# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## Key Research Findings
### SUHI Variations at a Local Scale and Their Drivers
This is the case when thermal infrared sensors with a nadir-pointing viewing geometry (e.g., Landsat ETM+, ASTER) are used to retrieve LST [62; 94; 154]. Cooling effects of vegetated areas on SUHI have also been studied in the form of various 2D and 3D USCs. The NDVI, vegetation fraction (VF), and the size of green spaces have by far received the most attention [66; 81; 95; 98; 103; 134; 137; 138; 140; 141; 143; 147; 148; 152; 153; 160; 161]. Cooling is reported to be the strongest when a city's green coverage lies between 70% and 80% [162] and the size and density of the green infrastructure is sufficient [140; 143; 152; 160; 163]. Moreover, the shape and orientation of vegetated areas matter, even though there is no consensus on whether green spaces should be of regular or irregular form [95; 103; 137; 141]. Under certain conditions, patches of vegetation can reduce the temperatures of their surroundings by up to 24 Kelvin [30] and within adistance of 60 to 840 m [137; 140; 161]. Instead of analyzing all types of vegetation as a whole (i.e., trees, shrubs, and grass), many studies specifically focus on the abundance and characteristics of urban (street) trees, parks, and forests [94; 103; 135; 143; 164; 98]. Among these, the percentage of tree cover is the most common used USC due to its higher potential to alleviate the SUHI than mixed vegetation [157; 165]. Apart from the proportion of urban trees, USCs describing their height, volume, and species have also been found closely related to urban LST [137; 147; 130; 94]. In addition to urban vegetation, the presence and proximity of water bodies are known to have a cooling effect on urban temperature [140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 140; 141; 142; 166]. However, impacts of the water body have been quantified by few indicators such as the normalized difference water index (NDWI) [166; 167] and the distance between water bodies (or the coastline) and built-up land [142; 158; 165; 168]. The SUHI variations have also been widely attributed to landscape composition and configuration metrics at a local scale [169; 167; 168; 165; 163; 166; 167; 169; 170; 171; 172; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 101; 102; 105; 103; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172]. Some authors even state that the patterns of built-up land and vegetation have the largest impact on urban LST [115]. It is frequently questioned whether landscape composition or configuration is more important. The answer to this question depends on the city under investigation [173], the land cover/land use, and the types of green space within the study area [174; 172; 173; 99] as well as the landscape metrics considered [171]. In general, composition is often found to be more important than configuration [175; 163; 169; 170; 176; 167; 168; 169; 170; 175]. This is particularly true when landscape metrics describing composition and configuration are compared on an individual basis. However, when feeding several metrics into a regression model to estimate LST, the combination of certain measures of landscape configuration outperforms all other predictor sets [171; 175]. In the same context, [PERSON] et al. [171] also noted that the joint usage of multi-scale metrics (i.e., patch-level, class-level, and landscape-level metrics) takes into account the hierarchical structure of urban landscapes, and, thus, can improve LST modeling. It is, therefore, not without reason that a few studies suggest to choose one composition metric and a small selection of configuration metrics to investigate SUHI variations [169; 175; 176].