An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes

Remote Sensing of Environment

Volumn 206, Issue , 2018, Pages 403-423

  Quan, Jinling ^a,b   Zhan, Wenfeng ^c   Ma, Ting ^a,b   Du, Yunyan ^a,b   Guo, Zheng ^d   Qin, Bangyong ^e  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c NANJING UNIVERSITY   (China)

^d NATIONAL SATELLITE METEOROLOGICAL CENTER   (China)

^e Chinese Academy of Sciences   (China)

Author keywords

Data fusion; Geostationary satellite; Heterogeneity; Land surface temperature; Landsat; MODIS

Indexed keywords

ATMOSPHERIC TEMPERATURE; DATA FUSION; ECONOMIC AND SOCIAL EFFECTS; GEOSTATIONARY SATELLITES; INTERPOLATION; LAND SURFACE TEMPERATURE; RADIOMETERS; REMOTE SENSING; SURFACE MEASUREMENT; SURFACE PROPERTIES;

FINE RESOLUTION; FUSION MODEL; HETEROGENEITY; HETEROGENEOUS LANDSCAPES; INTEGRATED MODELING; LAND SURFACE TEMPERATURE; LAND-COVER TYPES; LANDSAT; MODIS; TEMPORAL ADAPTIVE;

LANDSAT;

ACCURACY ASSESSMENT; GEOSTATIONARY SATELLITE; HETEROGENEITY; INTEGRATED APPROACH; LAND COVER; LAND SURFACE; LANDSAT; MODIS; PREDICTION; REMOTE SENSING; SATELLITE DATA; SPATIOTEMPORAL ANALYSIS; SURFACE TEMPERATURE;

EID: 85044377586     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.12.003     Document Type: Article

References (70)

1. Aharon, M., Elad, M., Bruckstein, A., An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54 (2006), 4311–4322.
2. Amorós-López, J., Gómez-Chova, L., Alonso, L., Guanter, L., Zurita-Milla, R., Moreno, J., Camps-Valls, G., Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. Int. J. Appl. Earth Obs. Geoinf. 23 (2013), 132–141.
3. Anderson, M.C., Kustas, W.P., Alfieri, J.G., Gao, F., Hain, C., Prueger, J.H., Evett, S., Colaizzi, P., Howell, T., Chávez, J.L., Mapping daily evapotranspiration at Adv. Water Resour.spatial scales during the BEAREX’08 field campaign. Adv. Water Resour. 50 (2012), 162–177.
4. Anselin, L., Local indicators of spatial association—LISA. Geogr. Anal. 27 (1995), 93–115.
5. Bechtel, B., Zakšek, K., Hoshyaripour, G., Downscaling land surface temperature in an urban area: a case study for Hamburg, Germany. Remote Sens. 4 (2012), 3184–3200.
6. Becker, F., Li, Z.L., Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote Sens. Rev. 12 (1995), 225–253.
7. Bisquert, M., Sánchez, J.M., Caselles, V., Evaluation of disaggregation methods for downscaling modis land surface temperature to landsat spatial resolution in Barrax test site. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 9 (2016), 1430–1438.
8. Bisquert, M., Sánchez, J.M., López-Urrea, R., Caselles, V., Estimating high resolution evapotranspiration from disaggregated thermal images. Remote Sens. Environ. 187 (2016), 423–433.
9. Chen, X., Li, W., Chen, J., Rao, Y., Yamaguchi, Y., A combination of TsHARP and thin plate spline interpolation for spatial sharpening of thermal imagery. Remote Sens., 6, 2014, 2845.
10. Chen, Y.H., Zhan, W.F., Quan, J.L., Zhou, J., Zhu, X.L., Sun, H., Disaggregation of remotely sensed land surface temperature: a generalized paradigm. IEEE Trans. Geosci. Remote Sens. 52 (2014), 5952–5965.
11. Chen, B., Huang, B., Xu, B., A hierarchical spatiotemporal adaptive fusion model using one image pair. Int. J. Digital Earth, 2016, 1–17.
12. China Meteorological Administration, Ground Meteorological Observation Criterion. 2003, China Meteorological Press, Beijing.
13. Deng, C.B., Wu, C.S., Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sens. Environ. 131 (2013), 262–274.
14. Duan, S.B., Li, Z.L., Wang, N., Wu, H., Tang, B.H., Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data. Remote Sens. Environ. 124 (2012), 15–25.
15. Emelyanova, I.V., McVicar, T.R., Van Niel, T.G., Li, L.T., van Dijk, A.I.J.M., Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: a framework for algorithm selection. Remote Sens. Environ. 133 (2013), 193–209.
16. Ermida, S.L., Trigo, I.F., DaCamara, C.C., Göttsche, F.M., Olesen, F.S., Hulley, G., Validation of remotely sensed surface temperature over an oak woodland landscape: the problem of viewing and illumination geometries. Remote Sens. Environ. 148 (2014), 16–27.
17. Ermida, S.L., DaCamara, C.C., Trigo, I.F., Pires, A.C., Ghent, D., Remedios, J., Modelling directional effects on remotely sensed land surface temperature. Remote Sens. Environ. 190 (2017), 56–69.
18. Gao, F., Jessup, M.D., Masek, J., Schwaller, M., Hall, F., On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44 (2006), 2207–2218.
19. Gao, F., Kustas, P.W., Anderson, C.M., A data mining approach for sharpening thermal satellite imagery over land. Remote Sens. 4 (2012), 3287–3319.
20. Gevaert, C.M., García-Haro, F.J., A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sens. Environ. 156 (2015), 34–44.
21. Guillevic, P.C., Privette, J.L., Coudert, B., Palecki, M.A., Demarty, J., Ottlé C., Augustine, J.A., Land surface temperature product validation using NOAA's surface climate observation networks: scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS). Remote Sens. Environ. 124 (2012), 282–298.
22. Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J.G., Gao, F., White, J.C., A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens. Environ. 113 (2009), 1613–1627.
23. Hu, J.Y., The Study of Algorithms for Land Surface Temperature Retrieval from Feng Yun Satellite Data. 2012, Master's thesis, Chinese Academy of Meteorological Sciences, China.
24. Huang, B., Song, H., Spatiotemporal reflectance fusion via sparse representation. IEEE Trans. Geosci. Remote Sens. 50 (2012), 3707–3716.
25. Huang, B., Wang, J., Song, H., Fu, D., Wong, K., Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring. IEEE Geosci. Remote Sens. Lett. 10 (2013), 1011–1015.
26. Huang, B., Zhang, H., Song, H., Wang, J., Song, C., Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial–temporal–spectral earth observations. Remote Sens. Lett. 4 (2013), 561–569.
27. Huang, F., Zhan, W., Duan, S., Ju, W., Quan, J., A generic framework for modeling diurnal land surface temperatures with remotely sensed thermal observations under clear sky. Remote Sens. Environ. 150 (2014), 140–151.
28. Inamdar, A.K., French, A., Hook, S., Vaughan, G., Luckett, W., Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States. J. Geophys. Res.-Atmos., 113, 2008, D07107.
29. Jiménez-Muñoz, J.C., Sobrino, J.A., Skokovic, D., Mattar, C., Cristóbal, J., Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci. Remote Sens. Lett. 11 (2014), 1840–1843.
30. Kim, J., Hogue, T.S., Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions. J. Appl. Remote. Sens., 6, 2012, 063569.
31. Kumar, K., Bhaskar, P., Padmakumari, K., Estimation of land surface temperature to study urban heat island effect using landsat ETM + image. Int. J. Eng. Sci. Technol. 4 (2012), 771–778.
32. Kustas, W.P., Norman, J.M., Anderson, M.C., French, A.N., Estimating subpixel surface temperatures and energy fluxes from the vegetation index radiometric temperature relationship. Remote Sens. Environ. 85 (2003), 429–440.
33. Li, Z.L., Tang, B.H., Wu, H., Ren, H.Z., Yan, G.J., Wan, Z.M., Trigo, I.F., Sobrino, J.A., Satellite-derived land surface temperature: current status and perspectives. Remote Sens. Environ. 131 (2013), 14–37.
34. Meng, X., Shen, H., Zhang, L., Yuan, Q., Li, H., A Unified Framework for Spatio–Temporal–Spectral Fusion of Remote Sensing Images. 2015, IEEE IGARSS, Milan, Italy, 2584–2587.
35. Prata, A.J., Caselles, V., Coll, C., Sobrino, J.A., Ottlé C., Thermal remote sensing of land surface temperature from satellites: current status and future prospects. Remote Sens. Rev. 12 (1995), 175–224.
36. Quan, J.L., Zhan, W.F., Chen, Y.H., Liu, W.Y., Downscaling remotely sensed land surface temperatures: a comparison of typical methods. J. Remote. Sens. 17 (2013), 363–389.
37. Quan, J.L., Chen, Y.H., Zhan, W.F., Wang, J.F., Voogt, J., Li, J., A hybrid method combining neighborhood information from satellite data with modeled diurnal temperature cycles over consecutive days. Remote Sens. Environ. 155 (2014), 257–274.
38. Quan, J.L., Chen, Y.H., Zhan, W.F., Wang, J.F., Voogt, J., Wang, M.J., Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model. Remote Sens. Environ. 149 (2014), 33–46.
39. Quan, J.L., Zhan, W.F., Chen, Y.H., Wang, M., Wang, J.F., Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands. J. Geophys. Res.-Atmos. 121 (2016), 2638–2657.
40. Ren, H., Du, C., Liu, R., Qin, Q., Yan, G., Li, Z., Meng, J., Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. J. Geophys. Res.-Atmos. 120 (2015), 1723–1738.
41. Schädlich, S., Göttsche, F.M., Olesen, F.S., Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sens. Environ. 75 (2001), 39–46.
42. Shen, H., Wu, P., Liu, Y., Ai, T., Wang, Y., Liu, X., A spatial and temporal reflectance fusion model considering sensor observation differences. Int. J. Remote Sens. 34 (2013), 4367–4383.
43. Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., Zhang, L., Missing information reconstruction of remote sensing data: a technical review. IEEE Geosci. Rem. Sens. Magazine 3 (2015), 61–85.
44. Shen, H., Huang, L., Zhang, L., Wu, P., Zeng, C., Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: a 26-year case study of the city of Wuhan in China. Remote Sens. Environ. 172 (2016), 109–125.
45. Shen, H., Meng, X., Zhang, L., An integrated framework for the spatio-temporal-spectral fusion of remote sensing images. IEEE Trans. Geosci. Remote Sens. 54 (2016), 7135–7148.
46. Sismanidis, P., Keramitsoglou, I., Kiranoudis, C.T., A satellite-based system for continuous monitoring of surface urban heat islands. Urban Climate 14:Part 2 (2015), 141–153.
47. Sismanidis, P., Keramitsoglou, I., Kiranoudis, T.C., 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., 8, 2016, 274.
48. Sobrino, J.A., Oltra-Carrió R., Sòria, G., Bianchi, R., Paganini, M., Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sens. Environ. 117 (2012), 50–56.
49. Song, H., Huang, B., Spatiotemporal satellite image fusion through one-pair image learning. IEEE Trans. Geosci. Remote Sens. 51 (2013), 1883–1896.
50. Wan, Z.M., New refinements and validation of the collection-6 MODIS landsurface temperature/emissivity product. Remote Sens. Environ. 140 (2014), 36–45.
51. Wan, Z.M., Dozier, J., A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 34 (1996), 892–905.
52. Wang, P., Gao, F., Masek, J.G., Operational data fusion framework for building frequent Landsat-like imagery. IEEE Trans. Geosci. Remote Sens. 52 (2014), 7353–7365.
53. Weng, Q.H., Fu, P., Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery. ISPRS J. Photogramm. Remote Sens. 97 (2014), 78–88.
54. Weng, Q.H., Fu, P., Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sens. Environ. 140 (2014), 267–278.
55. Weng, Q.H., Fu, P., Gao, F., Generating daily land surface temperature at Landsat resolution by fusing landsat and MODIS data. Remote Sens. Environ. 145 (2014), 55–67.
56. Woodcock, C.E., Strahler, A.H., The factor of scale in remote sensing. Remote Sens. Environ. 21 (1987), 311–332.
57. Wu, M., Niu, Z., Wang, C., Wu, C., Wang, L., Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. J. Appl. Remote. Sens., 6, 2012, 063507.
58. Wu, P., Shen, H., Ai, T., Liu, Y., Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion. Int. J. Digital Earth 6 (2013), 113–133.
59. Wu, M., Li, H., Huang, W., Niu, Z., Wang, C., Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring. Environ. Sci.: Processes Impacts 17 (2015), 1396–1404.
60. Wu, P., Shen, H., Zhang, L., Göttsche, F.-M., Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature. Remote Sens. Environ. 156 (2015), 169–181.
61. Xu, H., Yu, Y.Y., Tarpley, D., Gottsche, F.M., Olesen, F.S., Evaluation of GOES-R land surface temperature algorithm using SEVIRI satellite retrievals with in situ measurements. IEEE Trans. Geosci. Remote Sens. 52 (2014), 3812–3822.
62. Zakšek, K., Oštir, K., Downscaling land surface temperature for urban heat island diurnal cycle analysis. Remote Sens. Environ. 117 (2012), 114–124.
63. Zeng, C., Shen, H., Zhang, L., Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method. Remote Sens. Environ. 131 (2013), 182–194.
64. Zhan, W.F., Chen, Y.H., Zhou, J., Wang, J.F., Liu, W.Y., Voogt, J., Zhu, X.L., Quan, J.L., Li, J., Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats. Remote Sens. Environ. 131 (2013), 119–139.
65. Zhan, W.F., Huang, F., Quan, J.L., Zhu, X.L., Gao, L., Zhou, J., Ju, W.M., Disaggregation of remotely sensed land surface temperature: a new dynamic methodology. J. Geophys. Res.-Atmos. 121 (2016), 538–554.
66. Zhang, H.K., Huang, B., Zhang, M., Cao, K., Yu, L., A generalization of spatial and temporal fusion methods for remotely sensed surface parameters. Int. J. Remote Sens. 36 (2015), 4411–4445.
67. Zhou, J., Chen, Y.H., Zhang, X., Zhan, W.F., Modelling the diurnal variations of urban heat islands with multi-source satellite data. Int. J. Remote Sens. 34 (2013), 7568–7588.
68. Zhu, X., Chen, J., Gao, F., Chen, X., Masek, J.G., An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 114 (2010), 2610–2623.
69. Zhu, X., Helmer, E.H., Gao, F., Liu, D., Chen, J., Lefsky, M.A., A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172 (2016), 165–177.
70. Zhukov, B., Oertel, D., Lanzl, F., Reinhackel, G., Unmixing-based multisensor multiresolution image fusion. IEEE Trans. Geosci. Remote Sens. 37 (1999), 1212–1226.