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Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China.
Zhai, Mingjian; Zhou, Xiang; Tao, Zui; Xie, Yong; Yang, Jian; Shao, Wen; Zhang, HongMing; Lv, Tingting.
Affiliation
  • Zhai M; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
  • Zhou X; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Tao Z; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Xie Y; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China. taozui@radi.ac.cn.
  • Yang J; Nanjing University of Information Science and Technology, Nanjing, 210044, China.
  • Shao W; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
  • Zhang H; Nanjing University of Information Science and Technology, Nanjing, 210044, China.
  • Lv T; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Sci Data ; 11(1): 883, 2024 Aug 14.
Article in En | MEDLINE | ID: mdl-39143120
ABSTRACT
Remote sensing technology has the potential to enhance the lake's large-scale and long-term dynamic monitoring capabilities significantly. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of lake ecosystems. This dataset provides satellite-ground synchronized in-situ data on water multi-parameters for typical lakes in China spanning the period between 2020 and 2023. It includes quality-checked water optical parameters (remote sensing reflectance (Rrs), chlorophyll-a (Chl-a), total suspended matter (TSM) and Secchi disk depth (SDD)), and water surface temperature (WST) data. It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights Firstly, synchronous observations from multiple satellites are coordinated during the data collection effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of Rrs and various parameters. This dataset will continuously update, substantially enhancing regional and global lake monitoring capabilities through satellite remote sensing data.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sci Data Year: 2024 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sci Data Year: 2024 Document type: Article Affiliation country: Country of publication: