# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Impacts of Clouds and Other Factors on LST Data  
Satellite-derived LST data are severely affected by clouds and other factors such as scene view angles and internal errors of sensors [24; 200]. [PERSON] et al. [201] reported that, among 21 images they browsed for the study in Detroit, US, only one image was cloud-free. [PERSON] et al. [69] found that there were significant thermal anisotropies in urban areas and the effects could be up to 9 K. To improve the accuracy of LST products, cloud-contaminated pixels are often removed by cloud-screening algorithms [202]. However, scene view angles and anisotropic effects have been overwhelmingly ignored in SUHI studies [203]. As a result, missing or low-quality pixels are not uncommon in LST products, especially over urban areas [204]. For example, [PERSON] et al. [205] found that the MODIS daily LST product generally covered less than 30% of the urban areas in the conterminous United States. The issue of missing pixels largely limits the application of LST in investigating the SUHI, which has significant spatial and temporal variations. Temporal aggregation has been widely used to build a cloud-free and seamless remote sensing product. For instance, daily LST data can be aggregated to a coarser temporal resolution, such as 8-day and monthly composites. However, temporal variation of LST is reduced in the derived product, which results in the loss of temporal information of SUHI. The derived data may not be able to distinguish extreme events, such as heat waves, which are one of the most important issues in urban climate studies [206]. Additionally, such methods have been widely criticized for their associated accuracy losses [207; 208; 209; 210]. [PERSON] and [PERSON] [207] suggested that temporal aggregation of LST increased the SUHI values substantially, especially during the daytime, in the summer, and in high SUHI regions, with higher biases in larger composite periods. [PERSON] and [PERSON] [203] and [PERSON] et al. [209] confirmed that quality control can significantly impact the SUHI estimates either by removing poor-quality pixels or weighting the pixels according to their quality, especially in urban centers. Therefore, it will be important for SUHI studies to fill gaps in LST data and develop a seamless daily LST product. New methods have been proposed for such a purpose in previous studies [211], such as integrating multi-source datasets from different sensors [212; 213; 214; 215], daily merging using statistical relationships between observations at different overpass times [216], empirical relationships using auxiliary data (e.g., land cover and NDVI) [217], and geostatistical-based spatial-temporal interpolation [218; 219]. These methods have been used individually or jointly to generate seamless LST products. For example, [PERSON] et al. [216] developed a daily merged MODIS Aqua-Terra LST dataset for the continental United States. [PERSON] et al. [205] developed a seamless LST product for urban areas in the continental United States by combining daily merge and spatial-temporal interpolation. However, there are challenges of efficiency and accuracy in these methods [211]. Large computing resources are needed in some of these methods, especially for large areas, while the uncertainties in the resulting LST products from other methods are large. Most importantly, the gaps in LST (e.g., cloud-contaminated pixels) are usually filled by the clear-sky observations, which may be higher than the actual LSTs under cloudy sky conditions [205; 220]. In other words, SUHI estimates based solely on clear-sky LST values would overestimate the SUHI effect no matter whether gaps were filled or not. Recently, a few studies have successfully generated all-weather LST values by fusing MODIS and passive microwave data (AMSR-E) [221; 222]. However, the coarse spatial resolution of AMSR-E (\(\sim\)25 km) impedes its application in urban areas.