This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas.

Impervious surfaces (IPS) are the major source of urban heat island effect (UHI), and the relationships between IPS and land surface temperature (LST) have been widely studied. However, the spatial impact of landscape patterns of patches with different IPS density (IPSD) on the thermal environment remains largely unexplored. Based on three Landsat 8 images of the Xuzhou built-up area obtained in May and the corresponding ground observations from 2014 to 2020, the IPSD and LST maps were inversed through a linear spectral mixture analysis and mono-window algorithm, respectively. The landscape patterns of the five IPSD levels were characterized by four landscape-level and five class-level metrics. Finally, the spatial correlation between all landscape metrics and LST were analyzed using bivariate Moran’s I. The results were as follows: (1) The findings revealed that for the landscape-level metrics, LST had significant positive spatial correlations with Shannon’s diversity index (SHDI), Shannon’s evenness index (SHEI), and patch density (PD), while showing a significant negative correlation with contagion index (CONTAG), indicating that increasing the types, even distribution degree, and density of patches, or decreasing the aggregation degree of the five IPSD levels will lead to the enhancement of the thermal environment. (2) Furthermore, the class-level metrics of each IPSD level, percentage of landscape (PLAND), largest patch index (LPI), landscape shape index (LSI), aggregation index (AI), and patch cohesion index (COHESION) showed significant correlations and LST, which signified that the spatial characteristics of patch proportion, predominance degree, shape complexity, aggregation degree, and natural connectivity degree of each IPSD level are important factors affecting UHI. In addition, the spatial correlations between LST and class-level metrics were significantly positive for IPSD levels 4 and 5 with an evidently higher Moran’s I value, indicating that landscape patterns of IPSD levels 4 and 5 were the key factors in UHI enhancement. Furthermore, the impact weights of each class-level metric of IPSD levels 4 and 5 on LST were also analyzed by applying the principal component analysis and the multivariate regression standardization coefficient. These results reveal the importance and impact mechanism of the IPSD spatial patterns on UHI evolution, which may provide a valuable reference for future urban planning and climate management. This study also suggests that regional UHI can be mitigated by reducing the area proportion, natural connectivity, and shape complexity of high-density impervious surfaces.

The landscape patterns of urban green spaces have been proven to be important factors that affect urban thermal environments. However, the spatial effect of the landscape patterns of urban patches with different vegetation densities on land surface temperature (LST) has not been investigated in detail. In this study, the built-up area of Xuzhou City was taken as the study region, and the four phases of Landsat 8 images and their corresponding ground observations from 2014 to 2020 were selected as the basic data. Normalized spectral mixture analysis and an improved mono-window algorithm were used to invert the vegetation component fraction (VF) and LST maps of the study area, respectively, and the surface patches were classified into five levels according to the VF values, from low to high. Four landscape-level and five class-level metrics were then selected to represent the landscape characteristics of each VF-level patch. The tested values of 60 and 780 m were regarded as the best grain size and spatial extent, respectively, in the calculation of all landscape metrics of ALL VF-level patches (VFLM) using the moving-window method. The results of bivariate Moran’s I for VFLM and LST showed the following: (1) for landscape-level metrics, only the Shannon diversity index and patch diversity have substantial negative spatial correlations with LST (with average |Moran’s I| < 0.2), indicating that the types of VF levels and the number of patches exert weak negative effects on the thermal environment for a certain area; (2) for class-level metrics such as percentage of landscape, patch cohesion index, largest patch index, landscape shape index, and aggregation index, only the class-level metrics of sub-high VF (LV4) and extreme-high (LV5) VF levels patches have significant negative spatial correlations with LST (with high Moran’s I value, and high–high and low–high distributions in local indications of spatial association cluster maps), indicating that only the patches of high VF levels can effectively alleviate LST and that patch proportion, natural connectivity degree, predominance degree, shape complexity, and aggregation degree are important landscape factors for regulating the thermal environment. Principal component analysis and multiple linear regression were applied to determine the impact weights of the class-level VFLMs of LV4 and LV5 patches on LST, which revealed the contributions of these landscape metrics to mitigating the urban heat island effect (UHI). These results signify the importance of and differences in the spatial patterns of various VF-level patches for UHI regulation; these patterns can provide new perspectives and references for urban green space planning and climate management.

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m⁻² increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

To date, little attention has been given to remote sensing-based algorithms for inferring urban surface evapotranspiration. A multi-source parallel model based on ASTER data was one of the first examples, but its accuracy can be improved. We therefore present a modified multi-source parallel model in this study, which has made improvements in parameterization and model accuracy. The new features of our modified model are: (1) a characterization of spectrally heterogeneous urban impervious surfaces using two endmembers (high- and low-albedo urban impervious surface), instead of a single endmember, in linear spectral mixture analysis; (2) inclusion of an algorithm for deriving roughness length for each land surface component in order to better approximate to the actual land surface characteristic; and (3) a novel algorithm for calculating the component net radiant flux with a full consideration of the fraction and the characteristics of each land surface component. HJ-1 and ASTER data from the Chinese city of Hefei were used to test our model’s result with the China–ASEAN ET product. The sensitivity of the model to vegetation and soil fractions was analyzed and the applicability of the model was tested in another built-up area in the central Chinese city of Wuhan. We conclude that our modified model outperforms the initial multi-source parallel model in accuracy. It can obtain the highest accuracy when applied to vegetation-dominated (vegetation proportion > 50%) areas. Sensitivity analysis shows that vegetation and soil fractions are two important parameters that can affect the ET estimation. Our model is applicable to estimate evapotranspiration in other urban areas.

Various physical characteristics of urban impervious surfaces (ISAs) and urban green spaces (UGSs) collectively regulate environmental temperatures through heating and cooling processes. However, current research often analyzes each regulating factor as an independent variable when examining its relationship with land surface temperature (LST), with limited studies considering the combined contribution weights of all regulating factors. Based on multi-source remote sensing data and ground observations from the near summers of 2014, 2016, 2017, and 2018 in the built-up area of Xuzhou City, numerical values and spatial distributions of 15 regulating factors, including ISA density (f_i), land surface albedo (Albedo), population density (Population), anthropogenic heat flux (AHF), maximum ISA patch index (LPI_ISA), natural connectivity of ISA patches (COHESION_ISA), aggregation index of ISA patches (AI_ISA), average shape index of ISA patches (SHAPE_MN_ISA), UGS density (f_v), evapotranspiration (ET), UGS shading index (UGSSI), maximum UGS patch index (LPI_UGS), natural connectivity of UGS patches (COHESION_UGS), aggregation index of UGS patches (AI_UGS), and average shape index of UGS patches (SHAPE_MN_UGS), were separately extracted within the study area. Using geographically weighted regression models and bivariate spatial autocorrelation models, we separately obtained the quantitative and spatial correlations between the 15 regulating factors and LST. The results revealed that all selected regulating factors exhibited high goodness-of-fit and significant spatial correlations with LST, which led to their categorization into eight warming factors and seven cooling factors. The factor detection of the Geographic Detector further reveals the combined contribution of all regulating factors to LST. The results indicate that cooling factors collectively have higher explanatory power for LST compared to warming factors, with UGSSI contributing the most to LST, while Population contributed the least. Furthermore, the interaction detection results of the Geographic Detector have highlighted variations in the explanatory power of different factor combinations on LST. Ultimately, it has identified factor combinations that have proven to be most effective in mitigating the urban heat environment across three scenarios: warming factors alone, cooling factors alone, and a combination of both warming and cooling factors. The suggested factor combinations are as follows: f_i ∩ Albedo, f_i ∩ LPI_ISA, UGSSI ∩ f_v, UGSSI ∩ LPI_UGS, f_i ∩ UGSSI, and Albedo ∩ UGSSI. Therefore, our findings hold the potential to provide a valuable reference for urban planning and climate governance. Tailoring factor combinations to the local context and selecting the most effective ones can enable cost-effective mitigation of the urban heat environment.