Hourly mapping of surface air temperature by blending geostationary datasets from the two-satellite system of GOES-R series

Zhang, Zhenwei; Du, Qingyun

doi:10.1016/j.isprsjprs.2021.10.022

Cited by 21 publications

(1 citation statement)

References 69 publications

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“…Satellite observations, whether obtained by polar‐orbiting or geostationary satellites, provide high‐resolution estimates of surface temperature. The launch of the Geostationary Operational Environmental Satellite (GOES)‐16 and 17 satellites (hereafter referred to collectively as GOES) significantly expanded the scope for detailed temperature observations across the US (Chang et al., 2021; Li et al., 2021; Yu et al., 2008; Zhang & Du, 2022). These satellites offer sub‐hourly estimates, enabling nearly continuous monitoring of the surface.…”

Section: Introductionmentioning

confidence: 99%

Improved Surface Urban Heat Impact Assessment Using GOES Satellite Data: A Comparative Study With ERA‐5

Lee,

Dessler

2024

Geophysical Research Letters

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We compare high‐resolution land‐surface temperature (LST) estimates from the GOES‐16/17 (GOES) satellites to ERA‐5 Land (ERA‐5) reanalysis data across nine large US cities. We quantify the offset and find that ERA‐5 generally overestimates LST compared to GOES by 1.63°C. However, this overestimation is less pronounced in urban areas, underscoring the limitations of ERA‐5 in capturing the LST gradient between urban and non‐urban areas. We then examine three quantities: Surface Urban Heat Island Intensity (SUHII), extreme LST events, and LST exposure by population. We find that ERA‐5 does not accurately represent the diurnal variation and magnitude of SUHII in GOES. Furthermore, while ERA‐5 was on average too warm, ERA‐5 underestimates extreme heat by an average of 2.40°C. Our analysis reveals higher population exposure to high LST in the GOES data set across the cities studied. This discrepancy is especially pronounced when estimating the population fraction that are most exposed to heat.

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Section: Introductionmentioning

confidence: 99%

Improved Surface Urban Heat Impact Assessment Using GOES Satellite Data: A Comparative Study With ERA‐5

Lee,

Dessler

2024

Geophysical Research Letters

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Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary

Guo,

Unger,

Khabibolla

et al. 2024

Theor Appl Climatol

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Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers.

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Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature

Hassani,

Santos,

Schneider

et al. 2023

Environ Model Assess

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Fine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.

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Hourly mapping of surface air temperature by blending geostationary datasets from the two-satellite system of GOES-R series

Cited by 21 publications

References 69 publications

Improved Surface Urban Heat Impact Assessment Using GOES Satellite Data: A Comparative Study With ERA‐5

Improved Surface Urban Heat Impact Assessment Using GOES Satellite Data: A Comparative Study With ERA‐5

Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary

Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature

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