China Earth Observation Data Cube: The 30-m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023
Yaotong Cai, X. Li, Peng Zhu, Sheng Nie, Cheng Wang, Xiaoping Liu, Yuhe Chen
Abstract
The growing demand for high-quality, temporally consistent satellite imagery for environmental monitoring and land use research has exposed a substantial data gap in China. Unlike the United States, which provides Analysis Ready Data (ARD) for Landsat imagery, Chinese researchers currently lack an equivalent resource, resulting in time-intensive data processing and potential research inaccuracies. In this study, we introduce the first seamless, annual Leaf-On Landsat composite data cube for China, covering 1985 to 2023. Leveraging the comprehensive image compositing approach, our dataset harmonizes images across multiple Landsat sensors and addresses key challenges such as cloud and shadow contamination, reflectance consistency, and the data gaps. Over this period, an average of 7.9% of data remained unavailable due to cloud/shadow cover and limited data accessibility. To address this, we applied segmented linear interpolation to generate proxies, which we validated for stability, achieving high consistency with actual Landsat references for both stable and dynamic pixel sequences ( r = 0.77 to 0.99, root mean square error [RMSE] = 0.0043 to 0.0232). Additionally, representativeness assessments indicate a strong correlation between our composites and Landsat reference images (closest to day of year 225) ( r = 0.75 to 0.94, RMSE = 0.025 to 0.063), confirming that these composites effectively capture seasonal vegetation conditions across diverse land cover types. This dataset is expected to help reduce preprocessing efforts for researchers and provide a solid basis for land use monitoring and environmental assessments across China.