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

Abstract. Artificial impervious surface area (ISA) documents the
human footprint. Accurate, timely, and detailed ISA datasets are therefore
essential for global climate change studies and urban planning. However, due
to the lack of sufficient training samples and operational mapping methods,
global ISA datasets at a 10 m resolution are 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 crowdsourced
OpenStreetMap (OSM) data, 58 million training samples were extracted via a
series of temporal, spatial, spectral, and geometric rules. We then produced
a 10 m resolution global ISA dataset (GISA-10m) from over 2.7 million
Sentinel optical and radar images on the Google Earth Engine platform. Based
on test samples that are independent of the training set, GISA-10m achieves
an overall accuracy of greater than 86 %. In addition, the GISA-10m
dataset was comprehensively compared with the existing global ISA datasets,
and the superiority of GISA-10m was confirmed. The global road area was
further investigated, courtesy of this 10 m dataset. It was found that China
and the US have the largest areas of ISA and road. The global rural ISA was
found to be 2.2 times that of urban while the rural road area was found to
be 1.5 times larger than that of the urban regions. The global road area
accounts for 14.2 % of the global ISA, 57.9 % of which is located in the
top 10 countries. Generally speaking, the produced GISA-10m dataset and the
proposed sampling and mapping method are able to achieve rapid and efficient
global mapping, and have the potential for detecting other land covers. It
is also shown that global ISA mapping can be improved by incorporating OSM
data. The GISA-10m dataset could be used as a fundamental parameter for
Earth system science, and will provide valuable support for urban planning
and water cycle study. The GISA-10m can be freely downloaded from
https://doi.org/10.5281/zenodo.5791855 (Huang et al., 2021a).
