# Uncertainty Analysis of Multisource Land Cover Products in China
## 1 Introduction  
Multisource land cover products are the basis for studying the Earth's surface and atmosphere [1] and have had a profound impact on the survival and development of humanity. Reasonably classified and accurate land cover products have been essential for studying Earth surface processes and information [2]. Land cover products play a crucial role in studying terrestrial surface processes, ecosystem assessments, environmental modeling, geographic national condition monitoring, remote sensing inversion, etc. [3, 4]. In short, land cover products are the basis for sustainability research [5]. In recent decades, the rapid development of remote sensing technology and algorithm improvements have resulted in many land cover products [6, 7]. Twenty-three categories of global land cover products and 41 categories of regional land cover products were summarized by [PERSON] [8]. Among them, the most widely used global land cover products include IGBP-DISCover from the US Geological Survey, UMD from the University of Maryland, CCI-LC from the European Space Agency, and FROM-GLC from Tsinghua University in China, etc. Due to the diverse types of data, different data sources, different classification methods, and different systems, the quality of these products is not the same, and the differences are large [9]. High-resolution products have high accuracy at regional and global scales [10] and have an extensive imaging range, low cost, and wide application [11, 12, 13]. When research has been conducted by scholars, products of a certain resolution have been selected, and the uncertainty brought about by spatial resolution has needed to be considered [14]. The types of land cover products can be divided into three categories: Qualitative products, semiquantitative products, and quantitative products. Qualitative products are represented by MCD12 products with discrete classes [15]; semiquantitative products are represented by ESA CCI products with several classes at a predetermined ratio [16]; and quantitative products are represented by VCF products with land cover fractions. The percentage of land cover types for each pixel of VCF products has been recorded, and the area of land cover types can be calculated quantitatively, which is a quantitative product. Land cover products act as an essential foundation for scholars to carry out remote sensing inversion and model simulation. Land cover products have essential applications when subgrid schemes, parameter assignment, and parameterization scheme determination are considered--the uncertainty of different products in these processes needed to be explored. For example, in the leaf area index (LAI) inversion, land cover data are needed. Due to the significant differences in multisource land cover products' spatial distribution and accuracy [17], different land cover data had greater accuracy in influencing LAI inversion. Researchers have pointed out that the main land cover products for China's land area cannot meet the needs of land surface process simulation, and there is great uncertainty [18]. With different spatial resolutions of land cover data at regional and global scales, gross primary productivity (GPP) model simulation results have considerable differences [19]. Land cover is the most significant uncertainty factor in estimating carbon storage and release in terrestrial ecosystems [20]. Due to the limited spatial resolution of remote sensing images, the existence of subpixels needs to be considered. That is, several different land cover types may be included in a single pixel [21], which would bring greater uncertainty to the research results [22; 23]. This uncertainty varies with spatial resolution [24]. Therefore, how the uncertainty of multisource land cover products changes with resolution urgently needs to be quantitatively explored. It is of great significance for scholars to carry out remote sensing inversion and numerical simulation.