Thermal remote sensing

Thermal remote sensing is working on two major laws which are as follows:

1. Stefan–Boltzmann law: Surface temperature of any objects radiate energy and shows specific properties. These properties are calculated by Boltzmann law.

2. Wien's displacement law: Wien's displacement law explains the relation between temperature and the wavelength of radiation. It states that the wavelength of radiation emitted from a blackbody is inversely proportional to the temperature of the black body.

File:November 18 Grand Prix Fire, California (ASTER).jpg on NASA's Terra satellite acquired this image of the Old Fire/Grand Prix fire east of Los Angeles]]

Thermal remote sensing is used in applications including:

• Geothermal exploration{{Cite journal |last1=van der Meer |first1=Freek |last2=Hecker |first2=Christoph |last3=van Ruitenbeek |first3=Frank |last4=van der Werff |first4=Harald |last5=de Wijkerslooth |first5=Charlotte |last6=Wechsler |first6=Carolina |date=2014-12-01 |title=Geologic remote sensing for geothermal exploration: A review |url=https://www.sciencedirect.com/science/article/pii/S0303243414001275 |journal=International Journal of Applied Earth Observation and Geoinformation |volume=33 |pages=255–269 |doi=10.1016/j.jag.2014.05.007 |bibcode=2014IJAEO..33..255V |issn=1569-8432|url-access=subscription }}

• Identification of geological units and structures

• Urban heat islands{{Cite journal |last1=Mansourmoghaddam |first1=Mohammad |last2=Rousta |first2=Iman |last3=Zamani |first3=Mohammadsadegh |last4=Olafsson |first4=Haraldur |date=2023-04-01 |title=Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland) |url=https://doi.org/10.1007/s11252-023-01337-9 |journal=Urban Ecosystems |language=en |volume=26 |issue=2 |pages=337–359 |doi=10.1007/s11252-023-01337-9 |bibcode=2023UrbEc..26..337M |s2cid=257680037 |issn=1573-1642|url-access=subscription }}

• Soil moisture studies{{Cite journal |last1=Wang |first1=Lingli |last2=Qu |first2=John J. |date=2009-06-01 |title=Satellite remote sensing applications for surface soil moisture monitoring: A review |url=https://doi.org/10.1007/s11707-009-0023-7 |journal=Frontiers of Earth Science in China |language=en |volume=3 |issue=2 |pages=237–247 |doi=10.1007/s11707-009-0023-7 |issn=1673-7490|url-access=subscription }}

• Hydrology{{Cite journal |last1=Schmugge |first1=Thomas J. |last2=Kustas |first2=William P. |last3=Ritchie |first3=Jerry C. |last4=Jackson |first4=Thomas J. |last5=Rango |first5=Al |date=2002-08-01 |title=Remote sensing in hydrology |url=https://www.sciencedirect.com/science/article/pii/S0309170802000659 |journal=Advances in Water Resources |volume=25 |issue=8 |pages=1367–1385 |doi=10.1016/S0309-1708(02)00065-9 |bibcode=2002AdWR...25.1367S |issn=0309-1708|url-access=subscription }}

• Coastal zones{{Cite journal |last1=Melis |first1=Maria Teresa |last2=Da Pelo |first2=Stefania |last3=Erbì |first3=Ivan |last4=Loche |first4=Marco |last5=Deiana |first5=Giacomo |last6=Demurtas |first6=Valentino |last7=Meloni |first7=Mattia Alessio |last8=Dessì |first8=Francesco |last9=Funedda |first9=Antonio |last10=Scaioni |first10=Marco |last11=Scaringi |first11=Gianvito |date=January 2020 |title=Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides |journal=Remote Sensing |language=en |volume=12 |issue=12 |pages=1971 |doi=10.3390/rs12121971 |bibcode=2020RemS...12.1971M |issn=2072-4292 |doi-access=free |hdl=11584/291977 |hdl-access=free }}{{Cite journal |last1=Devi |first1=Gayathri K. |last2=Ganasri |first2=B. P. |last3=Dwarakish |first3=G. S. |date=2015-01-01 |title=Applications of Remote Sensing in Satellite Oceanography: A Review |journal=Aquatic Procedia |series=INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE'15) |volume=4 |pages=579–584 |doi=10.1016/j.aqpro.2015.02.075 |issn=2214-241X|doi-access=free |bibcode=2015AqPro...4..579D }}

• Volcanology

• Forest fires: Thermal remote sensing plays a vital role in the determination of Forest fire based on the principle of identifying fire pixel according to the temperature difference between the energy emitting from the surface and ambient temperature.

• Coal fires{{Cite journal |last1=Zhang |first1=J. |last2=Wagner |first2=W. |last3=Prakash |first3=A. |last4=Mehl |first4=H. |last5=Voigt |first5=S. |date=August 2004 |title=Detecting coal fires using remote sensing techniques |url=https://www.tandfonline.com/doi/full/10.1080/01431160310001620812 |journal=International Journal of Remote Sensing |language=en |volume=25 |issue=16 |pages=3193–3220 |doi=10.1080/01431160310001620812 |bibcode=2004IJRS...25.3193Z |s2cid=140197767 |issn=0143-1161|url-access=subscription }}

• Seismology{{Cite journal |last=Tronin |first=A. A. |date=2006-01-01 |title=Remote sensing and earthquakes: A review |url=https://www.sciencedirect.com/science/article/pii/S1474706506000337 |journal=Physics and Chemistry of the Earth, Parts A/B/C |series=Recent Progress in Seismo Electromagnetics and Related Phenomena |volume=31 |issue=4 |pages=138–142 |doi=10.1016/j.pce.2006.02.024 |bibcode=2006PCE....31..138T |issn=1474-7065|url-access=subscription }}

• Environmental modelling{{Cite journal |last=Weng |first=Qihao |date=2009-07-01 |title=Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends |url=https://www.sciencedirect.com/science/article/pii/S092427160900046X |journal=ISPRS Journal of Photogrammetry and Remote Sensing |volume=64 |issue=4 |pages=335–344 |doi=10.1016/j.isprsjprs.2009.03.007 |bibcode=2009JPRS...64..335W |issn=0924-2716|url-access=subscription }}

• Meteorology{{Cite journal |last1=McVicar |first1=Tim R. |last2=Jupp |first2=David L. B. |date=1999-09-15 |title=Estimating one-time-of-day meteorological data from standard daily data as inputs to thermal remote sensing based energy balance models |url=https://www.sciencedirect.com/science/article/pii/S0168192399000520 |journal=Agricultural and Forest Meteorology |volume=96 |issue=4 |pages=219–238 |doi=10.1016/S0168-1923(99)00052-0 |bibcode=1999AgFM...96..219M |issn=0168-1923|url-access=subscription }}

• Intelligence / military applications{{Cite journal |last1=Allison |first1=Robert S. |last2=Johnston |first2=Joshua M. |last3=Craig |first3=Gregory |last4=Jennings |first4=Sion |date=August 2016 |title=Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring |journal=Sensors |language=en |volume=16 |issue=8 |pages=1310 |doi=10.3390/s16081310 |issn=1424-8220 |pmc=5017475 |pmid=27548174 |bibcode=2016Senso..16.1310A |doi-access=free }}

• Heat loss from buildings{{Cite journal |last1=Nguyen |first1=Tran Xuan Bach |last2=Rosser |first2=Kent |last3=Chahl |first3=Javaan |date=October 2021 |title=A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation |journal=Journal of Imaging |language=en |volume=7 |issue=10 |pages=217 |doi=10.3390/jimaging7100217 |pmid=34677303 |pmc=8540138 |issn=2313-433X |doi-access=free }}

One of the most important applications of thermal remote sensing in earth sciences is to calculate the Land Surface Temperature (LST). LST is a measurement of how hot the land is to the touch. It differs from air temperature (the temperature given in weather reports) because land heats and cools more quickly than air.{{Cite web |date=2023-07-31 |title=Vegetation & Land Surface Temperature |url=https://earthobservatory.nasa.gov/global-maps/MOD_NDVI_M/MOD_LSTD_M |access-date=2023-12-11 |website=earthobservatory.nasa.gov |language=en}} LST is a key variable that is required to accurately model the surface energy budge.{{Cite journal |last1=Prata |first1=A. J. |last2=Caselles |first2=V. |last3=Coll |first3=C. |last4=Sobrino |first4=J. A. |last5=Ottlé |first5=C. |date=2009 |title=Thermal remote sensing of land surface temperature from satellites: Current status and future prospects |url=http://www.tandfonline.com/doi/abs/10.1080/02757259509532285 |journal=Remote Sensing Reviews |language=en |volume=12 |issue=3–4 |pages=175–224 |doi=10.1080/02757259509532285 |issn=0275-7257|url-access=subscription }} Thermal remote sensing from satellites to derive land surface temperatures has a long history that can be traced back to the TIROS-II satellite, launched in the early 60s.{{Cite journal |last1=Wark |first1=D. Q. |last2=Yamamoto |first2=G. |last3=Lienesch |first3=J. H. |date=1962-09-01 |title=Methods of Estimating Infrared Flux and Surface Temperature from Meteorological Satellites |journal=Journal of the Atmospheric Sciences |language=EN |volume=19 |issue=5 |pages=369–384 |doi=10.1175/1520-0469(1962)019<0369:MOEIFA>2.0.CO;2 |bibcode=1962JAtS...19..369W |issn=0022-4928|doi-access=free }} From the outset certain problems were recognised when deriving temperatures over the land, most notably the low temperatures observed over deserts. To quantify the effects of the atmosphere and the surface (emissivity effects) and, both from theory and experiment, various algorithms developed to derive LST. These algorithms are different in terms of accuracy and application.File:Land-12-00885-g008.png, obtained from Landsat 8 and Landsat 9 thermal bands. ]]

The Thematic Mapper (TM) sensor on Landsat 4 and Landsat 5 included a thermal (6th) band. Landsat 8 and Landsat-9 also acquires thermal data in two 10 and 11 bands from Thermal Infrared Sensor (TIRS).{{Cite web |title=What are the band designations for the Landsat satellites? {{!}} U.S. Geological Survey |url=https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites |access-date=2023-12-10 |website=www.usgs.gov}}

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) utilizes a unique combination of wide spectral coverage and high spatial resolution in the visible near-infrared through shortwave infrared to the thermal infrared regions. The ASTER instruments acquire thermal data in Thermal Infrared (TIR) 90 meter Bands (bands 10-14).

The Advanced Very High Resolution Radiometer (AVHRR) instrument on US National Oceanographic and Atmospheric Administration (NOAA) 9, 10, 11 and 12 had two bands in Thermal Infrared regions (bands 4, 5).{{Cite web |title=USGS EROS Archive - Advanced Very High Resolution Radiometer (AVHRR) - Sensor Characteristics {{!}} U.S. Geological Survey |url=https://www.usgs.gov/centers/eros/science/usgs-eros-archive-advanced-very-high-resolution-radiometer-avhrr-sensor |access-date=2023-12-11 |website=www.usgs.gov}}

Given recent developments in UAVs, thermal images with high spatial and temporal resolutions have become available at a low cost.

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Category:Remote sensing