Land-Surface Emissivity Retrieval in MSG-SEVIRI TIR Channels Using MODIS Data

A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized...

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Published in:IEEE transactions on geoscience and remote sensing Vol. 52; no. 9; pp. 5587 - 5600
Main Authors: Peres, Leonardo F., Libonati, Renata, DaCamara, Carlos C.
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Arid regions
Arid zones
Bias
Channels
Emissivity
Land surface
Land surface temperature
land-surface temperature (LST)
Libraries
linear regression
Materials
Meteorology
MODIS
MSG/SEVIRI
Regression analysis
Retrieval
Soil
TERRA/MODerate resolution Imaging Spectroradiometer (MODIS)
Vegetation mapping
linear regression
TERRA/MODerate resolution Imaging Spectroradiometer (MODIS)
Emissivity
MSG/SEVIRI
land-surface temperature (LST)
ISSN:0196-2892, 1558-0644
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Abstract A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized split window (SW) algorithm for land-surface temperature (LST) retrievals in the operational chain of the Satellite Application Facility on Land Surface Analysis (LSA SAF). Information from MODIS is incorporated by means of linear regression models expressing emissivity in SEVIRI thermal-infrared channels as a linear combination of emissivities in MODIS bands. The linear models are applied to the MODIS emissivity product MOD11C3, and a comparison is performed with the operational LSA-SAF product. Special attention is devoted to the semiarid and arid regions of North Africa where emissivity is highly variable. When compared with the new emissivity maps, the LSA-SAF product displays more uniform emissivity values over these regions, leading to higher retrievals for all channels (bias around 0.03) except for IR3.9 (bias from -0.05 to -0.08). The root-mean-square error (RMSE) varies from 0.06 to 0.09 (0.02 to 0.03) for IR3.9 (IR10.8 and IR12.0) and is about 0.06 for IR8.7. The impact on LST is assessed by comparing the retrievals from a SW algorithm using as input the following: 1) the SEVIRI emissivity LSA-SAF product and 2) SEVIRI emissivity maps from MOD11C3. The uncertainty in the LSA-SAF emissivity product results into LST values with bias ranging from -0.4 to -1.0 K and RMSE around 1.6 K. The new emissivity maps based on MODIS data may be an alternative to the standard LSA-SAF emissivity product over semiarid and arid areas, which cover 26% of the land surfaces within the SEVIRI full disk.
AbstractList A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized split window (SW) algorithm for land-surface temperature (LST) retrievals in the operational chain of the Satellite Application Facility on Land Surface Analysis (LSA SAF). Information from MODIS is incorporated by means of linear regression models expressing emissivity in SEVIRI thermal-infrared channels as a linear combination of emissivities in MODIS bands. The linear models are applied to the MODIS emissivity product MOD11C3, and a comparison is performed with the operational LSA-SAF product. Special attention is devoted to the semiarid and arid regions of North Africa where emissivity is highly variable. When compared with the new emissivity maps, the LSA-SAF product displays more uniform emissivity values over these regions, leading to higher retrievals for all channels (bias around 0.03) except for IR3.9 (bias from $-$0.05 to $-$0.08). The root-mean-square error (RMSE) varies from 0.06 to 0.09 (0.02 to 0.03) for IR3.9 (IR10.8 and IR12.0) and is about 0.06 for IR8.7. The impact on LST is assessed by comparing the retrievals from a SW algorithm using as input the following: 1) the SEVIRI emissivity LSA-SAF product and 2) SEVIRI emissivity maps from MOD11C3. The uncertainty in the LSA-SAF emissivity product results into LST values with bias ranging from $-$0.4 to $-$1.0 K and RMSE around 1.6 K. The new emissivity maps based on MODIS data may be an alternative to the standard LSA-SAF emissivity product over semiarid and arid areas, which cover 26% of the land surfaces within the SEVIRI full disk.
Author Peres, Leonardo F.
Libonati, Renata
DaCamara, Carlos C.
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crossref_primary_10_1016_j_rse_2018_04_033
crossref_primary_10_1109_TGRS_2019_2936297
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SubjectTerms Algorithms
Arid regions
Arid zones
Bias
Channels
Emissivity
Land surface
Land surface temperature
land-surface temperature (LST)
Libraries
linear regression
Materials
Meteorology
MODIS
MSG/SEVIRI
Regression analysis
Retrieval
Soil
TERRA/MODerate resolution Imaging Spectroradiometer (MODIS)
Vegetation mapping
Title Land-Surface Emissivity Retrieval in MSG-SEVIRI TIR Channels Using MODIS Data
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