-04 satellite images to map QVPs in the Yellow River Delta, China, using the Random Forest (RF) classifier. The classification accuracies corresponding to individual and multi-season combined images were compared to understand the seasonal effect and the importance of optimal image timing and acquisition frequency for QVP mapping. For classification based on single season imagery, the early spring March imagery, with an overall accuracy (OA) of 98.1%, was proven to be more adequate than the other four individual seasonal images. The early spring (March) and winter (December) combined dataset produced the most accurate QVP detection results, with a precision rate of 66.3%, a recall rate of 43.9%, and an F measure of 0.528. For larger study areas, the gain in accuracy should be balanced against the increase in processing time and space when including the derived spectral indices in the RF classification model. Future research should focus on applying higher resolution imagery to QVP mapping.

The existing pansharpening methods applied to recently obtained satellite data can produce spectral distortion. Therefore, quality assessments should be performed to address this. However, quality assessment of the whole image may not be sufficient, because major differences in a given region or land cover can be minimized by small differences in another region or land cover in the image. Thus, it is necessary to evaluate the performance of the pansharpening process for different regions and land covers. In this study, the widely used modified intensity-hue-saturation (mIHS), Gram–Schmidt spectral sharpening (GS), color spectral sharpening (CN), and principal component analysis (PCA) pansharpening methods were applied to Gaofen 2 (GF-2) imagery and evaluated according to region and land-cover type, which was determined via an object-oriented image analysis technique with a support vector machine-supervised method based on several reliable quality indices at the native spatial scale without reference. Both visual and quantitative analyses based on region and land cover indicated that all four approaches satisfied the demands for improving the spatial resolution of the original GF-2 multispectral (MS) image, and mIHS produced results superior to those of the GS, CN, and PC methods by preserving image colors. The results indicated differences in the pansharpening quality among different land covers. Generally, for most land-cover types, the mIHS method better preserved the spectral information and spatial autocorrelation compared with the other methods.