Satellite air temperature estimation for monitoring the canopy layer heat island of Milan

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Satellite air temperature estimation for monitoring the canopy layer heat island of Milan. / Pichierri, Manuele; Bonafoni, Stefania; Biondi, Riccardo.

In: Remote Sensing of Environment, Vol. 127, 2012, p. 130-138.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Author

Pichierri, Manuele; Bonafoni, Stefania; Biondi, Riccardo / Satellite air temperature estimation for monitoring the canopy layer heat island of Milan.

In: Remote Sensing of Environment, Vol. 127, 2012, p. 130-138.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Bibtex

@article{358f2dda289b468cb073f0c6e2977298,
title = "Satellite air temperature estimation for monitoring the canopy layer heat island of Milan",
keywords = "Canopy layer heat island, Satellite-based sensor, Air temperature, Retrieval algorithm",
publisher = "Elsevier Inc.",
author = "Manuele Pichierri and Stefania Bonafoni and Riccardo Biondi",
year = "2012",
doi = "10.1016/j.rse.2012.08.025",
volume = "127",
pages = "130--138",
journal = "Remote Sensing of Environment",
issn = "0034-4257",

}

RIS

TY - JOUR

T1 - Satellite air temperature estimation for monitoring the canopy layer heat island of Milan

A1 - Pichierri,Manuele

A1 - Bonafoni,Stefania

A1 - Biondi,Riccardo

AU - Pichierri,Manuele

AU - Bonafoni,Stefania

AU - Biondi,Riccardo

PB - Elsevier Inc.

PY - 2012

Y1 - 2012

N2 - In this work, satellite maps of the urban heat island of Milan are produced using satellite-based infrared sensor data. For this aim, we developed suitable algorithms employing satellite brightness temperatures for the direct air temperature estimation 2 m above the surface (canopy layer), showing accuracies below 2K. The air temperatures measured by ground-based weather stations were properly matched with brightness temperatures observed by the Moderate-resolution Imaging Spectroradiometer (MODIS) on board of both Terra and Aqua satellites. In total, 931 daytime and nighttime scenes taken between 2007 and 2010 were processed. Analysis of the canopy layer heat island (CLHI) maps during summer months reveals an average heat island effect of 3–4K during nighttime (with some peaks around 5K) and a weak CLHI intensity during daytime. In addition, the satellite maps reveal a well defined island shape across the city center from June to September confirming that, in Milan, urban heating is not an occasional phenomenon. Furthermore, this study shows the utility of space missions to monitor the metropolis heat islands if they are able to provide nighttime observations when CLHI peaks are generally significant.

AB - In this work, satellite maps of the urban heat island of Milan are produced using satellite-based infrared sensor data. For this aim, we developed suitable algorithms employing satellite brightness temperatures for the direct air temperature estimation 2 m above the surface (canopy layer), showing accuracies below 2K. The air temperatures measured by ground-based weather stations were properly matched with brightness temperatures observed by the Moderate-resolution Imaging Spectroradiometer (MODIS) on board of both Terra and Aqua satellites. In total, 931 daytime and nighttime scenes taken between 2007 and 2010 were processed. Analysis of the canopy layer heat island (CLHI) maps during summer months reveals an average heat island effect of 3–4K during nighttime (with some peaks around 5K) and a weak CLHI intensity during daytime. In addition, the satellite maps reveal a well defined island shape across the city center from June to September confirming that, in Milan, urban heating is not an occasional phenomenon. Furthermore, this study shows the utility of space missions to monitor the metropolis heat islands if they are able to provide nighttime observations when CLHI peaks are generally significant.

KW - Canopy layer heat island

KW - Satellite-based sensor

KW - Air temperature

KW - Retrieval algorithm

U2 - 10.1016/j.rse.2012.08.025

DO - 10.1016/j.rse.2012.08.025

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

VL - 127

SP - 130

EP - 138

ER -