Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-161-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-161-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A coarse pixel-scale ground “truth” dataset based on global in situ site measurements to support validation and bias correction of satellite surface albedo products
Fei Pan
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Xiaodan Wu
CORRESPONDING AUTHOR
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
Qicheng Zeng
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Rongqi Tang
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Jingping Wang
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Xingwen Lin
College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
Dongqin You
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jianguang Wen
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Qing Xiao
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Short summary
To effectively tackle the challenges posed by spatial-scale differences and spatial heterogeneity, this paper presents a distinctive coarse pixel-scale ground “truth" dataset by upscaling sparsely distributed in situ measurements. This dataset is a valuable resource for validating and correcting global surface albedo products, enhancing reference data accuracy by 6.04 %. Remarkably, it substantially enhances 17.09 % in regions with strong spatial heterogeneity.
To effectively tackle the challenges posed by spatial-scale differences and spatial...
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