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Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.

Statistical downscaling of land surface temperature (SDLST) algorithms with diverse scaling factors and regression models have been used to produce high spatial resolution LSTs based on Landsat-8 LST. However, the optimal choice of scaling factors and regression models and their associated combinations over various land surfaces, especially from a global perspective, remain unclear and even controversial. To cope with this issue, we compare 35 SDLST algorithms derived from a combination of seven scaling factors and five frequently used regression models over 32 geographical regions worldwide. The seven scaling factors, at varying degrees, make use of the LST-related information embedded within the visible and near-infrared and short-wave infrared bands of Landsat-8 data. The five regression models involved are multiple linear regression, partial least squares regression, artificial neural networks, support vector regression, and random forest (RF). Our main findings are: (1) The performance of the scaling factors and regression models are highly dependent on each other. Nevertheless, for most scaling factors, especially for high-dimension scaling factors with numerous LST-related variables, the downscaling algorithms that use RF as the regression model achieve the highest accuracy. (2) RFT21, a newly proposed SDLST algorithm based on the comparison results and further optimization, has high global operability and sufficiently high accuracy. RFT21 requires only Landsat-8 data as the inputs, and achieves the highest accuracy in comparison with the thermal sharpening (TsHARP) and high-resolution urban thermal sharpener (HUTS) algorithms, with the mean root-mean-square error (RMSE) reduced by more than 15%. These findings will facilitate the generation of high spatial resolution LSTs worldwide and associated applications.

Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.

Understanding the diurnal dynamics of surface urban heat islands (SUHIs) is an indispensable step towards their full interpretation at multiple time scales. However, because of the tradeoff between the spatial and temporal resolutions of satellite-derived land surface temperature (LST) data, the climatology, variety, and taxonomy of diurnal SUHI (DSUHI) patterns remain largely unknown for numerous cities with different bioclimates. By combining daily MODIS LST data with a newly developed four-parameter diurnal temperature cycle (DTC) model, we selected 354 Chinese megacities located in different bioclimatic zones to examine the characteristics of the DSUHI descriptors and systematically investigate the prevalent DSUHI temporal patterns. The DSUHI variations demonstrate that both the daily maximum and minimum SUHI intensity (SUHII) can occur during most periods of the day, although these intensities are more likely to occur in the early morning and noon/afternoon. Our results also reveal that both strong SUHIs (SUHII > 3 K) and surface urban cool islands (SUCIs) (SUHII

Understanding the dynamics and spatial heterogeneity of the intra-city surface heat island (herein termed the surface urban heat island, SUHI) is critical for the design of urban heat mitigation strategies. Large disparities in the spatiotemporal variations of SUHIs are anticipated under different urbanization processes. However, most previous studies have focused solely on the inter-annual spatiotemporal SUHI variations of regular urbanization, while those for stable- and counter-urbanization remain largely unknown. Based on the remote identification of these three urbanization types over Guangzhou, China, we propose a novel strategy to investigate simultaneously the spatiotemporal variations and the associated controls of SUHIs. Our results indicate that: (1) Counter-, regular-, and stable-urbanization occurs mainly over the city center, city periphery, and the in-between areas, respectively. (2) The three urbanization types all demonstrate similar and significant growth in the daytime local SUHI intensity (SUHII). (3) There are significant disparities in the contributions of controls to the inter-annual daytime SUHII trends for these three urbanization processes. For the regular urbanization, the increase of the impervious surface percentage (ISP) dominates daytime SUHII growth, while the combination of ISP and residual factors (e.g., background climate and 3D urban geometry) leads for counter urbanization. For stable urbanization, the combination of residual controls and the increase in population density is the major factor. Urban divisional management may contribute to the mitigation of intra-city SUHI. Our findings potentially advance our understanding of changes in urban thermal environment under different urbanization processes.

Separate impacts of building and tree on the urban thermal environment have been studied extensively, but their combined impacts, especially from both the horizontal (i.e., two-dimensional (2D)) and vertical (i.e., three-dimensional (3D)) perspectives remain largely unclear. Based on satellite thermal data and elaborate 2D and 3D urban morphology, herein we simultaneously investigate the separate and combined impacts of building and tree over Nanjing in China from both the 2D and 3D perspectives. We further examine the day–night contrast together with the sensitivity of such impacts to scale. Our results show that, when compared with urban structures from a single dimension, the combination of 2D and 3D structures is more capable of predicting urban land surface temperatures (LSTs) for both day and night. The assessments further illustrate that the separate and combined impacts of building and tree on LSTs are usually more significant when the spatial scale increases. As for the separate impacts of building and tree, 2D structure affects more urban thermal environment than 3D structure at all spatial scales during the day, but an opposite trend occurs at night. Moreover, for the combined impact of building and tree on LST across different scales, daytime and nighttime LSTs are respectively dominated by 2D and 3D building structures. Combining 2D and 3D structures improves the explained LST variation by 7.3%–11.1% and 25.3%–37.7% for day and night, respectively when compared to using 2D structures only. These findings emphasize the need to incorporate both 2D and 3D urban morphology to improve the urban thermal environment.

Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.

Temporally regular and spatially continuous monitoring of surface urban heat island (SUHI) has been extremely difficult until the advent of spaceborne land surface temperature (LST) products. The higher errors of these LST products compared with in-situ measurements, nevertheless, have resulted in a comparatively inaccuracy and may distort the interpretation of SUHI. Although reports have shown that LST quality matters to the SUHI interpretation, a systematic investigation on how the SUHI indicators are responsive to the LST quality across cities within dissimilar bioclimates remains rare. With regard to this issue, our study chose eighty-six major cities across the mainland China and analyzed the SUHI intensity (SUHII) discrepancies (referred to as ΔSUHII) between using and not using quality control (QC) flags from Moderate Resolution Imaging Spectroradiometer data. Our major findings include: (1) the SUHII can be significantly impacted by the MODIS QC flags, and the associated seasonal ΔSUHIIs generally account for 25.5 % (29.6 %) of the total intensity in the day (night). (2) The ΔSUHIIs differ season-by-season and significant discrepancies also appear among northern and southern cities, with northern ones often possessing a higher annual mean ΔSUHII. (3) The internal ΔSUHIIs within an individual city are also heterogeneous, with the variations exceeding 5.0 K (3.0 K) in northern (southern) cities. (4) The ΔSUHII is significantly negatively related to the SUHII and cloud cover percentage mostly in transitional seasons. Our findings highlight that one needs to be very careful when using the LST-product-based SUHII to interpret the SUHI.