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New Spatial Data on U.S. Urban Extent and Global Pesticide Use Released

January 3, 2020

The NASA Socioeconomic Data and Applications Center (SEDAC) operated by CIESIN has released two new data sets, one focused on a new approach for assessing urban extent in the continental U.S. and a second that estimates the potential exposure of major food crops around the world to selected chemicals used in pesticides.

Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods is a highly accurate urban settlement layer at a spatial resolution of 500 meters that is based in part on nighttime lights data from NASA’s Black Marble project. Machine learning methods were used to provide a more consistent, quantitative measure of urban extent, drawing on observations collected at high temporal frequency by the Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing instruments. The data set was developed by former CIESIN scientist Xue Liu, now at Harvard University′s Center for Geographic Analysis, together with SEDAC deputy manager Alex de Sherbinin and former staff member Yanni Zhan. The derivation of the data set is described in a recent open access article by Liu et al. in the journal Remote Sensing.

The Global Pesticide Grids (PEST-CHEMGRIDS) data set was developed by Federico Maggi of the University of Sydney and colleagues, to assess human and ecosystem exposure to potential and recognized toxic chemicals, for the purposes of environmental modelling and assessment of agricultural chemical contamination and risk. PEST-CHEMGRIDS includes comprehensive data on the 20 most-used pesticide active ingredients, on six dominant crops and four aggregated crop classes, at 5 arc-minute resolution (about 10 kilometers at the equator), estimated for the year 2015 and projected to 2020 and 2025. The data set includes 200 data quality maps for each active ingredient on each crop. The data set is described in detail in a recent open access paper by Maggi et al. published in the journal Scientific Data. 

These data are distributed as part of SEDAC′s mission to archive and disseminate key socioeconomic and related environmental data sets that either utilize or complement satellite-based remote sensing data, in support of scientific research, applications, and education. Data selection is overseen by SEDAC′s User Working Group (UWG). Data set authors are invited to submit their data for possible SEDAC archiving and open dissemination; for the submission criteria and form, please see the SEDAC Data Submission page.

See: Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods
       Global Pesticide Grids (PEST-CHEMGRIDS), v1 (2015, 2020, 2025)