Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

Zhi, Zou; Zhan, Wenfeng; Liu, Zihan; Bechtel, Benjamin; Gao, Lun; Hong, Falu; Huang, Fan; Lai, Jiameng

doi:10.3390/rs10040650

Cited by 32 publications

(35 citation statements)

References 38 publications

(61 reference statements)

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“…LST at Suzhou was derived using the radiative transfer equation, based on the near-infrared bands of Landsat images [60][61][62]. We calculated vegetation coverage and land surface emissivity for atmospheric radiation correction [63,64], and converted surface heat radiation intensity to LST [65,66].…”

Section: Spring and Summer Lstmentioning

confidence: 99%

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

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Section: Spring and Summer Lstmentioning

confidence: 99%

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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“…Secondly, the seasonal pattern explored in this study was not complete due to the absence of spring, as the images in spring were all contaminated by cloud. In fact, the common cloud contamination has always impeded the spatio-temporal exploration of urban thermal environment despite the constant sampling frequency of the satellite sensors [10,12,81,82]. As a result, the patterns and dynamics of LST are supposed to partly depend on the LST data available [12,83].…”

Section: The Limitationsmentioning

confidence: 99%

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

et al. 2019

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There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.

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“…Generation of the global GEO LST products is a challenging task, and we also need to consider the effects of spectral mismatch and resolution inconsistency among multiple sensors, so intercalibration, pixel resampling, and correction of the time difference between different sensors are required [92]. In addition, we plan to use the gap-filling method [102,103] to recover the LSTs under cloudy conditions and to produce an all-sky LST product in the future, which will be more useful for different applications. In addition, the influence of topography on LST retrieval [104,105] was not considered in this study, and needs to be studied in the future.…”

Section: Discussionmentioning

confidence: 99%

An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

Sun

et al. 2020

Remote Sensing

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An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.

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Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

Cited by 32 publications

References 38 publications

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

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