**4. Discussion**

**Figure 7.** An example of downscaled LST (K) for the August 30, 2013 (a). The ASTER (b) and MODIS (c) LST products

corresponding to the same date are also presented for comparison.

*min<sup>E</sup>* ‖*S*(*L*)

140 Multi-purposeful Application of Geospatial Data

then constructed by applying Eq. (5) for high resolution (*H*).

where *S*¯′

(*L*)

− *A*(*L*)

∙ *E* + *b <sup>w</sup>*<sup>2</sup> \_\_\_

tion parameter to ensure small spectral variations. The high spatial resolution thermal band is

are predefined spectra corresponding to surface cover types and *b* is a regulariza-

*<sup>n</sup>* (*E* − *S* ′

(*L*) )‖2 2

(6)

Medium spatial resolution satellite data have been used in the past with spectral unmixing methods for mapping the urban surface cover [15]. This chapter demonstrated the use of image endmember and synthetic spectra to estimate sub-pixel information on the urban surface cover. The proposed methodology is fast in terms of computational time and affordable to implement and apply for urban studies. It is also easy to reproduce for other cities, if the relevant data are available. It is, thus, suitable for monitoring the surface cover and it can be used for change detection and time series analysis. The products are useful for various studies, related to surface cover properties, urban climate, urban climatology, and urban expansion.

An example use of this detailed urban surface cover information is the LST downscaling method. The method described and applied in this chapter is highly dependent on accurate surface cover information. It has been demonstrated than the uncertainty in the downscaled LST estimation is closely linked to the uncertainty related to the surface cover fractions [29]. The methodology for mapping the urban surface cover is applicable to Sentinel-2 imagery. The enhanced spatial and spectral resolution of Sentinel-2 compared to Landsat is expected to advance the method. The optical bands of Sentinel-2 are similar to the ones of Landsat 8, but the enhanced spatial resolution of 10 m provides better insights on the underlying objects. Further advances may include analysis of the 10 m bands for identifying pure spectra to be used as endmembers for spectral unmixing techniques. Moreover, the additional bands in the near-infrared compared to Landsat, for example, provide more spectral information necessary for unmixing techniques. Although these bands are designed for detecting and discriminating between different vegetation types, and their main advantage lies in this kind of applications, they can be proven useful for differentiating between urban materials as well. Further analysis is, though, needed to come to conclusions on using the red-edge bands for urban monitoring. Finally, Sentinel-2A and Sentinel-2B will provide frequent acquisitions with a revisit of 5 days in the equator and, in combination with the large swath of 290 km, the potential of updating the surface cover information is significantly increased. Thus, the increased temporal resolution substantiates the urban surface cover monitoring even in areas with persistent cloud cover.

**Author details**

**References**

Zina Mitraka\* and Nektarios Chrysoulakis

DOI: 10.1109/JURSE.2017.7924591

2003;**23**:1-26. DOI: 10.1002/joc.859

**26**:224-240. DOI: 10.1016/j.rser.2013.05.047

1276. DOI: 10.1007/s10980-008-9296-6

1151419

01431160512331316874

DOI: 10.1038/news041129-6

\*Address all correspondence to: mitraka@iacm.forth.gr

Foundation for Research and Technology Hellas, Heraklion, Greece

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Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature

http://dx.doi.org/10.5772/intechopen.71986

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The application of the downscaling method described in this chapter was demonstrated through an example using MODIS thermal data. With the use of the Sentinel-2 imagery, the surface cover characterization is expected to be improved significantly as discussed earlier. Moreover, the OLCI spectral bands measuring in VNIR share some common bands with Sentinel-2 MSI and this may allow further exploitation for updating the surface cover. Since, the Sentinels are developed for synergies [30], algorithms that exploit the common bands of OLCI and MSI may increase the accuracy of surface characterization and emissivity estimation.
