The Urban Heat Island (UHI) is one of the most representative manifestations of climatic modification [1
]. This effect refers to the relative warmth of urban areas with respect to their surrounding rural areas and is present in every city and town. UHIs are primarily caused by the alteration of the urban local climate and heat balance due to the large-scale conversion of pervious surfaces to impervious surfaces and also the increased anthropogenic heat fluxes of urban areas [1
]. UHIs exhibit strong spatial, temporal and vertical variations and have been related to a range of issues, such as human health and energy demand [3
]. Their impact is expensive [6
] and extends to large populations and areas [5
Due to their importance, the study of UHIs has concerned the scientific community for more than 50 years. Most relevant studies employ in situ
Air Temperature (TA) data [1
]. After 1972, remotely-sensed Land Surface Temperature (LST) data have also been utilized for the study of the Surface UHI (SUHI) [13
]. In contrast to TA data, which are point measurements confined to local conditions [17
], thermal infrared (TIR) remote sensing is capable of providing a simultaneous and synoptic view of the urban thermal environment [18
]. This enables the more detailed assessment of the urban hotspots and the relationship between the urban core and the surrounding natural lands [18
]. Nevertheless, the use of satellite TIR data is not straightforward, and a number of limitations and problems, such as the atmospheric influence, the unknown emissivity and the effective anisotropy, have to be addressed prior to their exploitation [21
]. To that end, one of the most important problems concerns the spatial and temporal resolution of the LST data. In particular, the available satellite sensors cannot provide datasets that capture the high spatial and temporal variability of SUHIs, and thus, their exploitation in urban climate studies is limited [22
]. For instance, the Sun-synchronous Landsat series satellites, which offer the appropriate high spatial resolution (~100 m), acquire LST data every 16 days; whereas the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) geostationary satellites provides ~4 km TIR data with a more appropriate temporal resolution of 5–15 min.
To overcome this problem, the statistical downscaling of geostationary LST data has been proposed. This process can lead to the generation of Downscaled LST (DLST) time series that combine high spatial and temporal resolution and preserve the radiometry of the original thermal data [22
]. In detail, the LST statistical downscaling is a scaling process that aims to enhance the spatial resolution of coarse-scale LST imagery using fine-scale auxiliary datasets. These auxiliary datasets are usually referred to as LST predictors and are statistically correlated to the LST [26
]. An LST downscaling scheme comprises two major parts. The first part is the set of LST predictors used for explaining the spatial variation of LST, while the second part is the regression tool used for associating the LST predictors with the LST data. These two parts are synergistically exploited in a three-stage procedure: firstly, the LST predictors are upscaled and co-registered to the coarse-scale LST data; then, a relationship between the coarse-scale LST data and the LST predictors is established using the regression tool; and finally, this relationship is applied to the fine-scale LST predictors, so as to generate the DLST data. Agam et al.
] and Kustas et al.
] include also a fourth process stage, which is an adjustment of the generated DLST data based on the differences of the observed and regressed coarse-scale LSTs. This post-downscaling processing aims to compensate the loss of LST variability due to the use of inflexible regression tools, such as least-square or linear fits [28
In recent years, a large number of relevant works have been published [26
] testing different LST predictors (separately or combined), such as: Vegetation Indices (VIs), emissivity data, land cover maps and topography data; and also regression tools, such as: linear regressors, least-square fits, Support Vector Regression Machines (SVMs) and Neural Networks (NNs) [22
]. From the available list of LST predictors, the most widely used is the Normalized Vegetation Difference Index (NDVI) [27
], which is strongly negatively correlated with summer daytime LST imagery [30
]. Most of the aforementioned works focus on the downscaling of ~4 km data down to 1 km. The downscaling to even higher spatial resolutions (<500 m) is still a very challenging task, mainly because the assumption that the LST data-predictor relationship is valid in both spatial scales weakens or ceases to apply [31
]. The work of Bechtel et al.
] is one of the few studies that discusses the downscaling of geostationary urban LST data down to 100 m with a Root-Mean-Square-Error (RMSE) of 2.2 °C (recent LST fusion studies [32
] also report similar downscaling factors). In this work, a large set of LST predictors was utilized that also included for the first time LST annual climatology data in the form of annual cycle parameters (ACPs) [34
]. The use of the ACPs for downscaling LST data below the 1-km cap provided very promising results [24
]. However, this type of LST predictor has not been used as extensively as the rest, and thus, the available relevant literature is still very limited.
Besides the identification of more robust predictors, another issue that requires attention is the evaluation of the generated high spatiotemporal DLST time series, which is hampered by the lack of appropriate ground truth data [21
]. Presently, most downscaling studies utilize independent LST image data (confined to certain time spots) with which they compare the generated data and calculate statistical measures of accuracy and similarity. However, this approach is quite limiting when evaluating DLST time series. This is because it does not examine if the downscaling process was capable of accurately reproducing the spatiotemporal features of the original LST time series (with greater spatial detail), e.g., the SUHI diurnal cycle, and their spatiotemporal inter-relationships (the 5–15-min acquisition cycle makes the relationships between sequential DLST data especially pronounced). A review of the relevant literature reveals the following gaps regarding the evaluation of DLST data/time series:
Limited sample dataset used for evaluating the proposed methods. Currently, the evaluation of the main bulk of available downscaling algorithms has been restricted to a small sample of scenes. However, this approach does not allow the assessment of the method’s consistency, robustness and reliability, which are also important. This is because the proposed LST downscaling algorithms will eventually be applied to LST time series and not only to individual scenes.
The in-depth evaluation of the revealed DLST spatial thermal patterns. The extraction of spatial information from LST images is a major input, especially for SUHI studies [20
]. The shape of the revealed DLST hotspots is heavily dependent on the set of LST predictors used. In detail, the downscaling of the same coarse-scale LST time series, with the same regression tool, but with different sets of LST predictors, would lead to the formation of different DLST spatial patterns.
The assessment of the spatiotemporal inter-relationships between sequential DLST data. The diurnal evolution of the LST (and TA) follows a sine wave-like pattern where the LST values smoothly increase during daytime and smoothly fall during nighttime. Short-term weather effects (e.g., heatwaves) and seasonal effects (e.g., vegetation phenology) affect this sine wave-like pattern. In LST time series, the impact of these effects is recorded as special time-dependent features [37
]. The features caused by short-term weather effects are presented as brief, but pronounced changes in LST values and patterns. For instance, a heatwave is recorded as an increase of LST values and an intensification of the SUHI spatial cluster for a number of consecutive days [38
]. On the other hand, the seasonal effects are more subtle and only observable when examining long time series, e.g., the gradual cooling from summer to winter. Hence, a successful downscaling of LST time series should result in a smooth diurnal evolution of DLST values and patterns and emulate the short-term and seasonal features of the coarse-scale LST time series.
Assessment of the downscaling method’s performance for different biomes, seasons and topographic and climatic conditions. This issue is important because these factors might influence the relationship between the LST data and the LST predictors and, thus, impact the downscaling process by rendering some LST predictors less effective or even ineffective for certain conditions. For instance, the correlation between NDVI and LST weakens during autumn months [30
] and depends also on vegetation type/latitude [40
This work concerns the first three issues presented above through the study of a downscaled three-month long MSG3-SEVIRI LST time series depicting Rome, Italy. In particular, it assesses the accuracy and correct pattern formation of the generated DLST data and also the impact of the downscaling process on the diurnal evolution of the DLST urban and rural spatial patterns. The assessment process is based firstly on comparisons with an independent LST time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) and secondly on the estimation of autocorrelation measures that exploit the high temporal resolution of the MSG3-SEVIRI data. This article proceeds as follows: in Section 2
, the study area and the employed LST data and predictors are presented. In Section 3
, the generation of the MSG3-SEVIRI DLST data and the performed evaluation tests are discussed, while in Section 4
, the results obtained are presented. The article concludes with a review of this study’s contribution and novelty.
The downscaling of frequently-acquired geostationary LST has the potential to compensate the lack of high spatiotemporal LST time series for urban climate studies. To deem the downscaling of geostationary LST time series successful and capable of capturing the spatial and temporal variations of SUHIs, the generated high spatiotemporal DLST time series must reproduce the spatiotemporal features of the coarse-scale LST time series with greater spatial detail. This is one of the first studies that discusses this issue and assesses if the generated DLST data can indeed be exploited in urban thermal applications. To address this question, this work studied the accuracy, the correct pattern formation and the temporal changes of a downscaled three month-long MSG3-SEVIRI LST time series depicting the city of Rome, Italy.
The results suggest that the downscaling process operated in a consistent manner, preserved the radiometry of the original MSG3-SEVIRI data and generated noon, afternoon and nighttime spatial thermal patterns that were similar to those present in the evaluation data. However, the evaluation revealed that the generated DLST data could not emulate the morning urban sink of Rome, which is an important issue for SUHI studies. Moreover, the results also suggest that the downscaling of urban pixels is more challenging than for rural pixels, both for daytime and nighttime images. In particular, the evaluation process showed that the diurnal evolution of the generated data was smooth, but the autocorrelation of the 1-km DLST data was higher than of the original 4-km LST data. This suggests that the DLST data could not present subtle spatial thermal changes during the course of a day as pronouncedly as the measured data could. These findings (even though confined to this study) reveal a series of issues that are important to urban thermal studies that using only conventional DLST evaluating schemes (i.e., comparisons with independent LST data confined to certain time spots) would remain unrevealed.
Generalizing the aforementioned observations, the assessment of high spatiotemporal DLST data for urban thermal applications should consider also the following issues:
The capability of the DLST data to accurately emulate the SUHI diurnal pattern cycle.
The capability to detect subtle spatial thermal changes during the course of a day.
The smoothness of the diurnal evolution of the DLST data.
The consistent performance of the employed downscaling method.
The exploitation of the DLST data spatiotemporal inter-relationship for evaluation purposes can overcome some of the limitations posed by the lack of ground truth data and facilitate the assessment of the issues listed above. Presently, this matter is overlooked. However, the capability of the downscaling process to accurately emulate the DLST diurnal cycle values and patterns and the time series’ temporal characteristics is crucial. This is because these two features ultimately determine the exploitability of the DLST time series for generating added value products and services for the study of the urban thermal environment, such as the estimation of air temperature, the SUHI analysis and the heat wave hazard assessment.
Besides the difficulties and the limitations currently faced, the generation of geostationary DLST time series is an important advancement of TIR remote sensing that can facilitate the study of the urban climate. Future research should focus more on the assessment of the spatiotemporal characteristics of DLST time series.