Increasing urbanization has caused changes in the heat balance in densely built urban areas [1
]. In such cases, both mean air temperature and land surface temperature (LST) in the urban centers are usually higher than their respective temperatures in the rural surroundings. This phenomenon is known as urban heat island (UHI) [4
]. Recent studies show that the development of UHI can be monitored using thermal remote sensing [7
]. Remote sensing has a great advantage over in situ
measurements. Instead of measurements at irregularly spaced point locations, remote sensing provides UHI with a quasi continuous monitoring of surfaces [13
]. However, remote sensing of urban climates is restricted by several factors [14
]. In particular, only the surface temperature UHI (also SUHI, here further referred to as UHI) can be directly monitored by remote sensing, which can largely differ from the canopy layer UHI. Another general problem of the satellite remote sensing is the lack of data having both high spatial and temporal resolution; a recent review of available sensors is given by Tomlison et al.
In terms of UHI monitoring, a spatial resolution of 1 km allows coarse scale temperature mapping and limits the analysis of relationships between the UHI and in situ
measurements of air temperature [17
]. The highest spatial resolutions of spaceborn LST sensors are about 100 m, which would be much more appropriate, because it is a good approximation for an average width of a building block. Satellite instruments that retrieve data in such resolutions are the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER; spatial resolution of its thermal bands is 90 m) and Landsat (LS; spatial resolution of 120 m for LS 4 and 5, 60 m for LS 7 and 100 m for the coming LS 8 [18
]). They fly on low earth orbiters with a relatively narrow swath and their revisit time is 16 days (eight days for ASTER including off-nadir acquisitions). Such a revisit time is too long to make these instruments suitable for the assessment of the diurnal evolution of UHI.
Instruments in the geostationary orbit like the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) aboard Meteosat Second Generation (MSG) satellites on the other hand have appropriate temporal resolution but too poor spatial resolution (standard retrieval time of SEVIRI is 15 min; one SEVIRI pixel for the case study area of Hamburg is approximately 3,300 by 6,700 m large). Many studies are thus based on instruments in polar orbits that have wide swath (over 2,000 km) and a revisit time of approximately one day (one daytime and one night-time image). Such instruments have nominal spatial resolution of about 1,000 m, thus they are considered as a reasonable compromise between the two previously described options.
This paper examines the possibility of using an alternative approach based on the fusion of data with different temporal and spatial resolutions. Such an approach should provide results in high temporal (15 min) and high spatial (100 m) resolution. This should be possible as LST spatial distribution is well correlated with numerous parameters. Many of them can be observed by remote sensing in high spatial resolution. If the correlation between these parameters in high spatial resolution (HR) and low spatial resolution (LR) LST is known, LST in HR can be estimated. The factor of this downscaling was not larger than 100 in previous studies. This study aims to examine whether LST downscaling can be used with a higher downscaling factor in order to fulfill the needs of urban climatology (Section 2.1). Furthermore, it is investigated which predictors are most suitable to implement such a downscaling scheme and how the errors depend on the target resolution (Section 3). At the end possible improvements are discussed (Section 4).
Regarding the large downscaling factor between SEVIRI and ASTER data, the results of the Hamburg case study are very promising. Three proposed predictor sets reached an RMSE of about 2.2 K and an explained variance of about 0.7. An RMSE of less than 2 K was reached by three predictor sets at a target resolution of 300 m, at 1,000 m resolution RMSEs of about 1.65 K and R2
of about 0.8 were reached. These results are very good compared with the accuracy of the SEVIRI LST retrieval of 2 K [48
]. In the past, only a few studies were dedicated to downscaling of LST in the urban areas. Liu and Pu [27
] reached a better R2
(0.77) for their Yokohama (Japan) case study. However, they downscaled from MODIS to ASTER resolution, which is a much smaller downscaling factor (about 100); comparable results were achieved in this study for a resolution of 300 to 400 m, which is still a higher downscaling factor. The downscaling from ASTER to 10 m resolution in San Juan (Puerto Rico) suggested by Dominguez et al.
] also resulted in higher explained variance (R2
= 0.81) and higher error (RMSE = 2.8 K) than in this study. Stathopoulou and Cartalis [41
] downscaled AVHRR to LS TM resolution in Athens (Greece) with much higher errors than in this study (RMSE = 4.9 K). We are aware of two studies that tried to downscale LST in urban areas using geostationary data. Zakšek and Oštir [32
] downscaled the SEVIRI data to 1,000 m resolution for the urban areas of central Europe. Their correlation is very high (R = 0.97, which corresponds to an R2
of about 0.94) but their error is also higher than ours (RMSE = 2.5 K). Keramitsoglou [58
] also downscaled SEVIRI data to 1,000 m resolution but for the area of Athens (Greece); 67% of the processed datasets exhibit correlation coefficient between 0.6 and 0.8.
Although the presented results are encouraging compared with the listed studies, improvements are still possible. The random error of the downscaling scheme is even much lower, if the large systematic bias of about 1.3 K for all models and resolutions is considered. Thus, the overall error could be substantially decreased if an independent estimate of the bias was available. The reason for the bias is the geometry of data retrieval. The geostationary SEVIRI views Europe from the South, thus it sees a remarkable share of southern vertical sides of objects. These are warmer, while northern facades of buildings are not seen by SEVIRI at all. On board a low Earth orbiter, ASTER can be used off-nadir at low angles and thus also often observes surfaces towards West or towards East, resulting in an azimuth dependent LST. However, the viewing geometry of ASTER is still “largely perpendicular” compared with SEVIRI (for instance a pointing angle of 5.71°for the used ASTER scene instead of almost 60° for SEVIRI). Hence, mostly horizontal objects (especially roofs and streets) are seen by ASTER. This bias can also be seen in the scatterplots in Figure 1
(higher intercept of the LR data) and is in acceptable agreement with previous studies. Trigo et al.
for instance reported that the SEVIRI LST was about 2 K higher than MODIS LST for the Iberian Peninsula, Central Africa, and the Kalahari [51
]. MODIS (terra) has a comparable viewing geometry to ASTER, since it flies on the same platform. The bias seemed to be largely independent from the tested resolutions (the mean bias of eight selected predictor sets was between 1.310 and 1.314 for all tested resolutions). Besides the geometrical influences, systematic errors due to a small time difference between SEVIRI and ASTER retrieval and different emissivity retrieval algorithms used in both products cannot be excluded.
In general, the aggregated TIR parameters (ACP, tirpca) turned out to be the most suitable predictors for LST downscaling and the sets selected by expert knowledge outperformed the automatic feature selection methods. Furthermore, the smallest predictor sets also resulted in the smallest errors. The predicted HR LST then just is a linear combination of few patterns. Since these parameters are essentially all derived from measurements at a similar time (and hence similar heating patterns), the downscaling is likely to perform less well for different times of the day. Unfortunately, ASTER validation data is mostly restricted to one acquisition time. Only a few night-time acquisitions are available (during the case study observation period, the night-time retrieval completely failed).
Heating patterns acquired during different seasons can be assumed to show comparable effects. Hence, the correlations between the ASTER LST and Landsat TIR data from different times of the year (also from different years) were investigated. In Figure 4
the correspondence between LST from ASTER and Landsat TIR (blue) as well as NDVI (green) from different days of the year are shown. The vertical line indicates the ASTER acquisition day and it can be seen that acquisitions from the same season have a higher R2
while the thermal patterns from winter acquisitions are substantially different. Hence, predictor sets of more than one pattern are expected to be more stable in order to produce heating patterns for different times of the day.
NDVI predictors were expected to be suitable for the downscaling scheme. The correlation between single NDVI predictors and ASTER LST (see also Figure 4
) in the case study was, however, significantly lower than the values from literature (for instance 0.7 in [36
]). This is a result of several factors. First, a large time lag between LST and NDVI is responsible for different phenological conditions that have a major influence on LST (Figure 4
shows a strong seasonal dependency between the variance of ASTER LST explained by NDVI). An additional analysis revealed that the explained variance of the NDVI predictors with the respective TIR patterns of the same day is much higher (R2
= 0.36). Secondly, NDVI should not be linearly upscaled but recomputed from the upscaled red and NIR bands. Thirdly and most importantly, the low correlations of the different NDVI result from the high fraction of water coverage in Hamburg. LST of the water bodies is largely dominated by their heat storage capacity as well as advection from upstream areas. For the given situation the water temperatures are lower than those of the vegetated areas. Conversely, water has a low NDVI like impervious surfaces (water bodies and adjacent sealed areas show LST deviations of more than 20 K). Hence, NDVI shows a much higher explained variance (e.g., R2
= 0.31 instead of R2
= 0.09 for scene #LT41960231989185XXX0) if the water pixels are excluded. Thus, we expect the fraction of active vegetation (besides those of impervious surfaces and water) to be a better LST predictor than NDVI. This is in agreement with Weng et al.
], who argue that LST is more correlated to vegetation fraction than NDVI.
To answer the question which predictors are suitable for the downscaling scheme, the explained variances of single predictors in LR and HR were compared. The results are displayed in Figure 5
. It can be seen that NDVI shows a much higher R2
in LR domain, which is again a consequence of the water bodies that cover only small proportions of single SEVIRI pixels. Hence, the basic assumption of the downscaling is not fulfilled for these predictors. Conversely, the ACP (red circles), the first TIR PC (magenta asterix at lower left), and soilseal
(cyan, upper right) show very high R2
in both HR and LR, which are almost identical (indicated by black dotted line). Hence, they fulfill the basic assumption and are suitable predictors, which is in good agreement with their results in Table 1
. The multispectral data partly suffers from the same problem as NDVI, while most of the morphological and heighstat
features (besides the minimum height) and some of the additional TIR PCs explain only little variance. This leaves only a small number of the tested predictors that are really suitable. The same conclusion was also underpinned by an additional experiment with PCs calculated from the complete predictor set. The first PC, which explained about 52% of the overall variability, was only very weakly correlated to ASTER LST (R = 0.27) indicating that a large number of predictors are rather ineffective.