Statistical Gap-Filling of SEVIRI Land Surface Temperature
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00524079" target="_blank" >RIV/67985807:_____/20:00524079 - isvavai.cz</a>
Result on the web
<a href="http://hdl.handle.net/11104/0308416" target="_blank" >http://hdl.handle.net/11104/0308416</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/rs12091423" target="_blank" >10.3390/rs12091423</a>
Alternative languages
Result language
angličtina
Original language name
Statistical Gap-Filling of SEVIRI Land Surface Temperature
Original language description
A reliable and practically useable method for gap filling in hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI LST) data using ERA5 Land Skin Temperature (ERA5ST) co-variate and additional easily accessible data (elevation, time, solar radiation info) is proposed. The suggested approach provides estimates to all weather conditions and it is based on a probabilistic model via modern regression models. We have tested two classes of regression models of different complexity and flexibility, namely multiple linear regression (MLR), and generalized additive model (GAM). This analysis uses as main input the hourly LST data set over Romania, through 2016 and 2017, extracted from MSG-SEVIRI images, which is an operational product of the Land Surface Analysis–Satellite Application Facility (LSA-SAF). The comparison between the estimated LST and the original LST values shows that GAM model, that takes into account the distance between missing LST locations and the nearest non-missing locations (GAM2), provides the best results, hence this was used to fill the gaps from the analyzed remote sensing product. Considering the fact that the best covariate (ERA5ST) has global coverage and it is available at high spatial resolution and temporal resolution, the proposed approach could be also used to perform the gap-filling of other existing LST remote sensing products.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing
ISSN
2072-4292
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
9
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
1423
UT code for WoS article
000543394000070
EID of the result in the Scopus database
2-s2.0-85085279778