Preprints
https://doi.org/10.5194/essd-2021-458
https://doi.org/10.5194/essd-2021-458
 
12 Apr 2022
12 Apr 2022
Status: this preprint is currently under review for the journal ESSD.

Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data

Xin Huang1,2, Jie Yang1, Wenrui Wang1, and Zhengrong Liu1 Xin Huang et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, P.R. China
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, P.R. China

Abstract. Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA-10m embraced an overall accuracy greater than 86 %. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated. It was found that China and the United States embraced the largest ISA and road area. The global rural ISA was 2.2 times that of urban while rural road area was 1.5 times larger than that of urban region. The global road area accounted for 14.2 % of the global ISA, 57.9 % of which was located in the top ten countries. Generally, the produced GISA-10m dataset and the proposed sampling and mapping method are able to achieve rapid and efficient global mapping, and have potential for detecting other land covers. It was also indicated that global ISA mapping can be improved by incorporating refined OSM data. GISA-10m can be used as a fundamental parameter for Earth system science, and provide valuable support for of urban planning and water cycle study. The GSIA-10m can be freely downloaded from http://doi.org/10.5281/zenodo.5791855 (Huang et al, 2021).

Xin Huang et al.

Status: open (until 07 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2021-458', Chong Liu, 17 Apr 2022 reply
  • RC1: 'Comment on essd-2021-458', Anonymous Referee #1, 03 May 2022 reply
  • RC2: 'Comment on essd-2021-458', Xian Guo, 12 May 2022 reply

Xin Huang et al.

Data sets

Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data Xin Huang; Jie Yang; Wenrui Wang; Zhengrong Liu https://zenodo.org/record/5791855

Xin Huang et al.

Viewed

Total article views: 460 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
345 102 13 460 30 5 8
  • HTML: 345
  • PDF: 102
  • XML: 13
  • Total: 460
  • Supplement: 30
  • BibTeX: 5
  • EndNote: 8
Views and downloads (calculated since 12 Apr 2022)
Cumulative views and downloads (calculated since 12 Apr 2022)

Viewed (geographical distribution)

Total article views: 414 (including HTML, PDF, and XML) Thereof 414 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 May 2022
Download
Short summary
Using more than 2.7 million Sentinel images, we proposed a global ISA mapping method and produced the 10-m global ISA dataset (GISA-10m), with overall accuracy exceeding 86 %. The inter-comparison between different global ISA datasets showed the superiority of our results. The ISA distribution at urban and rural was discussed and compared. For the first time, courtesy of the high spatial resolution, the global road ISA was further identified and its distribution was discussed.