# Uncertainty Analysis of Multisource Land Cover Products in China
## 2 Materials and Methods  
### Land Cover Data  
The multisource land cover products used in this study were representatively qualitative, semiquantitative, and quantitative land cover products. The source, resolution, and land cover classification systems, and classification methods of the three products were different. The parameters are shown in Table 1.  
The MODIS land cover products (MCD12) classification system has 17 categories. The overall evaluation accuracy was 78.3%, and the spatial resolution was 1 km. It is a commonly used land cover data product of the IGBP system [30]. The product was obtained based on the artificial neural network and decision tree classification method, which can be downloaded at [[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)) (accessed on 22 October 2014). It was a qualitative product with discrete classes.  
ESA CCI land cover data divided the land surface into 37 land cover categories according to the United Nations Land Cover Classification System (UN-LCCS), with an overall accuracy of 71.5%. The data came from [[http://maps.elie.ucl.ac.be/CCI/viewer/download/](http://maps.elie.ucl.ac.be/CCI/viewer/download/)]([http://maps.elie.ucl.ac.be/CCI/viewer/download/](http://maps.elie.ucl.ac.be/CCI/viewer/download/)) (accessed on 22 December 2019) with a spatial resolution of 300 m. The data had high user accuracy for paddy fields, evergreen broad-leaved forests, cities, bare land, water bodies, and permanent glaciers [31]. In addition, the proportion of plant functional types (PFTs) in qualitative land cover types was considered in this study. Therefore, the initial data of 37 types of ESA CCI were summarized into \(0.5\times 0.5^{\circ}\) resolution and then converted into PFT data. The PFTs included forests, shrubs, farmland, and grassland [32]. Combined with the actual situation in the study area, Classes 2 and 1 were calculated through the PFT cross-walking table, as shown in Table 2. The ESA CCI PFT-based land cover products were converted from qualitative pixel types to land cover percentages and from qualitative products to quantitative products, which are semiquantitative products.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product** & **Organization** & **Resolution** & \begin{tabular}{c} **Land Cover** \\ **Category** \\ \end{tabular} & **Classification** & **Type** \\ \hline MCD12 & \begin{tabular}{c} Boston \\ University \\ European \\ \end{tabular} & 1 KM & 17 & Decision tree & Qualitative \\ ESA CCI & \begin{tabular}{c} Space \\ Agency \\ \end{tabular} & 300 M & 37 & \begin{tabular}{c} Unsupervised \\ classification \\ \end{tabular} & \begin{tabular}{c} Semi- \\ quantitative \\ \end{tabular} \\ MEaSURES & \
\begin{tabular}{c} University of \\ Maryland \\ \end{tabular} & 5 KM & 3 &
\begin{tabular}{c} Linear Model \\ Trees \\ \end{tabular} & Quantitative \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land cover products used in this work.  
MEaSURES vegetation continuous field (VCF) products were divided into three layers: Tree cover, nontree vegetation, and bare ground. VCF products represent a new revolutionary method to characterize plant land cover [33]. Traditional land cover maps assigned each pixel to a single land cover category. VCF products used fractional vegetation coverage (FVC), which was a primary means of measuring global forest cover change [11; 34; 35]. FVC was defined as the ratio of the vertical projection area of green vegetation on the ground to the total area. The spatial resolution was 5 km, and the data came from [[https://lpdaac.usgs.gov/productsearch/](https://lpdaac.usgs.gov/productsearch/)]([https://lpdaac.usgs.gov/productsearch/](https://lpdaac.usgs.gov/productsearch/)) (accessed on 19 February 2020). Since the percentage of vegetation coverage was directly recorded by the VCF product, it was a qualitative product.