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      Long‐Term and Fine‐Scale Surface Urban Heat Island Dynamics Revealed by Landsat Data Since the 1980s: A Comparison of Four Megacities in China

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          Abstract

          Long‐term and fine‐scale monitoring of surface urban heat island (SUHI) is critical for the design of heat mitigation strategies. Landsat series offer long‐term (since the 1980s) and fine‐scale land surface temperature (LST) observations for such SUHI analysis. However, Landsat data are characterized by a long revisit period (16 days) and are seriously impacted by cloud contamination and stripe gaps, making the Landsat‐derived long‐term SUHI trends across cities incomparable. To address this issue, here we applied the Prophet model to reconstruct temporally consistent long‐term (∼1985–2019) clear‐sky Landsat LSTs over four megacities in China (Beijing, Chongqing, Shanghai, and Shenzhen). The results show that the mean absolute error of the SUHI intensity (SUHII) estimated using the reconstructed LSTs ranges from 0.2 to 0.6°C. The reliability of the reconstructed LSTs is further evidenced by the general consistency between the reconstructed Landsat and original Moderate Resolution Imaging Spectroradiometer LSTs. Our analysis further demonstrates that the overall SUHII has been increasing slowly over Beijing but has been increasing rapidly in the other three megacities since the 1980s. Local SUHII trends also varied between urban regions. Urban core is characterized by an initial increase and then a slight decrease in the local SUHII, while a continuously increasing trend is observed for the urban fringe. For the new urban development, the interannual SUHII trend lies somewhere between these trends. Overall, our study provides a practical approach to reconstruct long‐term clear‐sky Landsat LSTs, and delivers a better understanding of long‐term and fine‐scale SUHI variations across cities.

          Key Points

          • Landsat land surface temperatures since the 1980s are reconstructed in four Chinese megacities using the Prophet model

          • Reconstructed Landsat land surface temperatures match well Moderate Resolution Imaging Spectroradiometer land surface temperatures in depicting interannual trends

          • Long‐term and fine‐scale variations of the overall surface urban heat island intensity (SUHII) and region‐based local SUHII are compared over four megacities

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          Google Earth Engine: Planetary-scale geospatial analysis for everyone

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Impact of regional climate change on human health.

              The World Health Organisation estimates that the warming and precipitation trends due to anthropogenic climate change of the past 30 years already claim over 150,000 lives annually. Many prevalent human diseases are linked to climate fluctuations, from cardiovascular mortality and respiratory illnesses due to heatwaves, to altered transmission of infectious diseases and malnutrition from crop failures. Uncertainty remains in attributing the expansion or resurgence of diseases to climate change, owing to lack of long-term, high-quality data sets as well as the large influence of socio-economic factors and changes in immunity and drug resistance. Here we review the growing evidence that climate-health relationships pose increasing health risks under future projections of climate change and that the warming trend over recent decades has already contributed to increased morbidity and mortality in many regions of the world. Potentially vulnerable regions include the temperate latitudes, which are projected to warm disproportionately, the regions around the Pacific and Indian oceans that are currently subjected to large rainfall variability due to the El Niño/Southern Oscillation sub-Saharan Africa and sprawling cities where the urban heat island effect could intensify extreme climatic events.
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                Author and article information

                Contributors
                Journal
                Journal of Geophysical Research: Atmospheres
                JGR Atmospheres
                American Geophysical Union (AGU)
                2169-897X
                2169-8996
                March 16 2022
                March 08 2022
                March 16 2022
                : 127
                : 5
                Affiliations
                [1 ] Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Nanjing China
                [2 ] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China
                Article
                10.1029/2021JD035598
                0d0cd416-4031-4175-8d34-a465d3f8bfdf
                © 2022

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