Abstract. A classification method with multi-temporal images of synthetic aperture radar (SAR) combined with Geographic information system, geoinformation data, and field validation, was applied for wetland mapping accuracy and typology. Wetland mapping is vital for management and conservation, particularly under environmental pressures such as wetland drainage and land reclamation. The aim of this study is to develop an accurate mapping of wetlands and open water systems of the Lower Doce River Valley - LDRV (Southeastern Brazil) with Synthetic Aperture Radar (SAR) imagery, using multitemporal classification techniques and ground truth validation. Sentinel-1B SAR imagery from 2016 and 2019 was processed with Google Earth Engine (GEE). Monthly median imagery condition for the rainy season was obtained and K-means unsupervised classification was applied. The study yields 4,157 wetlands, 262.27 km2 with predominant small patches. Fieldwork revealed three main wetlands categories: coastal wetlands, inland wetlands and artificial wetlands. The results have shown an overall accuracy of 81.9% and a Kappa coefficient of 0.71. Wetlands, non-wetlands, and open waters classes present accuracy of 50, 80 and 95%, respectively.

NO2 is a mainly anthropogenic gas that affects population health and its exposure is associated with several respiratory diseases. Its tropospheric concentration is associated with vehicle emissions. During 2020, COVID-19 lockdowns have impeded population's mobility, hence constructing an almost ideal situation to study their relationship with tropospheric NO2 concentration. We used TROPOMI satellite images, Google mobility reports and vehicule count in order to study these relationships in six big Latin American metropolitan areas. In all of them, tropospheric NO2 concentration decreased during 2020 compared to 2019, particularly during April 2020. The daily vehicle count in Buenos Aires was a significantly important variable in order to explain NO2 concentration variations. This study strengthens previous research findings about NO2 concentration reduction during COVID-19 lockdowns and shows the relationship between human mobility and air pollution in the particular context of Latin America big cities.

NO2 is a mainly anthropogenic gas that affects population health and its exposure is associated with several respiratory diseases. Its tropospheric concentration is associated with vehicle emissions. During 2020, COVID-19 lockdowns have impeded populations mobility, hence constructing an almost ideal situation to study their relationship with tropospheric NO2 concentration. We used TROPOMI (TROPOspheric Monitoring Instrument) satellite images, Google mobility reports and vehicule count in order to study these relationships in six big Latin American metropolitan areas: Mexico DF, Sao Paulo, Buenos Aires, Rio de Janeiro, Lima and Bogota. In all of them, tropospheric NO2 concentration decreased during 2020 compared to 2019, particularly during April 2020. Temperature differences alone could not explain the NO2 concentration differences between February and April 2020. The daily vehicle count in Buenos Aires was a significantly important variable in order to explain NO2 concentration variations (p < 0.001) and it could be replaced by the daily Googles residential variation without significant information loss (p~1). This study strengthens previous research findings about NO2 concentration reduction during COVID-19 lockdowns and shows the relationship between human mobility and air pollution in the particular context of Latin America big cities.

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