A Method to Downscale Satellite Microwave Land-Surface Temperature

High-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) ins...

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Main Authors: Samuel Favrichon, Catherine Prigent, Carlos Jiménez
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/7/1325
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spelling doaj-413a08b6a2904a72b7755110fd66c7012021-03-31T23:01:25ZengMDPI AGRemote Sensing2072-42922021-03-01131325132510.3390/rs13071325A Method to Downscale Satellite Microwave Land-Surface TemperatureSamuel Favrichon0Catherine Prigent1Carlos Jiménez2Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, 75006 Paris, FranceSorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, 75006 Paris, FranceEstellus, 75002 Paris, FranceHigh-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) instruments can also be used to provide surface-temperature measurements but suffer from coarser spatial resolutions. To increase their resolution, a downscaling methodology applicable over different land environments and at any time of the day is proposed. The method uses a statistical relationship between clear sky-predicting variables and clear-sky temperatures to estimate temperature patterns that can be used in conjunction with coarse measurements to create high-resolution products. Different predicting variables are tested showing the need to use IR-derived information on vegetation, temperature diurnal evolution, and a temporal information. To build a true ‘all-weather’ methodology, the effect of clouds on surface temperatures is accounted for by correcting the clear-sky diurnal cycle amplitude, using cloud parameters from meteorological reanalysis. Testing the method on a coarse IR synthetic data at ∼25 km resolution yields a Root Mean Square Deviations (RMSD) between the ∼5 km high-resolution and downscaled temperatures smaller than 1 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C. When applied to observations by the Special Sensor Microwave Imager Sounder (SSMIS) at ∼25 km resolution, the downscaling to ∼5 km yields a smaller RMSD compared to IR observations. These results demonstrate the relevance of the methodology to downscale MW land-surface temperature and its potential to spatially enhanced the current ‘all-weather’ satellite monitoring of surface temperatures.https://www.mdpi.com/2072-4292/13/7/1325microwave remote sensingland-surface temperatureall-weatherspatial resolution
collection DOAJ
language English
format Article
sources DOAJ
author Samuel Favrichon
Catherine Prigent
Carlos Jiménez
spellingShingle Samuel Favrichon
Catherine Prigent
Carlos Jiménez
A Method to Downscale Satellite Microwave Land-Surface Temperature
Remote Sensing
microwave remote sensing
land-surface temperature
all-weather
spatial resolution
author_facet Samuel Favrichon
Catherine Prigent
Carlos Jiménez
author_sort Samuel Favrichon
title A Method to Downscale Satellite Microwave Land-Surface Temperature
title_short A Method to Downscale Satellite Microwave Land-Surface Temperature
title_full A Method to Downscale Satellite Microwave Land-Surface Temperature
title_fullStr A Method to Downscale Satellite Microwave Land-Surface Temperature
title_full_unstemmed A Method to Downscale Satellite Microwave Land-Surface Temperature
title_sort method to downscale satellite microwave land-surface temperature
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-03-01
description High-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) instruments can also be used to provide surface-temperature measurements but suffer from coarser spatial resolutions. To increase their resolution, a downscaling methodology applicable over different land environments and at any time of the day is proposed. The method uses a statistical relationship between clear sky-predicting variables and clear-sky temperatures to estimate temperature patterns that can be used in conjunction with coarse measurements to create high-resolution products. Different predicting variables are tested showing the need to use IR-derived information on vegetation, temperature diurnal evolution, and a temporal information. To build a true ‘all-weather’ methodology, the effect of clouds on surface temperatures is accounted for by correcting the clear-sky diurnal cycle amplitude, using cloud parameters from meteorological reanalysis. Testing the method on a coarse IR synthetic data at ∼25 km resolution yields a Root Mean Square Deviations (RMSD) between the ∼5 km high-resolution and downscaled temperatures smaller than 1 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C. When applied to observations by the Special Sensor Microwave Imager Sounder (SSMIS) at ∼25 km resolution, the downscaling to ∼5 km yields a smaller RMSD compared to IR observations. These results demonstrate the relevance of the methodology to downscale MW land-surface temperature and its potential to spatially enhanced the current ‘all-weather’ satellite monitoring of surface temperatures.
topic microwave remote sensing
land-surface temperature
all-weather
spatial resolution
url https://www.mdpi.com/2072-4292/13/7/1325
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