statistical gap-filling of seviri land surface temperature

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ID: 166077
2020
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Abstract
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.
Reference Key
dumitrescu2020remotestatistical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Alexandru Dumitrescu;Marek Brabec;Sorin Cheval
Journal Journal of pharmacological sciences
Year 2020
DOI
10.3390/rs12091423
URL
https://www.mdpi.com/2072-4292/12/9/1423
Keywords
land surface temperature

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