### HOW THE GLOBAL REFLECTIVITY MAP WORKS
Reflective Earth uses satellite images to calculate how much sunlight is reflected back to space from the selected area and how much could be reflected if the surface were brightened. Explore multiple areas to understand the reflectivity of different surfaces nearby.
### ACTIONS YOU CAN TAKE TO MAKE A DIFFERENCE
You can reduce heat in your building and substantially cut your cooling energy bills by increasing the reflectivity of your building and surrounding areas. You can also help reduce death and illness from extreme heat by advocating for changes in a neighborhood or a city’s reflectivity.
### DOWNLOAD THE DATA
After you designate all of the computer screen area or selected 10 meter square pixels, you can download the data for analysis or to share with your neighbors, with property owners, and with local officials. Ask them to join you in taking action to make outdoor surfaces more reflective and make your community more resilient to extreme heat.
About the Reflectivity Optimization map
The Reflectivity Map shows measurements of surface reflectivity (%) and potential surface-reflected outgoing solar radiation (watts per square meter). The surface reflectivity measurements are based on Sentinel-2 satellite images from the European Space Agency (ESA) and the potential surface-reflected outgoing solar radiation is based on atmospheric data from the European Center for Medium Range Weather Forecasts (ECMWF). The map and underlying data were developed under the leadership of Michel Gelobter by the Reflective Earth team in partnership with the World Resources Institute and the Cool Roof team at Google.
About the Data
This map shows four products: a potential surface-reflected outsolation [irradiance](https://glossary.ametsoc.org/wiki/Irradiance) (W/m2) at 30 km resolution, a surface reflectivity at 10 m resolution, a present outsolation irradiance (W/m2), and Mapbox Earth images at varying resolutions. Here we define "outsolation" as outgoing solar radiation at the top of the atmosphere.
The potential outsolation values are the annually averaged irradiances that would be reflected from the surface to space by an extreme reflectivity intervention (i.e. changing surface reflectivity to 1.0) ([Smoliak et al. 2022)](https://iopscience.iop.org/article/10.1088/2515-7620/ac7a25). The potential values are based on a simple radiative transfer model coupled with a surface driven by 30 years of hourly [ERA5 reanalysis data](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) from the European Centre for Medium-range Weather Forecasts Copernicus Climate Change Service with a horizontal resolution of about 30 km. Surfaces are assumed to be perfectly [Lambertian](https://glossary.ametsoc.org/wiki/Lambertian_surface) (reflecting light equally in all directions), which will cause an underestimate of surface reflectivity for some [specular surfaces.](https://glossary.ametsoc.org/wiki/Specular_reflection)
Surface reflectivity as displayed on the map is computed from Sentinel-2 multi-spectral satellite images using a narrow-to-broadband reflectance conversion ([Bonafoni and Sekertekin,2020)](https://ieeexplore.ieee.org/document/8974188). The data is provided by the European Space Agency via Google Earth Engine. Data values represent weighted averages of surface reflectance in six spectral [bands](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial) (B2, B3, B4, B8, B11, B12) during ”cloud-free” conditions over the past 12 calendar months. For this map, "cloud-free" is when the Sentinel-2 cloud occurrence probability is less than 10%. If fewer than 10 images are available over the past 12 calendar months for any pixel, the algorithm raises the maximum acceptability cloud probability to 30% for that pixel. Some bleed over of reflectivity values to adjoining pixels occurs due to orthorectification of images, the mismatch of data resolution, and coarse spatial representation of fine surface features.
The outsolation irradiance product reported here is the annually averaged irradiance currently reflected from the surface to space. It is the product of the estimated outsolation potentials and the surface reflectivities.
The underlying visible images are drawn from [Mapbox](https://www.mapbox.com/). Users should be aware that Mapbox Earth images are mosaics of moments throughout the year using different images for each zoom level. In many cases, our annually averaged potential and reflectivity values will be congruent with how the surface appears in Mapbox images. However, many parts of the world experience large changes in surface reflectivity due to seasonal vegetation changes, snow cover, reservoir water levels, land use changes, and so forth. In these cases, the annually averaged potential and reflectivity values will seem incongruous with the displayed image.
### A Note on specular reflection:
Our annually-averaged reflectivity is the among the best available estimates of surface reflectance for most surfaces under clear skies. Smooth surfaces like water on a calm day will have a [specular reflection](https://glossary.ametsoc.org/wiki/Specular_reflection), which will cause our reflectivity to be an underestimate. Most man-made materials of interest, such as non-metallic roofing materials, paints, and shade fabrics, are sufficiently Lambertian that the “reflectivity” does not significantly change with elevation or with latitude. Thus, we sometimes refer to solar “reflectivity” as an invariable property of a man-made material. There are some man-made materials with significant specular reflection for which the reflectivity changes significantly with latitude and tilt angle and those are addressed as special cases. Mirrored surfaces are the most extreme example.
Better water reflectance retrievals are under development. For determining reflectivity of any water surface, we suggest you use a reference such as Cogley JG, The Albedo of Water as a Function of Latitude, American Meteorological Society, 01 Jun 1979, doi:10.1175/1520-0493(1979)107.
About the Data