A Comparison of Recent Global Time-Series Land Cover Products - Peilin Li; Yan Wang; Chisheng Wang; Lin Tian; Meijiao Lin; Siyao Xu; Chuanhua Zhu (Remote Sensing)
Accurate and reliable land cover data are essential for environmental monitoring, climate research, and sustainable land management. However, the proliferation of multi-source global land cover datasets with long time series poses challenges for selecting the best products for specific applications. Existing assessments often lack systematic comparisons of classification accuracy and time consistency across geographic areas. This study addresses the critical gap in cross-product comparability by systematically comparing five recent global time-series land cover products (GLC_FCS30D, Esri Land Cover, MCD12Q1, ESA CCI, and Dynamic World) against a reference dataset (CGLS-LC100). Through a unified classification system, resolution resampling, and random sampling validation, we assessed their classification accuracy and time-series change accuracy across three transitional regions representing diverse environmental contexts: rapidly urbanizing regions, agriculturally intensive zones, and high-latitude forested areas. The results indicate that while datasets exhibit spatial consistency, significant discrepancies exist in land cover classification, with each dataset demonstrating varying levels of accuracy depending on the environmental context and land cover type. High-resolution products (e.g., GLC_FCS30D, Dynamic World) are optimal for monitoring fragmented landscapes and urban expansion, whereas long-term datasets (e.g., ESA CCI, MCD12Q1) suit climate trend analysis in stable ecosystems. Based on the evaluation, we provide generalized guidance for dataset selection aligned with land cover types and monitoring objectives, emphasizing the need for region-specific and application-oriented choices. This study highlights challenges in dynamic datasets, including classification system discrepancies, resolution effects, and reference data limitations, and suggests that future advancements should focus on improving classification algorithms, refining sampling methods, and developing assessment systems that incorporate high-precision, real-time validation data.