Developed by Yaotong Cai and colleagues at the Aerospace Information Research Institute (AIRCAS), the dataset addresses longstanding challenges in satellite data processing, including cloud contamination, sensor discrepancies, and data gaps. Unlike the United States, which has long had preprocessed Landsat Analysis Ready Data (ARD), Chinese researchers lacked an equivalent resource - until now.
"This dataset is a significant breakthrough for environmental monitoring in China. It not only simplifies satellite data processing but also provides a long-term resource for research on land use, climate change, and biodiversity conservation," said Dr. Cai. "Our methodology offers a robust solution for handling the challenges posed by cloud cover and sensor inconsistencies, and we hope it will drive future research."
The data cube was created using imagery from Landsat 4/5, 7, and 8/9, processed uniformly through Google Earth Engine. Surface reflectance values were harmonized across sensors using the Land Surface Reflectance Code (LaSRC). The researchers applied quality assessment bands to remove cloud and shadow artifacts, and used a medoid compositing technique to select the most representative pixel each year, preserving spectral integrity and minimizing noise.
To fill data gaps caused by persistent cloud cover or sensor failures, the team employed segmented linear interpolation. The resulting composite captures vegetation dynamics across China with high temporal and spectral consistency, making it a valuable tool for monitoring forest changes, evaluating climate impacts, and supporting land use policy development.
The dataset's reliability was validated through statistical correlation analyses. Future improvements aim to enhance cloud and shadow masking techniques, incorporate data from additional satellites, and expand coverage to include leaf-off periods - boosting its utility for both regional and global climate studies.
Research Report:China Earth Observation Data Cube: The 30-m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023
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