Human population is rapidly urbanizing, with consequences for resource consumption, energy use and greenhouse gas emissions, and ecosystem and public health. Predicting global urbanization's impact…

This federal contract award, with ID 80NSSC18K1434, was granted to the University System of New Hampshire (USNH), through its University of New Hampshire (UNH) division operating as the Office of Sponsored Research. The $500,002.56 grant is for research focused on using microwave backscatter (scatterometer) data to quantify and analyze global urbanization trends and their impacts. The work will involve relating scatterometer data to other remote sensing and urban population datasets to create a comprehensive multi-sensor database for urban land surface classification and climate studies. This research effort builds on prior work using QuikSCAT backscatter data. The award is not designated as a set-aside contract and is being funded by the National Aeronautics and Space Administration (NASA). As a non-profit, 1862 land grant educational institution, UNH is well-positioned to perform the R&D, technical assistance, and STEM education programming required under this grant.

Human population is rapidly urbanizing, with consequences for resource consumption, energy use and greenhouse gas emissions, and ecosystem and public health. Predicting global urbanization's impact requires spatio-temporal quantification of urban rates of change with globally consistent, well-calibrated data. Microwave backscatter (scatterometer) data provide a distinctive perspective on the earth's surface, complementing other remote sensing data to create a more comprehensive picture of the earth system. Recent work has shown that quikscat backscatter data provides important, unique information on global urbanization. We will extend and expand upon our recent urban growth research, using qscat and other scatterometer data (KU-band: oscat, rapidscat and scatsat; c-band: ERS-1/2 ascat-a/b). We will analyze urban backscatter data for temporal trends, geographical and developmental patterns. Urban backscatter data will be empirically related to non-microwave urban remote sensing data (e.g., dmsp/OLS nighttime lights, landsat, modis, viirs day-night band), as well as urban population growth data. This multisensor, multi-decadal time series of urban backscatter in conjunction with other data will provide the basis for developing a globallyconsistent urban land surface classification. This multisensory database will also be empirically related to ground-based urban structure data, or characterization for urban climate studies and urban metabolism studies. We will continue to use the extensive scatterometer data collection generated by the NASA scatterometer climate pathfinder project at brigham young university, allowing US to focus on interdisciplinary applications of the scatterometer data, rather than (necessary) raw data processing.