Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Prikaziuk, Egor; Tol, Christiaan van der

doi:10.3390/rs11202424

Cited by 26 publications

(19 citation statements)

References 82 publications

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“…The results presented here, for Sentinel day and night products used to obtain the LST and the SUHI in the cities of Andalusia, present adequate yields that are consistent with each other and similar to those provided by similar investigations (Li et al 2011 ; Tan and Li 2015 ; Sobrino et al 2016 ; Prikaziuk and van der Tol 2019 ; Yang et al 2019 , 2020b ; Chiang and Ivan 2020 ; Hu et al 2020 ; Venter et al 2020 ).…”

Section: Discussionsupporting

confidence: 88%

“…The existing SW algorithms that serve to gauge LST are based on the concept of differential absorption (McMillin 1975), whereby the difference between the two TIRS band wavelengths allows for correction of the atmospheric effects produced on the signal. Abundant studies report on the validation, use and precision of these algorithms in Sentinel-3 images (Coppo et al 2010;Wan 2013;Ruescas et al 2016;Sobrino et al 2016;Prikaziuk and van der Tol 2019;Chiang and Ivan 2020;Yang et al 2020b).…”

Section: Sentinel-3 Images Land Surface Temperature Estimationmentioning

confidence: 99%

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Analysis of Urban Heat Island and Heat Waves Using Sentinel-3 Images: a Study of Andalusian Cities in Spain

García

2021

Earth Syst Environ

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At present, understanding the synergies between the Surface Urban Heat Island (SUHI) phenomenon and extreme climatic events entailing high mortality, i.e., heat waves, is a great challenge that must be faced to improve the quality of life in urban zones. The implementation of new mitigation and resilience measures in cities would serve to lessen the effects of heat waves and the economic cost they entail. In this research, the Land Surface Temperature (LST) and the SUHI were determined through Sentinel-3A and 3B images of the eight capitals of Andalusia (southern Spain) during the months of July and August of years 2019 and 2020. The objective was to determine possible synergies or interaction between the LST and SUHI, as well as between SUHI and heat waves, in a region classified as highly vulnerable to the effects of climate change. For each Andalusian city, the atmospheric variables of ambient temperature, solar radiation, wind speed and direction were obtained from stations of the Spanish State Meteorological Agency (AEMET); the data were quantified and classified both in periods of normal environmental conditions and during heat waves. By means of Data Panel statistical analysis, the multivariate relationships were derived, determining which ones statistically influence the SUHI during heat wave periods. The results indicate that the LST and the mean SUHI obtained are statistically interacted and intensify under heat wave conditions. The greatest increases in daytime temperatures were seen for Sentinel-3A in cities by the coast (LST = 3.90 °C, SUHI = 1.44 °C) and for Sentinel-3B in cities located inland (LST = 2.85 °C, SUHI = 0.52 °C). The existence of statistically significant positive relationships above 99% (p < 0.000) between the SUHI and solar radiation, and between the SUHI and the direction of the wind, intensified in periods of heat wave, could be verified. An increase in the urban area affected by the SUHI under heat wave conditions is reported. Graphical Abstract

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Section: Discussionsupporting

confidence: 88%

Section: Sentinel-3 Images Land Surface Temperature Estimationmentioning

confidence: 99%

Analysis of Urban Heat Island and Heat Waves Using Sentinel-3 Images: a Study of Andalusian Cities in Spain

García

2021

Earth Syst Environ

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“…As was mentioned in the introduction, many OLCI studies were conducted on synthetic (either model-simulated or resampled) datasets: simulation of TOA signal [8], estimation of leaf chlorophyll content [44], canopy chlorophyll content [45] grass chlorophyll and nitrogen content [46], nitrogen-phosphorous ratio [47], LAI [48], and biochemical parameters [10]. Wang and Atkinson [49] proposed an algorithm of Sentinel-2 spatio-temporal gap-filling with OLCI data for red, green, blue, and near-infrared bands.…”

Section: Time Series Applications For Landmentioning

confidence: 99%

“…Our team has explored the theoretical applications of the recent Sentinel-3 [7] products for land surface monitoring using model simulations [8][9][10]. However, at the following proof-of-concept step using the "real-world" data [11], we faced the bottlenecks mentioned above-the preparation of a time-series dataset takes several days.…”

Section: Introductionmentioning

confidence: 99%

Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

2021

Self Cite

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In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata.

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“…Variance-based SA (generally called Sobol SA) introduces uncertainty as variance and decomposes the variance of the output of the model or system into portions that can be attributed to inputs or sets of inputs [2,3]. Sobol SA is very popular; the principles of the method are often mentioned [4][5][6][7][8] and many articles have applied Sobol SA in their research [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles

Kala

2021

Symmetry

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The article introduces quantile deviation l as a new sensitivity measure based on the difference between superquantile and subquantile. New global sensitivity indices based on the square of l are presented. The proposed sensitivity indices are compared with quantile-oriented sensitivity indices subordinated to contrasts and classical Sobol sensitivity indices. The comparison is performed in a case study using a non-linear mathematical function, the output of which represents the elastic resistance of a slender steel member under compression. The steel member has random imperfections that reduce its load-carrying capacity. The member length is a deterministic parameter that significantly changes the sensitivity of the output resistance to the random effects of input imperfections. The comparison of the results of three types of global sensitivity analyses shows the rationality of the new quantile-oriented sensitivity indices, which have good properties similar to classical Sobol indices. Sensitivity indices subordinated to contrasts are the least comprehensible because they exhibit the strongest interaction effects between inputs. However, using total indices, all three types of sensitivity analyses lead to approximately the same conclusions. The similarity of the results of two quantile-oriented and Sobol sensitivity analysis confirms that Sobol sensitivity analysis is empathetic to the structural reliability and that the variance is one of the important characteristics significantly influencing the low quantile of resistance.

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Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Cited by 26 publications

References 82 publications

Analysis of Urban Heat Island and Heat Waves Using Sentinel-3 Images: a Study of Andalusian Cities in Spain

Analysis of Urban Heat Island and Heat Waves Using Sentinel-3 Images: a Study of Andalusian Cities in Spain

Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles

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