Comparison of Diurnal Variation of Land Surface Temperature From GOES-16 ABI and MODIS Instruments

Beale, Christopher; Norouzi, Hamidreza; Sharifnezhadazizi, Z.; Bah, A. R.; Yu, Peng; Yu, Yunyue; Blake, Reginald; Vaculik, Anna F.; González-Cruz, Jorge E.

doi:10.1109/lgrs.2019.2930174

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…Compared with MODIS LST data, ECOSTRESS LST data show higher temporal and spatial resolution and can more accurately reflect temperature differences. Our study shows that LST spatial distribution in urban areas varied between day and night, which is consistent with the findings of previous studies ( 71 , 72 ). The daytime differences in LST are mainly related to the type of ground cover and material; for instance, building materials and impermeable surfaces tend to absorb more energy than vegetation.…”

Section: Discussionsupporting

confidence: 93%

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Shi

Yang

Wang

et al. 2022

Front. Public Health

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High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

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Section: Discussionsupporting

confidence: 93%

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Shi

Yang

Wang

et al. 2022

Front. Public Health

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“…Average differences are within AE2.0°C for GOES during both daytime and nighttime, whereas MODIS has higher discrepancies (up to AE5.0°C). A strong seasonal cycle can be noted in MODIS during the daytime, with the largest positive differences in summer, consistent with prior efforts (Li, Yu, et al 2014;Beale et al 2019;Martin et al 2019). Overall, GOES shows better agreement with LST at the EC tower as compared to MODIS especially during the summer, which may seem counterintuitive considering the higher spatial resolution of MODIS.…”

Section: Model Evaluations At Ec Towersupporting

confidence: 80%

A Multiyear Assessment of Irrigation Cooling Capacity in Agricultural and Urban Settings of Central Arizona

Hanzo

Vivoni

Bohn

2021

J American Water Resour Assoc

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“…The accuracy of the GOES-R LST products is found to be about 2.5 K, and that meets the mission requirements [20]. A comparison of diurnal variations in LST from the GOES-R ABI and MODIS sensors over North America showed a general agreement with each-other except for the mountainous regions [44]. LST data at 5 -min temporal resolution and about 2 km spatial resolution over the Continental United States were obtained from the NOAA for 2017 and 2018.…”

Section: Goes-r Datasupporting

confidence: 52%

“…Although SDM marginally overestimates LST as compared to Landsat-8 observations, the bias is 0.75 K for 31 August 2017, whereas it is as low as 0.26 K for 28 April 2018. The general overestimation of LST by GOES-R as compared to Landsat-8 was also observed over North America [44]. The root-mean-square error (RMSE) in the SDM LST is less than 1.5 K in both cases.…”

Section: Resultsmentioning

confidence: 76%

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

et al. 2022

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The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

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Comparison of Diurnal Variation of Land Surface Temperature From GOES-16 ABI and MODIS Instruments

Cited by 13 publications

References 26 publications

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective

A Multiyear Assessment of Irrigation Cooling Capacity in Agricultural and Urban Settings of Central Arizona

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

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