## 1 Introduction  
Land cover is a concept emerging with the development of remote sensing technology, and remote sensing is the only effective means for large-scale land cover mapping [1]. Since the 1980s, the international scientific community has been highly concerned about remote sensing mapping of global land cover [2; 3; 4]. A variety of 1000, 300, 30, and 10 m resolution global, regional, or national land cover products have been developed [5; 6; 7; 8; 9; 10; 11; 12]. Classical supervised and unsupervised classification technologies are commonly used, but the accuracy is not high, which is about 60% to 70% [13; 14]. A deep learning algorithm has not been applied to the classification of land cover products on a large scale [15]. The second-level land cover product classification is more difficult to obtain reliable results via automatic classification. Classification accuracy varies spatially, and spatial variation in accuracy should be quantitatively evaluated. Evaluating and improving the accuracy of land cover product classification (hereinafter referred to as pre-classified image) obtained by conventional classification algorithm and quantifying the uncertainty of classification are necessary to study global change, geographical condition census, social environment planning, and ecological resource management. Two kinds of methods are used to improve the accuracy of remote sensing classification. One of the methods is the application of Geoscience Knowledge Rules. Since the 1980s, experts and scholars have introduced expert systems and knowledge engineering to solve remote sensing classification problems [16; 17; 18; 19; 20]. In previous land cover mapping, auxiliary data, such as digital elevation model (DEM) data, ecological region data, vegetation data of countries or regions [5], global mangrove atlas, global human settlements, and regional data of global urban coverage ([[http://maps.elie.ucl.ac.be/CCI/viewer/index.php](http://maps.elie.ucl.ac.be/CCI/viewer/index.php)]([http://maps.elie.ucl.ac.be/CCI/viewer/index.php](http://maps.elie.ucl.ac.be/CCI/viewer/index.php))),MODIS (moderate-resolution imaging spectroradiometer, NASA, USA) NDVI (normalized different vegetation index) data, global geographic information data, global DEM data, various thematic data, and online high-resolution images are employed to improve product accuracy [21]. However, the expert knowledge and reference auxiliary data used in land cover mapping are sporadic and unsystematic, and no global integral system is available to manage expert knowledge and auxiliary data for reuse. Eco geographical regions are areas where ecosystems (and the type, quality, and quantity of environmental resources) are generally similar. They are relatively large units of land containing a distinct assemblage of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change [22]. Eco geographical regions can be applied as a frame to construct a global expert knowledge base and assist in remote sensing image classification. [PERSON] et al. [23] adopted the \"world terrestrial ecological region\" established by the World Wildlife Fund for natural protection as the basic framework of the global Geo-Eco zoning knowledge base [22]. An object-oriented method is used to construct a rule base and help identify spurious changes in remote sensing image detection. Five kinds of attributes, namely, DEM, slope, NDVI, temperature, and moisture, of each Geo-eco zone, are collected to identify spurious change. The accuracy of change detection is improved to a certain extent [23]. Another method to improve the accuracy of remote sensing classification is the application of geostatistics. Geostatistics is used to analyze and predict the values associated with spatial or spatiotemporal phenomena. Geostatistics has been applied in remote sensing since the 1980s but not popular. [PERSON] [24] made a detailed review.