scholarly journals Comparison of Masking Algorithms for Sentinel-2 Imagery

2021 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Viktoria Zekoll ◽  
Magdalena Main-Knorn ◽  
Jerome Louis ◽  
David Frantz ◽  
Rudolf Richter ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Visual Assessment ◽  
Complete Classification ◽  
Buffer Size ◽  
Automated Processing ◽  
Classification Images ◽  
The Difference ◽  
True Color ◽  
Class Assignment ◽  
Sentinel 2

Masking of clouds, cloud shadow, water and snow/ice in optical satellite imagery is an important step in automated processing chains. We compare the performance of the masking provided by Fmask (“Function of mask” implemented in FORCE), ATCOR (“Atmospheric Correction”) and Sen2Cor (“Sentinel-2 Correction”) on a set of 20 Sentinel-2 scenes distributed over the globe covering a wide variety of environments and climates. All three methods use rules based on physical properties (Top of Atmosphere Reflectance, TOA) to separate clear pixels from potential cloud pixels, but they use different rules and class-specific thresholds. The methods can yield different results because of different definitions of the dilation buffer size for the classes cloud, cloud shadow and snow. Classification results are compared to the assessment of an expert human interpreter using at least 50 polygons per class randomly selected for each image. The class assignment of the human interpreter is considered as reference or “truth”. The interpreter carefully assigned a class label based on the visual assessment of the true color and infrared false color images and additionally on the bottom of atmosphere (BOA) reflectance spectra. The most important part of the comparison is done for the difference area of the three classifications considered. This is the part of the classification images where the results of Fmask, ATCOR and Sen2Cor disagree. Results on difference area have the advantage to show more clearly the strengths and weaknesses of a classification than results on the complete image. The overall accuracy of Fmask, ATCOR, and Sen2Cor for difference areas of the selected scenes is 45%, 56%, and 62%, respectively. User and producer accuracies are strongly class- and scene-dependent, typically varying between 30% and 90%. Comparison of the difference area is complemented by looking for the results in the area where all three classifications give the same result. Overall accuracy for that “same area” is 97% resulting in the complete classification in overall accuracy of 89%, 91% and 92% for Fmask, ATCOR and Sen2Cor respectively.

scholarly journals AEROSOL PLUMES CHARACTERIZATION BY HYPERSPECTRAL IMAGES COUPLED WITH SENTINEL-2 PRODUCTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 791-797
Author(s):  
G. Calassou ◽  
P.-Y. Foucher ◽  
J.-F. Leon
Keyword(s):  
Optical Thickness ◽  
Atmospheric Correction ◽  
Principal Component ◽  
Aerosol Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Multispectral Data ◽  
The Difference ◽  
Industrial Aerosol ◽  
Sentinel 2

Abstract. In this paper, we focus on the retrieval of microphysical and optical properties of industrial aerosol plumes through a process using airborne hyperspectral and Sentinel-2 multi-spectral images. The process allows first to perform atmospheric correction and second to determine background aerosols thanks to a comparison between hyperspectral and Sentinel-2 reflectances. Hyperspectral methodologies use the radiance differential between the measurement in the plume and the corresponding measurements out of the plume to estimate plume properties. To retrieve the surface reflectance under the plume, a principal component analysis coupling hyperspectral and multispectral data class by class is achieved. The developed method aims to compare the difference between measured and estimated reflectance with a radiative transfer model accounting for plume properties (particle radius and aerosol optical thickness of the plume). We have applied the method to a steel plant in the south of France. The retrieved plume show an aerosol mean radius between 0.05 and 0.2 µm with a mean aerosol optical thickness about 0.05 along the plume.

scholarly journals Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

scholarly journals Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series

2021 ◽  
Vol 13 (2) ◽  
pp. 205
Author(s):  
Philipp Hochreuther ◽  
Niklas Neckel ◽  
Nathalie Reimann ◽  
Angelika Humbert ◽  
Matthias Braun
Keyword(s):  
Time Series ◽  
Observation Interval ◽  
Lake Area ◽  
Climate Data ◽  
Melt Pond ◽  
Automated Processing ◽  
Average Observation ◽  
Average Size ◽  
Lake Size ◽  
Sentinel 2

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79°N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

scholarly journals Determination of land surface reflectance using the AATSR dual-view capability

2014 ◽  
Vol 7 (7) ◽  
pp. 7451-7494
Author(s):  
L. Sogacheva ◽  
P. Kolmonen ◽  
T. H. Virtanen ◽  
E. Rodriguez ◽  
A.-M. Sundström ◽  
...  
Keyword(s):  
Land Surface ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Surface Reflectance ◽  
Imaging Spectrometer ◽  
Comparison Results ◽  
Moderate Resolution ◽  
The Difference ◽  
Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995–2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite.

scholarly journals Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes

2018 ◽  
Vol 73 ◽  
pp. 63-76 ◽  
Author(s):  
Ion Sola ◽  
Alberto García-Martín ◽  
Leire Sandonís-Pozo ◽  
Jesús Álvarez-Mozos ◽  
Fernando Pérez-Cabello ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Mediterranean Landscapes ◽  
Sentinel 2

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 Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

2020 ◽  
Vol 12 (6) ◽  
pp. 946 ◽  
Author(s):  
Yafei Luo ◽  
David Doxaran ◽  
Quinten Vanhellemont
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Quality Parameters ◽  
Satellite Constellations ◽  
Water Turbidity ◽  
Gironde Estuary ◽  
The Difference

This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.

scholarly journals Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band

2019 ◽  
Vol 11 (3) ◽  
pp. 355 ◽  
Author(s):  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
Liangyun Liu
Keyword(s):  
Chlorophyll Fluorescence ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Correction Method ◽  
Oxygen Absorption ◽  
Atmospheric Transmission ◽  
Absorption Bands ◽  
Transfer Path ◽  
The Difference ◽  
Atmospheric Transmittance

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band.

scholarly journals Evaluation of atmospheric correction algorithms for Sentinel-2 over paddy field area

2020 ◽  
Vol 500 ◽  
pp. 012081
Author(s):  
Kurnia Ulfa ◽  
Hendayani ◽  
Masnita Indriani Oktavia ◽  
Kuncoro Adi Pradono ◽  
Liana Fibriawati ◽  
...  
Keyword(s):  
Paddy Field ◽  
Atmospheric Correction ◽  
Field Area ◽  
Sentinel 2
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