Downscaling NLDAS-2 daily maximum air temperatures using MODIS land surface temperatures

Autoři: William L. Crosson aff001;  Mohammad Z. Al-Hamdan aff001;  Tabassum Z. Insaf aff002
Působiště autorů: Universities Space Research Association, NASA Marshall Space Flight Center, Huntsville, AL, United States of America aff001;  New York State Department of Health & University at Albany- State University of New York, Albany, NY, United States of America aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article


We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation.

Klíčová slova:

Algorithms – Earth sciences – Prisms – Public and occupational health – Remote sensing – Surface temperature – Urban areas – Surface energy


1. Atkinson PM. Downscaling in remote sensing. International Journal of Applied Earth Observation and Geoinformation 2013, 22, 106–114. doi: 10.1016/j.jag.2012.04.012

2. Zhan W, Chen Y, Zhou J, Wang J, Liu W, Voogt J. et al. Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats. Rem. Sens. Environ. 2013, 131, 119–139. doi: 10.1016/j.rse.2012.12.014

3. Borden K, Cutter SL. Spatial patterns of natural hazards mortality in the United States. Intl. J. Health Geographics 2008, 7, doi: 10.1186/1476-072X-7-64

4. Luber GE. Heat-related deaths–United States, 1999–2003. Morbidity and Mortality Weekly Report (July 28 2008), 95, 796–798.

5. Weinberger KR, Haykin L, Eliot MN, Schwartz JD, Gasparrini A, Wellenius GA. Projected temperature-related deaths in ten large U.S. metropolitan areas under different climate change scenarios. Environ Int. 2017;107:196–204. doi: 10.1016/j.envint.2017.07.006 28750225

6. Weinberger KR, Kirwa K, Eliot MN, Gold J, Suh HH, Wellenius GA. Projected changes in temperature-related morbidity and mortality in Southern New England. Epidemiology. 2018 Jul; 29(4): 473–481.

7. Al-Hamdan MZ, Crosson WL, Economou SA, Estes MG, Estes SM, Hemmings SN et al. Environmental public health applications using remotely sensed data. Geocarto International 2014, 29, 85–98. doi: 10.1080/10106049.2012.715209 24910505

8. Estes MG, Quattrochi DA, Luvall JC, Gorsevski V. Urban heat islands: Mitigation strategies for planners. American Planning Association, Planners Advisory Service Newsletter 2000.

9. Johnson D, Lulla V, Stanforth A, Webber J. Remote sensing of heat-related health risks: The trend toward coupling socioeconomic and remotely sensed data. Geography Compass 2011, 5, 767–780, doi: 10.1111/j.1749-8198.2011.00442.x

10. Owen TW, Carson TN, Gillies RR. An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. Int. J. Rem. Sens. 1998, 19, 1663–1681. doi: 10.1080/014311698215171

11. Quattrochi DA, Luvall JC, Rickman DL, Estes MG, Laymon CA, Howell B. A decision support information system for urban landscape management using thermal infrared data. Photogrammatric Engineering and Remote Sensing 2000, 66, 1195–1207.

12. Crosson WL, Laymon CA, Inguva R, Schamschula M. Assimilating remote sensing data in a surface flux-soil moisture model. Hydrol. Proc. 2002, 16, 1645–1662. doi: 10.1002/hyp.1051

13. Lakshmi V. A simple surface temperature assimilation scheme for use in land surface models. Water Resources Research 2000, 36, 3687–3700. doi: 10.1029/2000WR900204

14. Luvall JC, Quattrochi DA, Rickman DL, Estes MG. Boundary Layer (Atmospheric) and Air Pollution | Urban Heat Islands. In Encyclopedia of Atmospheric Sciences, 2nd ed., North G.R, Pyle J. Zhang F., Eds., Elsevier, 2015, pp 310–318. doi: 10.1016/B978-0-12-382225-3.00442–4

15. Liu L, Zhang Y. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing 2011, 3, 1535–1552. doi: 10.3390/rs3071535

16. Hess JJ, Eidson M, Tlumak J, Raab K, Luber G. An evidence-based public health approach to climate change adaptation. Environ. Health Perspect. 2014, 122, 1177–1186. doi: 10.1289/ehp.1307396 25003495

17. Moran MS. A window-based technique for combining Landsat Thematic Mapper thermal data with higher-resolution multispectral data over agricultural lands. Photogrammetric Engineering and Remote Sensing 1990, 56, 337–342.

18. Kustas WP, Norman JM, Anderson MC, French AN. Estimating sub-pixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote Sensing of Environment 2003, 85, 429–440. doi: 10.1016/S0034-4257(03)00036-1

19. Jeganathan C, Hamm NAS, Mukherjee S, Atkinson V, Raju PLN, Dadhwal VK. Evaluating a thermal image sharpening model over a mixed agricultural landscape in India. International Journal of Applied Earth Observation and Geoinformation 2011, 13, 178–191. doi: 10.1016/j.jag.2010.11.001

20. Essa W, van der Kwast J, Verbeiren B, Batelaan O. Downscaling of thermal images over urban areas using the land surface temperature–impervious percentage relationship. International Journal of Applied Earth Observation and Geoinformation 2013, 23, 95–108. doi: 10.1016/j.jag.2012.12.007

21. Liu D, Pu R. Downscaling thermal infrared radiance for subpixel land surface temperature retrieval. Sensors 2008, 8, 2695–2706. doi: 10.3390/s8042695 27879844

22. Liu D, Zhu X. An enhanced physical method for downscaling thermal infrared radiance. IEEE Geosci. Remote Sens. Letters 2012, 9, 690–694. doi: 10.1109/LGRS.2011.2178814

23. Zaksek K, Astir K. Downscaling land surface temperature for urban heat island diurnal cycle analysis. Rem. Sens. Environ. 2012, 117, 114–124. doi: 10.1016/j.rse.2011.05.027

24. Keramitsoglou I, Kiranoudis CT, Weng Q. Downscaling geostationary land surface temperature imagery for urban analysis. IEEE Geosci. Remote Sens. Letters 2013, 10, 1253–1257. doi: 10.1109/LGRS.2013.2257668

25. Sismanidis P, Keramitsoglou I, Kiranoudis CT, Bechtel B. Assessing the capability of a downscaled urban Land Surface Temperature time series to reproduce the spatiotemporal features of the original data. Remote Sens. 2016, 8, 274; doi: 10.3390/rs8040274

26. Bechtel B, Zaksek K, Hoshyaripour G. Downscaling land surface temperature in an urban area: A case study for Hamburg, Germany. Remote Sensing 2012, 4, 3184–3200. doi: 10.3390/rs4103184

27. Hutengs C, Vohland M. Downscaling land surface temperatures at regional scales with random forest regression. Rem. Sens. Environ. 2016 178, 127–141. doi: 10.1016/j.rse.2016.03.006

28. Bisquert M, Sánchez JM, Caselles V. Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site. IEEE J. of Selected Topics in Appl. Earth Obs. and Rem. Sens. 2016, 9, 1430–1438. doi: 10.1109/JSTARS.2016.2519099

29. Rong Y, Su H, Zhang R, Tian J, Chen S, Yang Y. et al. A new physically based method for air temperature downscaling. Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, 1814–1817. doi: 10.1109/IGARSS.2011.6049474

30. Zaksek K, Schroedter-Hornscheidt M. Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments. ISPRS Journal of Photogrammetry and Remote Sensing 2009, 64, 414–421. doi: 10.1016/j.isprsjprs.2009.02.006

31. Thornton PE, Thornton MM, Mayer BW, Wilhelmi N, Wei Y, Devarakonda R., et al. Daymet: Daily surface weather on a 1 km grid for North America, Version 3. Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center for Biogeochemical Dynamics (DAAC), 2018. Available online:

32. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, et al. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. Int. J. Climatology 2008, 28: 2031–2064. doi: 10.1002/joc.1688

33. Cosgrove BA, Lohmann D, Mitchell KE, Houser PR, Wood EF, Schaake JC, et al. Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res.-Atmospheres 2003, 108, D22, 8845, doi: 10.1029/2002JD003316

34. Rui H, Mocko D. README Document for North America Land Data Assimilation System Phase 2 (NLDAS-2) Products. Accessed December 2019.

35. Wan Z, Dozier J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci and Rem. Sens. 1996, 34. 892–905, doi: 10.1109/36.508406

36. Eliezer H, Johnson S, Crosson WL, Al-Hamdan MZ, Insaf TZ. Ground-truth of a 1 km down-scaled NLDAS air temperature product using the New York City Community Air Survey, J. of Appl. Rem. Sens. 2019, 13(2), 024516, doi: 10.1117/1.JRS.13.024516

37. Anniballe R, Bonafoni S, Pichierri M. Spatial and temporal trends of the surface and air heat island over Milan using MODIS data, Rem. Sens. Environ. 2014, 150, 163–171, doi: 10.1016/j.rse.2014.05.005

Článek vyšel v časopise


2020 Číslo 1
Nejčtenější tento týden