scholarly journals Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

2020 ◽  
Vol 12 (8) ◽  
pp. 1285 ◽  
Author(s):  
Pannimpullath Remanan Renosh ◽  
David Doxaran ◽  
Liesbeth De Keukelaere ◽  
Juan Ignacio Gossn
Keyword(s):  
Satellite Data ◽  
Time Lag ◽  
Atmospheric Correction ◽  
Test Site ◽  
Minimum Time ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Estuarine Waters ◽  
Turbid Waters ◽  
Sentinel 2

The present study assesses the performance of state-of-the-art atmospheric correction (AC) algorithms applied to Sentinel-2-MultiSpectral Instrument (S2-MSI) and Sentinel-3-Ocean and Land Color Instrument (S3-OLCI) data recorded over moderately to highly turbid estuarine waters, considering the Gironde Estuary (SW France) as a test site. Three spectral bands of water-leaving reflectance ( R h o w ) are considered: green (560 nm), red (655 or 665 nm) and near infrared (NIR) (865 nm), required to retrieve the suspended particulate matter (SPM) concentrations in clear to highly turbid waters (SPM ranging from 1 to 2000 mg/L). A previous study satisfactorily validated Acolite short wave infrared (SWIR) AC algorithm for Landsat-8-Operational Land Imager (L8-OLI) in turbid estuarine waters. The latest version of Acolite Dark Spectrum Fitting (DSF) is tested here and shows very good agreement with Acolite SWIR for OLI data. L8-OLI satellite data corrected for atmospheric effects using Acolite DSF are then used as a reference to assess the validity of atmospheric corrections applied to other satellite data recorded over the same test site with a minimum time difference. Acolite DSF and iCOR (image correction for atmospheric effects) are identified as the best performing AC algorithms among the tested AC algorithms (Acolite DSF, iCOR, Polymer and C2RCC (case 2 regional coast color)) for S2-MSI. Then, the validity of six different AC algorithms (OLCI Baseline Atmospheric Correction (BAC), iCOR, Polymer, Baseline residual (BLR), C2RCC-V1 and C2RCC-V2) applied to OLCI satellite data is assessed based on comparisons with OLI and/or MSI Acolite DSF products recorded on a same day with a minimum time lag. Results show that all the AC algorithms tend to underestimate R h o w in green, red and NIR bands except iCOR in green and red bands. The iCOR provides minimum differences in green (slope = 1.0 ± 0.15, BIAS = 1.9 ± 4.5% and mean absolute percentage error (MAPE) = 12 ± 5%) and red (slope = 1.0 ± 0.17, BIAS = −9.8 ± 9% and MAPE = 28 ± 20%) bands with Acolite DSF products from OLI and MSI data. For the NIR band, BAC provides minimum differences (slope = 0.7 ± 0.13, BIAS = −33 ± 17% and MAPE = 55 ± 20%) with Acolite DSF products from OLI and MSI data. These results based on comparisons between almost simultaneous satellite products are supported by match-ups between satellite-derived and field-measured SPM concentrations provided by automated turbidity stations. Further validation of satellite products based on rigorous match-ups with in-situ R h o w measurements is still required in highly turbid waters.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1469 ◽  
Author(s):  
Marcela Pereira-Sandoval ◽  
Ana Ruescas ◽  
Patricia Urrego ◽  
Antonio Ruiz-Verdú ◽  
Jesús Delegido ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atmospheric Correction ◽  
Inland Waters ◽  
Atmospheric Effects ◽  
Image Correction ◽  
Lakes And Reservoirs ◽  
Dark Pixel ◽  
Organic Particles ◽  
Sentinel 2

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

scholarly journals SNOW COVER MAPPING AND ICE AVALANCHE MONITORING FROM THE SATELLITE DATA OF THE SENTINELS

2018 ◽  
Vol XLII-3 ◽  
pp. 1765-1772 ◽  
Author(s):  
S. Wang ◽  
B. Yang ◽  
Y. Zhou ◽  
F. Wang ◽  
R. Zhang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Satellite Data ◽  
Atmospheric Correction ◽  
Mapping Method ◽  
Backscattering Coefficient ◽  
Multispectral Data ◽  
Monitoring Frequency ◽  
Application Potential ◽  
Correction Result ◽  
Sentinel 2

In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2&amp;thinsp;h, which meet the requirements of disaster emergency monitoring.

scholarly journals RELATIVE RADIOMETRIC NORMALIZATION USING SEVERAL AUTOMATICALLY CHOSEN REFERENCE IMAGES FOR MULTI-SENSOR, MULTI-TEMPORAL SERIES

2020 ◽  
Vol V-2-2020 ◽  
pp. 845-852
Author(s):  
C. Hessel ◽  
R. Grompone von Gioi ◽  
J. M. Morel ◽  
G. Facciolo ◽  
P. Arias ◽  
...  
Keyword(s):  
Time Series ◽  
Atmospheric Correction ◽  
Linear Interpolation ◽  
Landsat 8 ◽  
Quality Metric ◽  
Multi Temporal ◽  
Reference Images ◽  
Random Sample Consensus ◽  
Affine Correction ◽  
Sentinel 2

Abstract. We propose a method for the relative radiometric normalization of long, multi-sensor image time series. This allows to increase the revisit time under comparable conditions. Although the relative radiometric normalization is a well-studied problem in the remote sensing community, the availability of an increasing number of images gives rise to new problems. For example, given long series spanning several years, finding features that are maintained through the whole period of time becomes arduous. Instead, we propose in this paper to use automatically detected reference images chosen by maximization of a quality metric. For each image, two affine correction models are robustly estimated using random sample consensus, using the two closest reference images; the final correction is obtained by linear interpolation. For each pair of source and reference images, pseudo-invariant features are obtained using a similarity measure invariant to radiometric changes. A final tone-mapping step outputs the images in the standard 8-bits range. This method is illustrated by the fusion of time series of Sentinel-2 at correction levels 1C, 2A, and Landsat-8 images. By using only the atmospherically corrected Sentinel-2 L2A images as anchors, the full output series inherits this atmospheric correction.

An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters

2018 ◽  
Vol 215 ◽  
pp. 18-32 ◽  
Author(s):  
Jianwei Wei ◽  
Zhongping Lee ◽  
Rodrigo Garcia ◽  
Laura Zoffoli ◽  
Roy A. Armstrong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coral Reefs ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Turbid Waters ◽  
Remote Sensing Reflectance

scholarly journals Assessment and inventory of forest parameters using medium-high resolution satellite data

2021 ◽  
Author(s):  
Ειρήνη Χρυσάφη
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Data ◽  
Earth Observation ◽  
Landsat 8 ◽  
Spectral Instrument ◽  
Stacked Generalization ◽  
Sentinel 2

Τα μεσογειακά δάση χαρακτηρίζονται από υψηλή χωροχρονική ετερογένεια και αποτελούν ένα από σημαντικότερα σημεία της βιοποικιλότητας στον πλανήτη. Η σημαντική αξία τους και το ευρύ φάσμα των οικοσυστημικών υπηρεσιών που παρέχουν, αναγνωρίζεται ευρέως από επιστήμονες, διεθνείς συμβάσεις και οργανισμούς. Ωστόσο, η ευπάθεια τους σε ανθρώπινες και φυσικές απειλές έχει ως αποτέλεσμα την διατάραξη τους. Συνεπώς, σχέδια βιώσιμης διαχείρισης και αειφορικής ανάπτυξης καθίστανται ως επιτακτική ανάγκη. Οι πρακτικές παρακολούθησης και απογραφής δασών απαιτούν την αξιόπιστη εκτίμηση δασικών παραμέτρων, όπως η κυκλική επιφάνεια, ο αριθμός δέντρων ανά μονάδα επιφάνειας και ξυλώδες όγκου. Η ετερογένεια των μεσογειακών δασών και η δύσκολη πρόσβασής τους, καθιστά την επιστήμη της τηλεπισκόπησης ως εξαιρετικά χρήσιμο μέσο για την αξιολόγηση των δασικών πόρων. Η τεχνολογία της τηλεπισκόπησης και τα ανοιχτά δεδομένα τηλεπισκόπησης παρέχουν μεγάλες δυνατότητες στον τομέα της δασολογίας και στην δασική απογραφή. Επιπλέον, η ταχεία πρόοδος στους αλγόριθμους τεχνητής νοημοσύνης διευκολύνει την ανάλυση ευρέος φάσματος δεδομένων. Σε αυτό το πλαίσιο, ο συνδυασμός αυτών των ισχυρών εργαλείων (δεδομένα τηλεπισκόπησης και προσεγγίσεις μηχανικής μάθησης) συνιστά μια πολλά υποσχόμενη, αλλά και ερευνητική πρόκληση, για την εκτίμηση δασικών παραμέτρων. Στη παρούσα διατριβή, εξετάζονται διάφορες προσεγγίσεις για την βελτιστοποίηση της εκτίμησης δασικών παραμέτρων με την χρήση δορυφορικών εικόνων και τεχνικών μηχανικής μάθησης.Η δομή της παρούσας διατριβής αποτελείται από τρία μέρη. Το πρώτο μέρος αποτελείται από τέσσερα κεφάλαια. Αρχικά, στο Κεφάλαιο 1, γίνεται μια εισαγωγή στην αξία των μεσογειακών δασών, στις υπηρεσίες που παρέχουν και στις απειλές που αντιμετωπίζουν. Το Κεφάλαιο 2 τονίζει την ανάγκη αειφορικής διαχείρισης των δασών και κατ 'επέκταση της απογραφής και αξιόπιστης εκτίμησης δασικών παραμέτρων. Στο Κεφάλαιο 3, παρουσιάζονται εν συντομία πηγές δεδομένων τηλεπισκόπησης και η συμβολή τους σε δασικές εφαρμογές και ιδιαίτερα στην εκτίμηση δασικών παραμέτρων, σε περιοχές της Μεσογείου. Το κεφάλαιο 4, αποτελεί μια εισαγωγή στους αλγόριθμους τεχνητής νοημοσύνης και μηχανικής μάθησης και πώς αυτές οι προσεγγίσεις εφαρμόζονται στον τομέα της τηλεπισκόπησης και της δασολογίας. Τέλος παρουσιάζονται τα ερευνητικά ερωτήματα και τα αντικείμενα της παρούσας διατριβής. Το δεύτερο μέρος αποτελείται από τέσσερα άρθρα, εκ των οποίων, το πρώτο (Κεφάλαιο 7) έχει δημοσιευτεί στο περιοδικό Remote Sensing of Environment (2017) και αφορά την εκτίμηση δασικών παραμέτρων χρησιμοποιώντας δια-εποχιακές εικόνες Landsat 8 Operational Land Imager. Το δεύτερο άρθρο (Κεφάλαιο 8) έχει δημοσιευτεί στο Remote Sensing Letters (2017) και αφορά τις σχέσεις μεταξύ ξυλώδες όγκου και εικόνων Sentinel-2 Multi Spectral Instrument. Το τρίτο άρθρο (Κεφάλαιο 9) έχει δημοσιευτεί στο περιοδικό International Journal of Applied Earth Observation and Geoinformation (2019) και αφορά την αξιολόγηση των δορυφορικών δεδομένων Sentinel-2 Multi Spectral Instrument για την εκτίμηση του ξυλώδες όγκου. Το τελευταίο άρθρο (Κεφάλαιο 10) που προορίζεται προς δημοσίευση, αποτελεί μια προκαταρκτική μελέτη για την εκτίμηση του ξυλώδες όγκου σε ένα μεσογειακό δασικό οικοσύστημα, με μία μετά-μαθησιακή προσέγγιση και την ανάπτυξη ενός μοντέλου συσσωρευμένης γενίκευσης (stacked generalization). Τέλος, στο τρίτο μέρος της παρούσας διατριβής παρουσιάζονται συνοπτικά οι απαντήσεις των ερωτημάτων που τέθηκαν στην παρούσα διατριβή και τα προβλήματα - περιορισμοί που αντιμετωπίστηκαν. Επίσης, προτείνονται δυνατότητες και προοπτικές εξέλιξης της παρούσας έρευνας, που θα μπορούσε να αποτελέσουν αντικείμενο για μελλοντική έρευνα.

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