How to map soil sealing, land take and impervious surfaces? A systematic review

Soil degradation is one of the main environmental issues within the international agendas on sustainability and climate adaptation. Among degradation processes, soil sealing represents the major threat, as ecosystem services dramatically decrease or are even nullified. The increasing use of big open data from satellites combined with AI algorithms are making geodata mining and mapping techniques essential to quantify soil sealing. Different keywords are adopted to define the phenomenon. However, at present, review articles presenting the state-of-the-art on mapping soil sealing by including the most common definitions are currently not available. Hence, we analyzed: (a) impervious surface, (b) soil sealing, (c) land take, (d) soil consumption, (e) land consumption. We provide a systematic review of remote sensing platforms and methodologies to map and to classify soil sealing, by highlighting: (a) definitions; (b) relationships among study areas, scales, platforms, resolutions, and classification methodologies; (c) emerging trends and policy implications. We performed a systematic search on Scopus (from 2000 to 2020), identifying 1277 papers; 392 focused on mapping soil sealing. 'Impervious surface' is the dominant definition. The phenomenon is more studied by the USA, China and Italy and, 'soil sealing' is recently more adopted in EU. Most studies focuses on mapping soil sealing at urban scale. We found Landsat are the most adopted platforms; they are frequently used for multi-temporal analyses. Eleven methodologies were identified: automatic classifications are the most adopted, dominated by pixel/sub-pixel-based approaches; other methods include Band Ratios, Supervised, OBIA, ANN. The majority of mapping analyses are performed on 30 m resolution in areas of 1000–10 000 km2. Landsat images are less used for smaller areas. In conclusion, as study area size increases, a decrease in image resolution with the use of more completely automatic classification methodologies is recorded. However, most studies focuses on comparing classification techniques rather than supporting policy making for sustainable urban planning. Thus, we encourage to fill the gap by developing approaches that applicable to international policies.