Uncertainty Analysis of Multisource Land Cover Products in China

Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEaSURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products' uncertainty decreased, and the resolution of 0.1 degrees was the inflection point of consistency.

Automated summary

Satellite-based land cover products are essential for sustainability, with different types of products exhibiting varying levels of uncertainty in their accuracy. This study in China utilized multiple land cover datasets to analyze spatial and temporal changes in forest and nontree vegetation, finding overall low uncertainty but significant differences in local areas. As spatial resolution decreases, so does uncertainty, with a resolution of 0.1 degrees being a turning point in consistency.