The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters

Wang, Dian; Ma, Ronghua; Xue, Kun; Loiselle, Steven

doi:10.3390/rs11020169

Cited by 79 publications

(39 citation statements)

References 70 publications

(80 reference statements)

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“…This study has identified Acolite SWIR AC algorithm as the most appropriate algorithm to retrieve Rhow in the green and red bands. Similar results were obtained in the inland waters of China, where Acolite SWIR and Acolite DSF provided better results compared with other tested AC algorithms [35]. These assessment results showed the Acolite SWIR and Acolite DSF algorithms can be effectively applied in turbid waters, while they perform poorly in clear waters.…”

Section: L8-olisupporting

confidence: 76%

“…The performance of different AC algorithms for L8-OLI data recorded over moderately turbid to turbid waters have been reported by several studies [1,[32][33][34][35]. The study conducted by [32] has used Acolite AC algorithm to derive SPM concentrations over the coastal mud banks of French Guiana.…”

Section: L8-olimentioning

confidence: 99%

“…Many studies did assess the performance of existing AC algorithms in moderately turbid waters for different satellite sensors such as SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) [28], MODIS-Aqua [29], GOCI (Geostationary Ocean Color Imager) [30], L8-OLI [31][32][33][34][35][36], S2-MSI [36][37][38][39][40][41] and S3-OLCI [42,43]. None of these studies considered the case of highly turbid waters for S3-OLCI.…”

mentioning

confidence: 99%

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

et al. 2020

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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.

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Section: L8-olisupporting

confidence: 76%

Section: L8-olimentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2020

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“…For example, Maciel et al [79] obtained MAPE values of about 20% for turbid waters in the Amazon Floodplain lakes. Wang et al [87] also observed MAPE values of about 20% for 6SV atmospheric correction for green and red bands of OLI (Operational Land Imager) Landsat-8 over eutrophic environments of lakes in China. In the present work, the variability between in situ and satellite-derived R rs may have influenced satellite OHA validation results ( Figure 9B Another factor that may have affected OHA performance when applied to the image was that not all sampling stations were collected on the same day of the satellite overpass.…”

Section: Oha Framework Assessmentmentioning

confidence: 94%

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

et al. 2019

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Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.

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“…The 6SV is an advanced radiative transfer code capable of accounting for radiation polarization in a mixed molecular-aerosol atmosphere being a vector version of the 6S [33]. The 6S atmospheric correction has already been successfully used in lakes and reservoirs [21,32,[34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

et al. 2020

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The Alqueva reservoir (South of Portugal) in the Guadiana river basin constitutes the most important water resource in southern Portugal for domestic and agricultural consumption. We present a methodology developed to characterize spatial and temporal variations of Secchi depth and diffuse attenuation coefficient (both related to dissolved/suspended particles and to water transparency), using high spatial resolution satellite images from Sentinel-2 Multi-Spectral Instrument (MSI). Empirical relations between satellite retrievals of surface reflectances and in situ measurements of water parameters were defined and applied to the entire reservoir for spatial and temporal analysis in the period July 2017-June 2019, useful in the identification of microalgae blooms and rapid variations in water characteristics, which allowed us to differentiate five zones. Water estimates with lower transparency and higher attenuation of radiation were found in the northern area of Alqueva reservoir during the months characterized by higher water temperatures, with Secchi depth monthly averages near 1.0 m and diffuse attenuation coefficient near or above 1.5 m −1 . Satellite retrievals of water with greater transparency in the reservoir were obtained in the southern area in months with low water temperature and atmospheric stability, presenting some monthly Secchi depth averages above 3 m, and diffuse attenuation coefficient below 0.8 m −1 . January 2018 presented great transparency of water with a Secchi depth of 7.5 m for pixels representing the 95th percentile and diffuse attenuation coefficient of 0.36 m for pixels representing the 5th percentile in the Southern region.

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The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters

Cited by 79 publications

References 70 publications

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

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

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

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