School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China
Rui Peng
School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China
Kai Yang
School of Land Science and Techniques, China University of Geosciences, Beijing 100083, China
Xiuzhi Chen
Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 519082, China
Gaofei Yin
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
Jinwei Dong
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Marie Weiss
Institute National de la Recherche Agronomique, Université d'Avignon et des Pays du Vaucluse (INRA-UAPV), 228 Route de l'Aérodrome, 84914 Avignon, France
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area...
