Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing
"> Figure 1
<p>Map showing the 14 validation sites from the ocean colour (OC) component of the Aerosol Robotic Network (AERONET-OC) station. 1: Galata, 2: Gloria, 3: GOT Seaprism, 4: Gustav Dalen Tower, 5: Helsinki, 6: Lake Erie, 7: Long Island Sound Coastal Observatory (LISCO), 8: Martha’s Vineyard Coastal Observatory (MVCO), 9: Palgrunden, 10: Thornton C-Power; 11: USC Seaprism, 12: Venise, 13: WaveCIS Site CSI-6, 14: Zeebrugge-MOW1.</p> "> Figure 2
<p>Number of match-ups between Landsat 8 OLI scenes and AERONET-OC site measurements within ±30-min window of the Landsat 8 overpass (GAL: Galata, GLO: Gloria, GOT: Got Seaprism, GUS: Gustav Dalen Tower, HEL: Helsinki, ERIE: Lake Erie, LIS: LISCO, MVC: MVCO, PAL: Palgrunden, THO: Thornton C-Power, USC: USC Seaprism, VEN: Venise, WAV: WaveCIS Site CSI-6, ZEB: Zeebrugge-MOW1). Dark blue represents the total number of initial match-ups within a ±30-min time window of the Landsat 8 overpass times for each site. Light blue represents the total number of final match-ups used for analysis after excluding scenes with sunglint and performing the match-up exercise.</p> "> Figure 3
<p>Scatterplots of the relationship between in-situ measurements (x-axis) and OLI estimates (y-axis) for each OLI band acquired over 14 AERONET-OC sites. Regression lines are shown in colours, while the thick dotted black lines are 1:1 lines. (<b>a</b>) 443 nm; (<b>b</b>) 482 nm; (<b>c</b>) 561 nm; (<b>d</b>) 651 nm.</p> "> Figure 4
<p>Overall band-by-band RMSE and mean bias results for all algorithms.</p> "> Figure 5
<p>Scatterplots of the error (sr<sup>−1</sup>) showing the dependency of <span class="html-italic">R</span><sub>rs</sub> retrieval accuracy from both ACOLITE and SeaDAS on (<b>a</b>) AOT(869), (<b>b</b>) SZA, and (<b>c</b>) wind speed. AOT(869) and wind speed were derived from coincident measurements at each AERONET-OC site used in this study, while SZA was obtained by subtracting the sun elevation angle provided in the Landsat 8 metadata from 90. Each circle represents a match-up data point, for a total of 54 data points across the 14 AERONET-OC sites. The 54 match-ups and their corresponding environmental parameter values are tabulated in <a href="#remotesensing-11-00469-t0A2" class="html-table">Table A2</a>.</p> "> Figure A1
<p>The root-mean-square errors showing the impacts of per-band spectral adjustment on AERONET-OC match-ups. For all AC methods, there is no noticeable effect in the 443 nm channel. Similarly, for the land-based AC methods, there are no observable differences in the 443 and 482 nm channels. Band adjustment improves the results for bands 2, 3, and 4 for SeaDAS, decreasing RMSE values by 16.6, 23.9, and 43.8% in the 482, 561, and 655 nm wavelengths, respectively, and also improves results for bands 3 and 4 for ACOLITE by 15.6 and 24.2%, respectively. For SeaDAS, the largest observable difference is in the 655 nm channel. This is by far the largest improvement from band adjustment across all bands and AC methods. Overall, SeaDAS is the most sensitive method to spectral band differences, with the largest difference (improvement) in the 655 nm channel. (<b>a</b>) ARCSI; (<b>b</b>) LaSRC; (<b>c</b>) ACOLITE; (<b>d</b>) SeaDAS.</p> "> Figure A2
<p>Line graphs showing the <span class="html-italic">R</span><sub>rs</sub> spectra of each of the 14 AERONET-OC stations (Results were averaged for each station except GOT Seaprism for which only one match-up is available).</p> "> Figure A2 Cont.
<p>Line graphs showing the <span class="html-italic">R</span><sub>rs</sub> spectra of each of the 14 AERONET-OC stations (Results were averaged for each station except GOT Seaprism for which only one match-up is available).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Landsat 8 OLI Data
2.2. AERONET-OC Data
2.3. Match-Up Exercise
2.4. Data Processing
2.4.1. Description of Atmospheric Correction Algorithms
2.4.2. Atmospheric Correction Procedure and Validation
3. Results and Discussion
3.1. Validation of AC Algorithms
3.2. Inter-Comparison of Reflectance Spectra at Each Site
3.3. Influence of Environmental Factors for SeaDAS and ACOLITE
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Landsat Scene ID | Site |
---|---|
[‘LC81810302014141LGN00’ | Galata |
[‘LC81810302014253LGN00’ | Galata |
[‘LC81810302015240LGN00’ | Galata |
[‘LC81810302015352LGN00’ | Galata |
[‘LC81800292014086LGN00’ | Gloria |
[‘LC81800292014358LGN00’ | Gloria |
[‘LC81800292015041LGN00’ | Gloria |
[‘LC81800292015361LGN00’ | Gloria |
[‘LC81280542014026LGN00’ | GOT_Seaprism |
[‘LC81920192013151LGN00’ | Gustav_Dalen_Tower |
[‘LC81880182013235LGN00’ | Helsinki_Lighthouse |
[‘LC81880182014190LGN00’ | Helsinki_Lighthouse |
[‘LC81880182016180LGN00’ | Helsinki_Lighthouse |
[‘LC81880182016228LGN00’ | Helsinki_Lighthouse |
[‘LC81880182016260LGN00’ | Helsinki_Lighthouse |
[‘LC80200312016219LGN00’ | Lake_Erie |
[‘LC80200312016235LGN00’ | Lake_Erie |
[‘LC80130322013273LGN00’ | LISCO |
[‘LC80130322014004LGN00’ | LISCO |
[‘LC80130322015023LGN00’ | LISCO |
[‘LC80130322015279LGN00’ | LISCO |
[‘LC80130322016266LGN00’ | LISCO |
[‘LC80110312013291LGN00’ | MVCO |
[‘LC80110312014038LGN00’ | MVCO |
[‘LC80110312014150LGN00’ | MVCO |
[‘LC80110312015025LGN00’ | MVCO |
[‘LC80110312014086LGN00’ | MVCO |
[‘LC81950192013156LGN00’ | Palgrunden |
[‘LC81950192016165LGN00’ | Palgrunden |
[‘LC81990242016129LGN00’ | Thornton_C-power |
[‘LC81990242016305LGN00’ | Thornton_C-power |
[‘LC80410372014312LGN00’ | USC_SEAPRISM |
[‘LC80410372016222LGN00’ | USC_SEAPRISM_2 |
[‘LC80410372016318LGN00’ | USC_SEAPRISM_2 |
[‘LC80410372016334LGN00’ | USC_SEAPRISM_2 |
[‘LC81920292014106LGN00’ | Venise |
[‘LC81920292015013LGN00’ | Venise |
[‘LC81920292015221LGN00’ | Venise |
[‘LC81920292016016LGN00’ | Venise |
[‘LC81920292016128LGN00’ | Venise |
[‘LC81920292016192LGN00’ | Venise |
[‘LC81920292016240LGN00’ | Venise |
[‘LC80220402013240LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402013320LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402014019LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402014291LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402014323LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402015038LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402015342LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402016009LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402016041LGN00’ | WaveCIS_Site_CSI |
[‘LC80220402016073LGN00’ | WaveCIS_Site_CSI |
[‘LC81990242014091LGN00’ | Zeebrugge-MOW1 |
[‘LC81990242014219LGN00’ | Zeebrugge-MOW1 |
Station Date | SZA (0) | AOT 869 (nm) | Wind Speed (m/s) | Chlorophyll-a (mg/m3) |
---|---|---|---|---|
Galata_2014141 | 27.68254 | 0.061308 | 4.109681 | 1.15 |
Galata_2014253 | 41.58995 | 0.116449 | 3.284061 | 1.10 |
Galata_2015240 | 37.50297 | 0.058537 | 2.129808 | 0.73 |
Galata_2015352 | 68.64532 | 0.191727 | 4.643727 | 0.62 |
Gloria_2014086 | 45.39897 | 0.039736 | 1.40709 | 1.03 |
Gloria_2014358 | 69.93295 | 0.009096 | 13.20025 | 2.28 |
Gloria_2015041 | 62.24466 | 0.011158 | 9.488579 | 1.64 |
Gloria_2015361 | 70.11336 | 0.01644 | 8.966497 | 1.31 |
Got_2014026 | 39.26405 | 0.184762 | 2.348026 | 0.81 |
Gustav_2013151 | 37.76921 | 0.045492 | 7.765895 | 1.44 |
Helsinki_2013235 | 49.6655 | 0.045049 | 7.813921 | 4.11 |
Helsinki_2014190 | 38.82244 | 0.052049 | 5.058227 | 5.19 |
Helsinki_2016180 | 38.00992 | 0.036343 | 3.183139 | 3.87 |
Helsinki_2016228 | 47.29317 | 0.015965 | 5.356986 | 3.00 |
Helsinki_2016260 | 58.31484 | 0.014555 | 7.385065 | 3.66 |
LakeErie_2016219 | 31.06475 | 0.036835 | 4.838009 | 5.32 |
LakeErie_2016235 | 35.04874 | 0.032271 | 2.098577 | 5.84 |
LISCO_2013273 | 45.86886 | 0.02143 | 6.629846 | 6.12 |
LISCO_2014004 | 65.76056 | 0.009206 | 3.691909 | 3.92 |
LISCO_2015023 | 63.25687 | 0.01911 | 5.097444 | 5.36 |
LISCO_2015279 | 48.07922 | 0.025828 | 6.469751 | 4.84 |
LISCO_2016266 | 43.53788 | 0.03848 | 4.592692 | 4.06 |
MVCO_2013291 | 53.30351 | 0.016554 | 8.056089 | 3.24 |
MVCO_2014038 | 60.57072 | 0.025061 | 6.897844 | 4.52 |
MVCO_2014086 | 43.2755 | 0.042702 | 8.934463 | 4.96 |
MVCO_2014150 | 25.55208 | 0.054678 | 2.590076 | 1.50 |
MVCO_2015025 | 64.20812 | 0.036832 | 10.15678 | 5.03 |
Palgrunden_2013156 | 37.03093 | 0.01894 | 3.94127 | 7.58 |
Palgrunden_2016165 | 36.71529 | 0.013707 | 0.5948 | 6.87 |
Thornton_2016129 | 36.60009 | 0.070453 | 7.756932 | 16.3 |
Thornton_2016305 | 66.8652 | 0.058625 | 2.756128 | 3.24 |
USCSeaPrism_2014312 | 52.58024 | 0.028872 | 4.974118 | 0.22 |
USCSeaPrism_2016222 | 37.88999 | 0.074677 | 3.123159 | 0.63 |
USCSeaPrism_2016318 | 54.05641 | 0.027335 | 3.450807 | 0.30 |
USCSeaPrism_2016334 | 57.61152 | 0.026866 | 3.217656 | 0.61 |
Venise_2014106 | 37.88999 | 0.023221 | 6.324373 | 3.41 |
Venise_2015013 | 68.62708 | 0.039125 | 3.700884 | 1.19 |
Venise_2015221 | 33.40939 | 0.125445 | 3.092528 | 0.78 |
Venise_2016016 | 68.39465 | 0.011226 | 6.740557 | 0.58 |
Venise_2016128 | 31.50274 | 0.03962 | 1.123216 | 1.01 |
Venise_2016192 | 27.88954 | 0.085338 | 1.76539 | 1.59 |
Venise_2016240 | 38.46594 | 0.033166 | 1.342931 | 1.87 |
WaveCIS_2013240 | 28.31299 | 0.080524 | 3.036319 | 2.15 |
WaveCIS_2013320 | 50.72926 | 0.069036 | 6.575934 | 2.20 |
WaveCIS_2014019 | 54.4896 | 0.03491 | 7.112117 | 3.99 |
WaveCIS_2014291 | 42.45005 | 0.016669 | 3.183118 | 1.55 |
WaveCIS_2014323 | 51.5107 | 0.016451 | 2.907233 | 1.53 |
WaveCIS_2015038 | 50.60994 | 0.022994 | 2.271182 | 1.80 |
WaveCIS_2015342 | 55.17512 | 0.033926 | 1.131623 | 3.37 |
WaveCIS_2016009 | 56.01941 | 0.072489 | 4.151914 | 3.19 |
WaveCIS_2016041 | 49.98228 | 0.008506 | 5.38026 | 3.97 |
WaveCIS_2016073 | 39.38958 | 0.052527 | 5.027627 | 2.76 |
Zeebruge_2014091 | 49.09826 | 0.093231 | 2.259445 | 3.42 |
Zeebruge_2014219 | 37.90776 | 0.13111 | 3.071374 | 4.11 |
ACOLITE | LaSRC | SeaDAS | |||
---|---|---|---|---|---|
561 nm | USC Seaprism: 2016222 | 443 nm | WaveCIS: 2013320 | 443 nm | Palgrunden: 2013156 |
655 nm | Gloria: 2014358 USC Seaprism: 2016222 | 655 nm | WaveCIS: 2013320 MVCO: 2014150 | 655 nm | GOT Seaprism: 2014026 USC Seaprism: 2016222 Venise: 2015221 |
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Band centres (nm) | |||||||
MODIS | 443 | 488 | 555 | 645 | 858 | 1640 | 2130 |
SeaWiFS | 443 | 490 | 555 | 670 | 865 | NA | NA |
OLI | 443 | 482 | 56 | 655 | 865 | 1609 | 2201 |
Signal-to-noise ratio (SNR) | |||||||
MODIS | 838 | 802 | 228 | 128 | 201 | 275 | 110 |
SeaWiFS | 950 | 1000 | 850 | 500 | 350 | NA | NA |
OLI | 344 | 478 | 279 | 144 | 67 | 30 | 14 |
Ltyp (w m−2 µ−1 sr−) | |||||||
MODIS | 4.9 | 32.1 | 29 | 21.8 | 24.7 | 7.3 | 1.0 |
SeaWiFS | 70.2 | 53.1 | 33.9 | 8.3 | 4.5 | NA | NA |
OLI | 69.8 | 55.3 | 27.5 | 13.4 | 4.06 | 0.353 | 0.0467 |
R2 | Slope | RMSE (1/sr) | Intercept | p-Values | |
---|---|---|---|---|---|
Rrs 443 | |||||
ARCSI | 0.43 (0.41) | 0.91 (0.89) | 0.0085 (0.0085) | 0.0080 (0.0084) | 8.92e-08 |
ACOLITE | 0.70 (0.68) | 0.97 (0.97) | 0.0039 (0.0039) | 0.0036 (0.0037) | 4.16e-15 |
LaSRC | 0.05 (0.05) | 0.23 (0.25) | 0.0042 (0.0042) | 0.0050 (0.0050) | 0.11 |
SeaDAS | 0.84 (0.84) | 1.08 (1.08) | 0.0013 (0.0013) | −0.0006 (−0.0006) | 2.36e-22 |
Rrs 482 | |||||
ARCSI | 0.68 (0.63) | 1.01 (0.92) | 0.0065 (0.0063) | 0.0060 (0.0061) | 2.00e-13 |
ACOLITE | 0.85 (0.79) | 1.03 (0.94) | 0.0032 (0.0031) | 0.0027 (0.0029) | 1.99e-14 |
LaSRC | 0.44 (0.43) | 0.60 (0.56) | 0.0035 (0.0035) | 0.0041 (0.0041) | 3.77e-08 |
SeaDAS | 0.92 (0.87) | 1.09 (1.00) | 0.0012 (0.0015) | −0.0002 (0.00009) | 5.44e-30 |
Rrs 561 | |||||
ARCSI | 0.77 (0.77) | 0.95 (0.97) | 0.0051 (0.0048) | 0.0046 (0.0042) | 5.27e-18 |
ACOLITE | 0.92 (0.87) | 1.00 (0.98) | 0.0016 (0.0019) | 0.0005 (0.0002) | 1.38e-29 |
LaSRC | 0.80 (0.78) | 0.83 (0.83) | 0.0030 (0.0029) | 0.0027 (0.0025) | 9.48e-20 |
SeaDAS | 0.95 (0.92) | 1.03 (1.21) | 0.0012 (0.0011) | 0.00005 (−0.0003) | 1.13e-34 |
Rrs 665 | |||||
ARCSI | 0.64 (0.63) | 0.91 (1.06) | 0.0033 (0.0034) | 0.0028 (0.0026) | 4.49e-13 |
ACOLITE | 0.93 (0.89) | 0.98 (1.13) | 0.0010 (0.0013) | 0.0006 (0.0005) | 1.91e-31 |
LaSRC | 0.52 (0.50) | 0.65 (0.75) | 0.0022 (0.0021) | 0.0011 (0.0010) | 8.39e-10 |
SeaDAS | 0.97 (0.92) | 1.01 (1.21) | 0.0005 (0.0011) | −0.0001 (−0.0003) | 4.00e-40 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Ilori, C.O.; Pahlevan, N.; Knudby, A. Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing. Remote Sens. 2019, 11, 469. https://doi.org/10.3390/rs11040469
Ilori CO, Pahlevan N, Knudby A. Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing. Remote Sensing. 2019; 11(4):469. https://doi.org/10.3390/rs11040469
Chicago/Turabian StyleIlori, Christopher O., Nima Pahlevan, and Anders Knudby. 2019. "Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing" Remote Sensing 11, no. 4: 469. https://doi.org/10.3390/rs11040469