In this study, 167 land parcels of downtown Shanghai, China, were used to investigate the relationship between parcel-level land use/land cover (LULC) components and associated summertime intra-surface urban heat island (SUHI) effect, and further analyze the potential of mitigating summertime intra-SUHI effect through the optimized LULC components, by integrating a thermal sharpening method combining the Landsat-8 thermal band 10 data and high-resolution Quickbird image, statistical analysis, and nonlinear programming with constraints. The results show the remarkable variations in intra-surface urban heat island (SUHI) effect, which was measured with the mean parcel-level blackbody sensible heat flux in kW per ha (Mean_pc_BBF). Through measuring the relative importance of each specific predictor in terms of their contributions to changing Mean_pc_BBF, the influence of parcel-level LULC components on excess surface flux of heat energy to the atmosphere was estimated using the partial least square regression (PLSR) model. Analysis of the present and optimized parcel-level LULC components and their contribution to the associated Mean_pc_BBF were comparable between land parcels with varying sizes. Furthermore, focusing on the gap between the present and ideally optimized area proportions of parcel-level LULC components towards minimizing the Mean_pc_BBF, the uncertainties arising from the datasets and methods, as well as the implications for sustainable land development and mitigating the UHI effect were discussed.

This study investigated the fine‐scale land parcel features and their influences on the intra‐surface urban heat island (SUHI) of four dense urban functional zones in downtown Shanghai. A thermal sharpening method was employed to map the high‐resolution intra‐SUHI intensity (SUHII) and magnitude (SUHIM) by integrating the cokriging interpolation of Landsat 8 thermal band 10 and Quickbird datasets. Together with three area proportions (area proportion of the cooling surface/CoolAP, area proportion of the buildings/BuildAP, and area proportion of the other impervious surface/OtherAP), the mean distance between the nearest neighboring buildings (BuildDist) and the building average story number (BuildSN) were used to characterize the land parcel features. The results showed that both the composition and the design attributes of land parcels were significantly related to the intra‐SUHI indicators (SUHII and SUHIM). Additionally, remarkable seasonal variations existed in the intra‐SUHI indicators. The results of the partial least square regression models explained 64.6–65.2% of the variance of the SUHIIs and 63.0–63.9% of the variance of the SUHIMs on hot days and 11.70–18.90% of the variance of the SUHIIs and 11.10–15.90% of the variance of the SUHIMs on cool and chilly days, respectively. This study provided compelling evidence that for a given land parcel, when fixing the effects of BuildAP or OtherAP, high levels of CoolAP, BuildSN, and BuildDist will help create a cooling microclimate on hot days. In contrast, high‐density land parcels with sparse cooling surface exhibit much higher SUHII and SUHIM levels than do low‐density parcels with dominant cooling surface.

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Ongoing urban expansion has accelerated the explosive growth of urban populations and has led to a dramatic increase in the impervious surface area within urban areas. This, in turn, has exacerbated the surface heat island effect within cities. However, the importance of the surface heat island effect within urban areas, scilicet the intra-SUHI effect, has attracted less concern. The aim of this study was to quantitatively explore the relationship between the spatial heterogeneity of a built environment and the intra-urban surface heat island (intra-SUHI) effect using the thermally sharpened land surface temperature (LST) and high-resolution land-use classification products. The results show that at the land parcel scale, the parcel-based relative intensity of intra-SUHI should be attributed to the land parcels featured with differential land developmental intensity. Furthermore, the partial least squares regression (PLSR) modeling quantified the relative importance of the spatial heterogeneity indices of the built environment that exhibit a negative contribution to decreasing the parcel-based intra-SUHI effect or a positive contribution to increasing the intra-SUHI effect. Finally, based on the findings of this study, some practical countermeasures towards mitigating the adverse intra-SUHI effect and improving urban climatic adaption are discussed.