Preprints
https://doi.org/10.5194/essd-2021-189
https://doi.org/10.5194/essd-2021-189

  01 Jul 2021

01 Jul 2021

Review status: this preprint is currently under review for the journal ESSD.

The NIEER AVHRR snow cover extent product over China – A long-term daily snow record for regional climate research

Xiaohua Hao1,2, Guanghui Huang3, Tao Che1,2, Wenzheng Ji1, Xingliang Sun1,4, Qin Zhao1, Hongyu Zhao1, Jian Wang1,2, Hongyi Li1,2, and Qian Yang5 Xiaohua Hao et al.
  • 1Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • 2Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • 3College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • 4Engineering Laboratory for National Geographic State Monitoring, Lanzhou Jiaotong University, Lanzhou 730070, China
  • 5School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China

Abstract. Using the Google Earth Engine (GEE) platform, a long-term AVHRR snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The new NIEER product has the spatial resolution of 5-km and the daily temporal resolution, and is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination and gap-filling. A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer’s accuracy was 81.0 % the user’s accuracy was 81.3 %, and the Cohen’s kappa value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 89.4 %, a producer’s accuracy of 90.2 %, a user’s accuracy of 96.1 %, and a Cohen’s kappa value of 0.713. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 percent, the omission error dropped from nearly 40 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is now already available at https://dx.doi.org/10.11888/Snow.tpdc.271381 (Hao et al. 2021).

Xiaohua Hao et al.

Status: open (until 26 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-189', Anonymous Referee #1, 26 Jul 2021 reply

Xiaohua Hao et al.

Data sets

Daily 5-km Gap-free AVHRR snow cover extent product over China (1981-2019) Xiaohua Hao, Wenzheng Ji, Qin Zhao, Xingliang Sun, Jian Wang, Honyi Li, Hongyu Zhao https://dx.doi.org/10.11888/Snow.tpdc.271381

Xiaohua Hao et al.

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Short summary
Long-term snow cover data is not only particularly of importance for climate research. Currently, China still lacks highly quality SCE product for climate research. This study develops a multi-level decision tree algorithm for cloud and snow discrimination and gap-filled technique based on AVHRR SR data by GEE. We generate a daily 5 km NIEER AVHRR SCE product across China from 1981 to 2019. The product has high accuracy and will serve as baseline data for climate and other related applications.