Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake
"> Figure 1
<p>True color composite of atmospherically corrected Sentinel 2 MSI images captured on 15 August 2016 (<b>a</b>) and 2 April 2017 (<b>b</b>), respectively, showing Poyang Lake, and the sampling sites in 2010 (×) and 2011(+) are illustrated in (<b>a</b>).</p> "> Figure 2
<p>Remote sensing reflectance (<span class="html-italic">R</span><sub>rs</sub>) spectra and their corresponding suspended particulate matter concentrations (mg/L), and the relative spectral response function of the Sentinel 2 MSI (black dash curve) (<b>a</b>); and the correlation coefficient (r) between the <span class="html-italic">R</span><sub>rs</sub> and suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) (<b>b</b>).</p> "> Figure 3
<p>Suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) retrieval models based on Sentinel 2 MSI B4 (<b>a</b>) and B7 (<b>b</b>).</p> "> Figure 4
<p>Scatter plots of estimated against measured suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) for the validation dataset: Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>). The solid line is the regression line between the estimated and measured values, and the dashed line is 1:1 line.</p> "> Figure 4 Cont.
<p>Scatter plots of estimated against measured suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) for the validation dataset: Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>). The solid line is the regression line between the estimated and measured values, and the dashed line is 1:1 line.</p> "> Figure 5
<p>Suspended particulate matter concentrations (<span class="html-italic">C</span><sub>SPM</sub>) retrieved from Sentinel 2 MSI B4 (<b>a</b>), B7 (<b>b</b>), B8b (<b>c</b>), and MODIS Terra B1 (<b>d</b>) captured on 15 August 2016. The areas in the red rectangle are zoomed in to show the detailed <span class="html-italic">C</span><sub>SPM</sub> variations.</p> "> Figure 6
<p>Scatter plots of <span class="html-italic">C</span><sub>SPM</sub> values derived from Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>) against those from MODIS Terra B1 on 15 August 2016. The solid line is the regression line and the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log transformation (y = log<sub>1.05</sub>(x)).</p> "> Figure 7
<p>The mean and standard deviation (Std) of <span class="html-italic">C</span><sub>SPM</sub> derived from Sentinel 2 MSI B4–B8b and MODIS Terra B1 on 15 August 2016.</p> "> Figure 8
<p>Suspended particulate matter concentrations (<span class="html-italic">C</span><sub>SPM</sub>) retrieved from Sentinel 2 MSI B4 (<b>a</b>), B7 (<b>b</b>), B8b (<b>c</b>), and MODIS Aqua B1 (<b>d</b>) on 2 April 2017. The areas in the red rectangle are zoomed in to show the detailed <span class="html-italic">C</span><sub>SPM</sub> variations.</p> "> Figure 9
<p>Scatter plots of <span class="html-italic">C</span><sub>SPM</sub> values derived from Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>) against those from MODIS Aqua on 2 April 2017. The solid line is the regression line and the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log transformation (y = log1.05(x)).</p> "> Figure 10
<p>The mean and standard deviation (Std) of <span class="html-italic">C</span><sub>SPM</sub> derived from Sentinel 2 MSI B4–B8b and MODIS Aqua B1 sensed on 2 April 2017.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. In-Situ Data
2.3. Satellite Images and Pre-Processing
2.3.1. Satellite Images
2.3.2. Sentinel 2 MSI Atmospheric Correction
2.4. In-Situ Water-Leaving Reflectance Calculation
2.5. Sentinel 2 MSI Spectral Simulation
2.6. Model Development
2.7. CSPM Estimation and Comparison
3. Results
3.1. In-Situ Data
3.2. Model Development
3.3. CSPM Estimation and Comparison
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Gordon, H.R.; Morel, A.Y. Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. Lect. Notes Coast. Estuar. Stud. 1983, 4, 375–387. [Google Scholar]
- Duan, H.; Ma, R.; Zhang, Y.; Zhang, B. Remote-sensing assessment of regional inland lake water clarity in northeast China. Limnology 2009, 10, 135–141. [Google Scholar] [CrossRef]
- Wu, G.; Cui, L.; Liu, L.; Chen, F.; Fei, T.; Liu, Y. Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China. Int. J. Remote Sens. 2015, 36, 343–360. [Google Scholar] [CrossRef]
- Fischer, H.B.; List, J.E.; Koh, C.R.; Imberger, J.; Brooks, N.H. Mixing in Inland and Coastal Waters; Academic Press: San Diego, CA, USA, 1979. [Google Scholar]
- Mouw, C.B.; Greb, S.; Aurin, D.; DiGiacomo, P.M.; Lee, Z.; Twardowski, M.; Binding, C.; Hu, C.; Ma, R.; Moore, T. Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions. Remote Sens. Environ. 2015, 160, 15–30. [Google Scholar] [CrossRef]
- Qin, B.; Hu, W.; Gao, G.; Luo, L.; Zhang, J. Dynamics of sediment resuspension and the conceptual schema of nutrient release in the large shallow Lake Taihu, China. Chin. Sci. Bull. 2004, 49, 54–64. [Google Scholar] [CrossRef]
- Tabata, M.; Ghaffar, A.; Nishimoto, J. Accumulation of metals in sediments of Ariake Bay, Japan. Electron. J. Environ. Agric. Food Chem. 2009, 8, 937–949. [Google Scholar]
- Liu, H.; Wu, G.; Shi, T.; Hu, Z.; Zhou, Q. Estimating orthophosphate phosphorus concentration in Shenzhen Bay with remote sensing and legacy in-situ measurements. In Proceedings of the Earth Observation and Remote Sensing Applications, Guangzhou, China, 4–6 July 2016. [Google Scholar]
- Cui, L.; Wu, G.; Liu, Y. Monitoring the impact of backflow and dredging on water clarity using MODIS images of Poyang Lake, China. Hydrol. Process. 2009, 23, 342–350. [Google Scholar] [CrossRef]
- Herbeck, L.S.; Unger, D.; Krumme, U.; Liu, S.M.; Jennerjahn, T.C. Typhoon-induced precipitation impact on nutrient and suspended matter dynamics of a tropical estuary affected by human activities in Hainan, China. Estuar. Coast. Shelf Sci. 2011, 93, 375–388. [Google Scholar] [CrossRef]
- Feng, L.; Hu, C.; Chen, X.; Tian, L.; Chen, L. Human induced turbidity changes in Poyang Lake between 2000 and 2010: Observations from MODIS. J. Geophys. Res. Oceans 2012, 117, C07006. [Google Scholar] [CrossRef]
- Wu, G.; Cui, L.; He, J.; Duan, H.; Fei, T.; Liu, Y. Comparison of MODIS-based models for retrieving suspended particulate matter concentrations in Poyang Lake, China. Int. J. Appl. Earth Obs. Geoinf. 2013, 24, 63–72. [Google Scholar] [CrossRef]
- Olmanson, L.G.; Brezonik, P.L.; Bauer, M.E. Evaluation of medium to low resolution satellite imagery for regional lake water quality assessments. Water Resour. Res. 2011, 47, 1900–1904. [Google Scholar] [CrossRef]
- Li, J.; Chen, X.; Tian, L.; Huang, J.; Feng, L. Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations. ISPRS J. Photogramm. Remote Sens. 2015, 106, 145–156. [Google Scholar] [CrossRef]
- Gernez, P.; Lafon, V.; Lerouxel, A.; Curti, C.; Lubac, B.; Cerisier, S.; Barillé, L. Toward Sentinel-2 high resolution remote sensing of suspended particulate matter in very turbid waters: SPOT4 (Take5) Experiment in the Loire and Gironde Estuaries. Remote Sens. 2015, 7, 9507–9528. [Google Scholar] [CrossRef]
- Dekker, A.; Vos, R.; Peters, S. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. Sci. Total Environ. 2001, 268, 197–214. [Google Scholar] [CrossRef]
- Pahlevan, N.; Schott, J.R. Leveraging EO-1 to evaluate capability of new generation of Landsat sensors for coastal/inland water studies. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 360–374. [Google Scholar] [CrossRef]
- Carpenter, D.; Carpenter, S. Modeling inland water quality using Landsat data. Remote Sens. Environ. 1983, 13, 345–352. [Google Scholar] [CrossRef]
- Malenovský, Z.; Rott, H.; Cihlar, J.; Schaepman, M.E.; García-Santos, G.; Fernandes, R.; Berger, M. Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sens. Environ. 2012, 120, 91–101. [Google Scholar] [CrossRef]
- Hansen, C.H.; Burian, S.J.; Dennison, P.E.; Williams, G.P. Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models. Remote Sens. 2017, 9, 409. [Google Scholar] [CrossRef]
- Manzo, C.; Bresciani, M.; Giardino, C.; Braga, F.; Bassani, C. Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3. Eur. J. Remote Sens. 2015, 48, 17–32. [Google Scholar] [CrossRef] [Green Version]
- Dörnhöfer, K.; Göritz, A.; Gege, P.; Pflug, B.; Oppelt, N. Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake. Remote Sens. 2016, 8, 941. [Google Scholar] [CrossRef]
- Kutser, T.; Paavel, B.; Verpoorter, C.; Ligi, M.; Soomets, T.; Toming, K.; Casal, G. Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters. Remote Sens. 2016, 8, 497. [Google Scholar] [CrossRef]
- Toming, K.; Kutser, T.; Laas, A.; Sepp, M.; Paavel, B.; Nõges, T. First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery. Remote Sens. 2016, 8, 640. [Google Scholar] [CrossRef]
- Vanhellemont, Q.; Ruddick, K. Landsat-8 as a precursor to Sentinel-2: Observations of human impacts in coastal waters. Procedings of the 2014 European Space Agency Sentinel-2 for Science Workshop, Frascati, Italy, 20–22 May 2014. [Google Scholar]
- Palmer, S.C.; Kutser, T.; Hunter, P.D. Remote sensing of inland waters: Challenges, progress and future directions. Remote Sens.Environ. 2015, 157, 1–8. [Google Scholar] [CrossRef]
- Salama, M.S.; Verhoef, W. Two-stream remote sensing model for water quality mapping: 2SeaColor. Remote Sens Environ. 2015, 157, 111–122. [Google Scholar] [CrossRef]
- Vanhellemont, Q.; Ruddick, K. Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8. Remote Sens. Environ. 2015, 161, 89–106. [Google Scholar] [CrossRef]
- Wang, Y.; Jia, Y.; Guan, L.; Lu, C.; Lei, G.; Wen, L.; Liu, G. Optimising hydrological conditions to sustain wintering waterbird populations in Poyang Lake National Natural Reserve: Implications for dam operations. Freshw. Biol. 2013, 58, 2366–2379. [Google Scholar] [CrossRef]
- Zhao, X.; Barlow, J.; Taylor, B.L.; Pitman, R.L.; Wang, K.; Wei, Z.; Stewart, B.S.; Turvey, S.T.; Akamatsu, T.; Reeves, R.R. Abundance and conservation status of the Yangtze finless porpoise in the Yangtze River, China. Biol. Conserv. 2008, 141, 3006–3018. [Google Scholar] [CrossRef]
- Yu, Z.; Chen, X.; Zhou, B.; Tian, L.; Yuan, X.; Feng, L. Assessment of total suspended sediment concentrations in Poyang Lake using HJ-1A/1B CCD imagery. Chin. J. Oceanol. Limnol. 2012, 30, 295–304. [Google Scholar] [CrossRef]
- Wu, G.; De Leeuw, J.; Skidmore, A.K.; Prins, H.H.; Liu, Y. Comparison of MODIS and Landsat TM5 images for mapping tempo–spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve, China. Int. J. Remote Sens. 2008, 29, 2183–2198. [Google Scholar] [CrossRef]
- De Leeuw, J.; Shankman, D.; Wu, G.; de Boer, W.F.; Burnham, J.; He, Q.; Yesou, H.; Xiao, J. Strategic assessment of the magnitude and impacts of sand mining in Poyang Lake, China. Reg. Environ. Chang. 2010, 10, 95–102. [Google Scholar] [CrossRef]
- Hou, X.; Feng, L.; Duan, H.; Chen, X.; Sun, D.; Shi, K. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sens. Environ. 2017, 190, 107–121. [Google Scholar] [CrossRef]
- Wu, G.; Liu, L.; Chen, F.; Fei, T. Developing MODIS-based retrieval models of suspended particulate matter concentration in Dongting Lake, China. Int. J. Appl. Earth Observ. Geoinfor. 2014, 32, 46–53. [Google Scholar] [CrossRef]
- Li, J.; Tian, L.; Chen, X.; Li, X.; Huang, J.; Lu, J.; Feng, L. Remote-sensing monitoring for spatio-temporal dynamics of sand dredging activities at Poyang Lake in China. Int. J. Remote Sens. 2014, 35, 6004–6022. [Google Scholar] [CrossRef]
- Cui, L.; Qiu, Y.; Fei, T.; Liu, Y.; Wu, G. Using remotely sensed suspended sediment concentration variation to improve management of Poyang Lake, China. Lake Reserv. Manag. 2013, 29, 47–60. [Google Scholar] [CrossRef]
- Mueller, J.L.; Morel, A.; Frouin, R.; Davis, C.; Arnone, R.; Carder, K.; Lee, Z.P.; Steward, R.G.; Hooker, S.; Mobley, C.D. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Radiometric Measurements and Data Analysis Protocols. Tech. Memo 2003-21621; Goddard Space Flight Center: Greenbelt, MD, USA, 2003. [Google Scholar]
- Ma, R.-H.; Tang, J.-W.; Dai, J.-F. Bio-optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing. Int. J. Remote Sens. 2006, 27, 4305–4328. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Gordon, H.R.; Wang, M. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm. Appl. Opt. 1994, 33, 443–452. [Google Scholar] [CrossRef] [PubMed]
- Vermote, E.F.; Tanré, D.; Deuze, J.L.; Herman, M.; Morcette, J.-J. Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans. Geosci. Remote Sens. 1997, 35, 675–686. [Google Scholar] [CrossRef]
- Wang, M.; Shi, W. Cloud masking for ocean color data processing in the coastal regions. IEEE Trans. Geosci. Remote Sens. 2006, 44, 3196–3205. [Google Scholar] [CrossRef]
- Vanhellemont, Q.; Ruddick, K. ACOLITE For Sentinel-2: Aquatic Applications of MSI Imagery. ESA Special Publication. Presented at the ESA Living Planet Symposium, Prague, Czech Republic, 9–13 May 2016. [Google Scholar]
- Doxaran, D.; Froidefond, J.-M.; Lavender, S.; Castaing, P. Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations. Remote Sens. Environ. 2002, 81, 149–161. [Google Scholar] [CrossRef]
- Petus, C.; Chust, G.; Gohin, F.; Doxaran, D.; Froidefond, J.-M.; Sagarminaga, Y. Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery. Cont. Shelf Res. 2010, 30, 379–392. [Google Scholar] [CrossRef]
- Wang, M.; Son, S.; Zhang, Y.; Shi, W. Remote Sensing of Water Optical Property for China’s Inland Lake Taihu Using the SWIR Atmospheric Correction With 1640 and 2130 nm Bands. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2013, 6, 2505–2516. [Google Scholar] [CrossRef]
- Gordon, H.R.; Brown, O.B.; Jacobs, M.M. Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Appl. Opt. 1975, 14, 417–427. [Google Scholar] [CrossRef] [PubMed]
- Wu, G.; Cui, L.; Duan, H.; Fei, T.; Liu, Y. Absorption and backscattering coefficients and their relations to water constituents of Poyang Lake, China. Appl. Opt. 2011, 50, 6358–6368. [Google Scholar] [CrossRef] [PubMed]
- Lee, Z.; Carder, K.L.; Arnone, R.A. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters. Appl. Opt. 2002, 41, 5755–5772. [Google Scholar] [CrossRef] [PubMed]
- Kutser, T.; Vahtmäe, E.; Paavel, B.; Kauer, T. Removing glint effects from field radiometry data measured in optically complex coastal and inland waters. Remote Sens. Environ. 2013, 133, 85–89. [Google Scholar] [CrossRef]
- Tian, L.; Wai, O.W.H.; Chen, X.; Li, W.; Li, J.; Li, W.; Zhang, H. Retrieval of total suspended matter concentration from Gaofen-1 Wide Field Imager (WFI) multispectral imagery with the assistance of Terra MODIS in turbid water—Case in Deep Bay. Int. J. Remote Sens. 2016, 37, 3400–3413. [Google Scholar] [CrossRef]
- Novoa, S.; Doxaran, D.; Ody, A.; Vanhellemont, Q.; Lafon, V.; Lubac, B.; Gernez, P. Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters. Remote Sens. 2017, 9, 61. [Google Scholar] [CrossRef]
- Yang, X.; Zhao, S.; Qin, X.; Zhao, N.; Liang, L. Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens. 2017, 9, 596. [Google Scholar] [CrossRef]
- Deng, R.; Yingqing, H.E.; Qin, Y.; Chen, Q. Pure water absorption coefficient measurement after eliminating the impact of suspended substance in spectrum from 400 nm to 900 nm. J. Remote Sens. 2012, 16, 174–191. [Google Scholar]
- Wu, G.; Cui, L.; Duan, H.; Fei, T.; Liu, Y. An approach for developing Landsat-5 TM-based retrieval models of suspended particulate matter concentration with the assistance of MODIS. ISPRS J. Photogramm. Remote Sens. 2013, 85, 84–92. [Google Scholar] [CrossRef]
- Wang, M.; Shi, W.; Tang, J. Water property monitoring and assessment for China’s inland Lake Taihu from MODIS-Aqua measurements. Remote Sens. Environ. 2011, 115, 841–854. [Google Scholar] [CrossRef]
- Wang, M.; Son, S.; Shi, W. Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data. Remote Sens. Environ. 2009, 113, 635–644. [Google Scholar] [CrossRef]
- Franz, B.A.; Bailey, S.W.; Kuring, N.; Werdell, P.J. Ocean color measurements with the Operational Land Imager on Landsat-8: Implementation and evaluation in SeaDAS. J. Appl. Remote Sens. 2015, 9, 096070. [Google Scholar] [CrossRef]
- Pahlevan, N.; Schott, J.R.; Franz, B.A.; Zibordi, G.; Markham, B.; Bailey, S.; Schaaf, C.B.; Ondrusek, M.; Greb, S.; Strait, C.M. Landsat 8 remote sensing reflectance (Rrs) products: Evaluations, intercomparisons, and enhancements. Remote Sens. Environ. 2017, 190, 289–301. [Google Scholar] [CrossRef]
Dataset | Number | Minimum | Maximum | Average | Std. Dev. | CV |
---|---|---|---|---|---|---|
Calibration | 34 | 19.00 | 294.50 | 79.45 | 63.22 | 79.57 |
Validation | 34 | 17.16 | 282.25 | 76.05 | 58.67 | 77.15 |
All | 68 | 17.16 | 294.50 | 77.75 | 60.56 | 77.89 |
Model | R2 | MAPE | RMSE | F |
---|---|---|---|---|
CSPM = 2.335 × exp(47.62 × B1) | 0.57 | 40.89 | 41.02 | 20.24 |
CSPM = 1.769 × exp(37.38 × B2) | 0.56 | 41.36 | 41.87 | 18.81 |
CSPM = 1.808 × exp(25.08 × B3) | 0.53 | 38.34 | 42.83 | 17.29 |
CSPM = 4.044 × exp(19.53 × B4) | 0.81 | 30.32 | 27.95 | 61.47 |
CSPM = 8.385 × exp(16.49 × B5) | 0.88 | 20.99 | 21.51 | 114.47 |
CSPM = 3329 × B61.375 | 0.91 | 16.61 | 18.20 | 151.14 |
CSPM = 2950 × B71.357 | 0.93 | 16.58 | 16.50 | 205.49 |
CSPM = 2887 × B81.223 | 0.91 | 16.23 | 18.14 | 167.15 |
CSPM = 2520 × B8b1.42 | 0.90 | 18.30 | 19.59 | 141.14 |
© 2017 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/).
Share and Cite
Liu, H.; Li, Q.; Shi, T.; Hu, S.; Wu, G.; Zhou, Q. Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake. Remote Sens. 2017, 9, 761. https://doi.org/10.3390/rs9070761
Liu H, Li Q, Shi T, Hu S, Wu G, Zhou Q. Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake. Remote Sensing. 2017; 9(7):761. https://doi.org/10.3390/rs9070761
Chicago/Turabian StyleLiu, Huizeng, Qingquan Li, Tiezhu Shi, Shuibo Hu, Guofeng Wu, and Qiming Zhou. 2017. "Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake" Remote Sensing 9, no. 7: 761. https://doi.org/10.3390/rs9070761