GLOBAL ESTIMATES OF URBAN SURFACE ALBEDO TIME SERIES WITH THE USE OF CLOUD COMPUTING N. Benas1, Z. Mitraka1, N. Chrysoulakis1, M. Marconcini2, T. Esch2 and G. Lantzanakis1

1Founda t ion fo r Re sear c h and Tec hno logy He l la s ( FORTH) , Greece, con tac t : benas@iacm. fo r t h .g r 2Ger man Aero space Cen te r (DLR ) , Ger many

MUAS 2015 | 4-5 November 2015, Frascati , I taly

ABSTRACT

RESULTS

REFERENCES

The Land Surface Albedo (LSA) is a critical physical variablewhich influences the Earth’s climate by affecting the energytransfer and distribution in the Earth-atmosphere system. Itsrole is highly significant in global and local scales, since LSAmeasurements provide a quantitative means for betterconstraining climate modelling efforts. In urban environments,LSA is crucial for the estimation of the local scale radiation andenergy budget.

In the present study, LSA was estimated in large urban areasglobally, at 0.5 km × 0.5 km spatial resolution and on an 8–daybasis, for the period 2001 - 2014. Products from the ModerateResolution Imaging Spectroradiometer (MODIS), on boardNASA’s Terra and Aqua satellites were used. Urban areas weremasked using the Global Urban Footprint (GUF) layer and LSAchanges during the period examined were assessed usinglinear regression.

All computations for albedo estimationwere performed usingthe Google Earth Engine (GEE) platform and the data availablein its catalog. GEE is a cloud computing system designed toenable petabyte-scale scientific analysis and visualization ofgeospatial datasets, which made possible LSA estimation atglobal scale for a 14-year period (2000-2014). Albedoproducts for the urban areas were then processed locally forthe statistical analysis.

MODIS data

Combined Terra and Aqua data, 8-day temporal average, at 0.5 × 0.5 kmspatial resolution

Include kernel weights (fiso, fvol, fgeo) for the computation of black andwhite sky albedo:

MODIS Level 3 AOT (550 nm), 8-day temporal average, at 1° × 1° spatialresolution

Global Urban Footprint data (DLR)

built-up areas derived from TerraSAR-X and TanDEM-X

Percentage of built-up areas at 100 m × 100 m spatial resolution

EARTH OBSERVATION DATA URBAN AREAS

METHODOLOGY

Case study: Rome

14-year average LSA

LSA trend, 2001-2014 Urban LSA trend, 2001-2014

Urban Area (GUF data)

Global trends in Urban Surface Albedo

Num

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of

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Urban LSA trends (%)

Global estimates of blue-sky albedo for 2000 - 20015 of 0.5 km × 0.5 km spatial resolution

Varying urban LSA trends in different world regions and cities

Potential causing factors, including NDVI changes and urban sprawl, are under investigation

Earth Observation data in combination with the power of cloud computing can support studies of urban

climate and energy budget

Benas, N. and Chrysoulakis, N., 2015. Estimation of the land surface albedo changes in the broader

Mediterranean area, based on 12 years satellite observations. Remote Sensing (in press).

Esch, T., Marconcini, M., Felbier, A., Roth, A., Heldens, W., Huber, M., Schwinger, M., Taubenbock, H., Muller,

A. and Dech, S., 2013. Urban Footprint Processor – Fully Automated Processing Chain Generating

Settlement Masks from Global Data of the TanDEM-X Mission. IEEE Geoscience and Remote Sensing Letters,

10, 1617 - 1621.

Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S. and Martonchik, J. V., 2013. Reflectance

quantities in optical remote sensing-definitions and case studies. Remote Sensing of Environment, 103, 27 -

42.

Surface Albedo Estimation

LSA 𝜃, 𝐿 = 1 − S 𝜃, 𝜏 𝜆 𝑎𝑏𝑠 𝜃, 𝜆 + S 𝜃, 𝜏 𝜆 𝑎𝑤𝑠 𝜃, 𝜆

𝜃: solar zenith angle𝜆: wavelength𝜏: aerosol optical thickness𝑆: fraction of diffuse skylight𝑎𝑏𝑠: black sky albedo𝑎𝑤𝑠: white sky albedo

Google Earth EngineLSA annual average estimations performed in GEE | Large computational power allowed hourly SZAs variations

Trends in Surface AlbedoEstimation of annual average LSA based on 8-day values | Linear regression analysis based on annual valuesduring 2001 – 2014 | Assessment of statistically significant LSA trends

Selection of Urban Areas

Population criteria ( > 4 million in 2010) | Selection of circular areas (25 km radius) based on city center scoordinates | GUF upscaled at 0.5 × 0.5 km Urban pixels: GUF ≠ 0

LSA averaged over multiple SZAs

CONCLUSIONS