Assessing Consistency of Five Global Land Cover Data Sets in China

TLDR

Global land cover mapping is crucial for research, yet five widely used datasets differ in data sources and class definitions, causing high uncertainty. This study examined the consistency and discrepancies among these five datasets within China. The datasets were resampled to a common resolution and 12‑class scheme, then compared pairwise and against the GLCD‑2005 reference. Agreement was good for dominant classes such as grassland and cropland, but forest classes, especially shrubland and wetland, varied markedly; GLC2005 best matched GLCD‑2005, MODIS LC showed highest map‑specific consistency, while UMd had the lowest agreement and consistency.

Abstract

Global land cover mapping with high accuracy is essential to downstream researches. Five global land cover data sets derived from moderate-resolution satellites, i.e., Global Land Cover Characterization (GLCC), University of Maryland land cover product (UMd), Global Land Cover 2000 project data (GLC2000), MODIS Land Cover product (MODIS LC), and GLOBCOVER land cover product (GlobCover), have been widely used in many researches. However, these data sets were produced using different data sources and class definitions, which led to high uncertainty and inconsistency when using them. This study looked into the consistencies and discrepancies among the five data sets in China. All of the compared data sets were aggregated to consistent spatial resolution and extent, along with a 12-class thematic classification schema; intercomparisons among five datasets and each with reference data GLCD-2005 were performed. Results show reasonable agreement across the five data sets over China in terms of the dominating land cover types like Grassland and Cropland; while discrepancies of Forest classes, particularly Shrubland and Wetland among them are great. Additionally, GLC2000 has the highest agreement with GLCD-2005; MODIS LC gets the highest map-specific consistency compared with others; whereas UMd has the lowest agreement with GLCD-2005, but also has the lowest map-specific consistency.