Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Remote Sensing Images and Images Pre-Processing
2.2.1. Radiometric Calibration
2.2.2. Atmospheric Correction
2.3. Field Measurements
2.3.1. Radiometric Measurements
2.3.2. Water Sample Analysis
2.4. Model Calibration Based on Simulated OHS Imagery
2.5. Signal-to-Noise Ratio Estimation
2.6. Statistical Analysis
3. Results and Discussion
3.1. Data Descriptive Statistics
3.2. Model Development and Validation Based on Simulated-OHS Imagery
3.2.1. Blue-Green Band Ratio Model
3.2.2. Near-Infrared and Red (NIR-Red) Band Ratio Model
3.2.3. Three-Band Model
3.2.4. Four-Band Model
3.2.5. Fluorescence Line Height (FLH) Model
3.2.6. Model Validation Based on Simulated-OHS Imagery
3.3. Validation and Spatial Patterns of Chl-a from OHS Imagery
3.3.1. Validation of Derived Chl-a for OHS Imagery
3.3.2. Spatial Patterns of Chl-a in Dianchi Lake
3.4. Uncertainty Analysis of OHS Imagery
3.4.1. Signal-to-Noise Ratio of OHS Imagery
3.4.2. Limitation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Bands | Wavelength (nm) | FWHM (nm) | Radiance Gains (W·m−2·sr−1·μm−1) | Radiance Offsets (W·m−2·sr−1·μm−1) | TDIStage |
---|---|---|---|---|---|
B1 | 466 | 5.0 | 0.31711 | 0.00000 | 6 |
B2 | 480 | 5.0 | 0.33824 | 0.00000 | 6 |
B3 | 500 | 5.0 | 0.42547 | 0.00000 | 6 |
B4 | 520 | 6.0 | 0.45222 | 0.00000 | 6 |
B5 | 536 | 6.0 | 0.45314 | 0.00000 | 6 |
B6 | 550 | 7.0 | 0.47178 | 0.00000 | 6 |
B7 | 566 | 7.0 | 0.43948 | 0.00000 | 6 |
B8 | 580 | 8.0 | 0.42103 | 0.00000 | 6 |
B9 | 596 | 8.0 | 0.46463 | 0.00000 | 5 |
B10 | 610 | 7.0 | 0.41791 | 0.00000 | 5 |
B11 | 626 | 8.0 | 0.37667 | 0.00000 | 5 |
B12 | 640 | 8.0 | 0.36352 | 0.00000 | 5 |
B13 | 656 | 8.0 | 0.39356 | 0.00000 | 4 |
B14 | 670 | 9.0 | 0.38094 | 0.00000 | 4 |
B15 | 686 | 10.0 | 0.31421 | 0.00000 | 4 |
B16 | 700 | 10.0 | 0.42416 | 0.00000 | 3 |
B17 | 716 | 10.0 | 0.36147 | 0.00000 | 3 |
B18 | 730 | 10.0 | 0.38121 | 0.00000 | 3 |
B19 | 746 | 10.0 | 0.35642 | 0.00000 | 3 |
B20 | 760 | 10.0 | 0.28216 | 0.00000 | 3 |
B21 | 776 | 9.0 | 0.36163 | 0.00000 | 3 |
B22 | 790 | 12.0 | 0.34793 | 0.00000 | 3 |
B23 | 806 | 11.0 | 0.36328 | 0.00000 | 3 |
B24 | 820 | 12.0 | 0.35926 | 0.00000 | 3 |
B25 | 836 | 9.0 | 0.38408 | 0.00000 | 3 |
B26 | 850 | 11.0 | 0.38707 | 0.00000 | 3 |
B27 | 866 | 11.0 | 0.38515 | 0.00000 | 3 |
B28 | 880 | 12.0 | 0.32043 | 0.00000 | 4 |
B29 | 896 | 11.0 | 0.28990 | 0.00000 | 5 |
B30 | 910 | 11.0 | 0.30713 | 0.00000 | 5 |
B31 | 926 | 14.0 | 0.34246 | 0.00000 | 6 |
B32 | 940 | 13.0 | 0.22125 | 0.00000 | 8 |
Model | Model Form | Study Region | Image Applied | RMSE (μg/L) | MAPE |
---|---|---|---|---|---|
Gurlin et al. [19] | Rrs(748)/Rrs(667) | Fremont Lakes | MODIS | 6.10 | 27.6% |
Rrs(709)/Rrs(665) | Fremont Lakes | MERIS | 3.60 | 11.6% | |
(Rrs−1(665)-Rrs−1(709)) Rrs(754) | Fremont Lakes | MERIS | 3.30 | 18.0% | |
Bi et al. [47] | FLH | Erhai Lake | OLCI | 1.92 | 13.5% |
(Rrs−1(665)-Rrs−1(709)) Rrs(754) | Erhai Lake | OLCI | 1.61 | 12.4% | |
Yang et al. [48] | (Rrs−1(665)-Rrs−1(709)) (Rrs−1(754)-Rrs−1(709)) | Kasumigaura Lake | MERIS | 8.68 | 12.3% |
Guo et al. [49] | (Rrs−1(680)-Rrs−1(660)) Rrs(745) | Taihu Lake | GOCI | 16.31 | 32.5% |
(Rrs−1(681)-Rrs−1(709)) Rrs(754) | Taihu Lake | GOCI | 15.17 | 26.5% | |
Härmä et al. [50] | (Rrs(531)-Rrs(748)) (Rrs(551)-Rrs(748)) | Finland Lake | MODIS | 11.60 | 77.0% |
Du et al. [51] | (Rrs−1(691)-Rrs−1(722)) Rrs(854) | Taihu Lake | Hyperion | 13.93 | 23.7% |
Lyu et al. [52] | SCI | Taihu Lake | MERIS | 4.89 | 38.1% |
(Rrs−1(665)-Rrs−1(709)) Rrs(779) | Taihu Lake | MERIS | 15.67 | 24.5% | |
(Rrs−1(665)-Rrs−1(709)) (Rrs−1(865)-Rrs−1(709)) | Taihu Lake | MERIS | 7.88 | 31.3% | |
This study | (Rrs−1(686)-Rrs−1(716)) Rrs(746) | Dianchi Lake | OHS | 15.55 | 16.31% |
Model | Variable (x) | Model Form | R2 | RMSE (μg/L) | MAPE |
---|---|---|---|---|---|
Gurlin et al. [19] | B19/B14 | Chl-a = 110.03x − 0.4494 | 0.691 | 21.10 | 22.41% |
B16/B14 | Chl-a = 54.529x − 7.5799 | 0.452 | 20.79 | 23.01% | |
(1/B14-1/B16)B20 | Chla = 142.93x + 40.651 | 0.712 | 21.64 | 23.35% | |
Bi et al. [47] | FLH | Chl-a = −11510x + 75.573 | 0.636 | 22.40 | 21.59% |
(1/B14-1/B16)B20 | Chl-a = 142.93x + 40.651 | 0.712 | 20.68 | 23.29% | |
Yang et al. [48] | (1/B14-1/B16)(1/B20-1/B16) | Chl-a = 8*10−6x + 89.593 | 0.000 | 24.43 | 28.15% |
Guo et al. [49] | (1/B15-1/B13)B19 | Chl-a = 273.31x + 52.424 | 0.515 | 21.32 | 21.69% |
(1/B15-1/B16)B20 | Chl-a = 178.45x + 42.631 | 0.738 | 21.23 | 21.87% | |
Härmä et al. [50] | (B5-B19)(B6-B19) | Chl-a = −17937x + 94.359 | 0.017 | 24.62 | 28.46% |
Du et al. [51] | (1/B15-1/B17)B26 | Chl-a = 190.48x + 65.261 | 0.602 | 20.26 | 20.16% |
Lyu et al. [52] | SCI | Chl-a = 4841.8x + 57.761 | 0.120 | 21.64 | 24.71% |
(1/B14-1/B16)B21 | Chl-a = 143.67x + 39.666 | 0.718 | 20.55 | 22.81% | |
(1/B14-1/B16)(1/B27-1/B16) | Chl-a = −0.0002x + 90.107 | 0.003 | 24.45 | 28.00% | |
This study | (1/B15-1/B17)B19 | Chl-a = 137.35x + 59.741 | 0.809 | 15.55 | 16.31% |
References
- Ma, R.H.; Tang, J.W.; Duan, H.T.; Pan, D.L. Progress in lake water color remote sensing. J. Lake Sci. 2009, 21, 143–158. (In Chinese) [Google Scholar]
- Beck, R.; Zhan, S.G.; Liu, H.X.; Tong, S.S.; Yang, B.; Xu, M.; Ye, Z.X.; Huang, Y.; Shu, S.; Wu, Q.S.; et al. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sens. Environ. 2016, 178, 15–30. [Google Scholar] [CrossRef] [Green Version]
- Tilstone, G.; Mallor-Hoya, S.; Gohin, F.; Couto, A.B.; Sá, C.; Goela, P.; Cristina, S.; Airs, R.; Icely, J.; Zühlke, M. Which ocean colour algorithm for MERIS in North West European waters? Remote Sens. Environ. 2017, 189, 132–151. [Google Scholar] [CrossRef] [Green Version]
- Falkowski, P.G.; Scholes, R.J.; Boyle, E.; Canadell, J.; Canfield, D.; Elser, J. The global carbon cycle: A test of our knowledge of Earth as a system. Science 2000, 290, 291–296. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Honeywill, C.; Paterson, D.M.; Hegerthey, S.E. Determination of microphy-tobenthic biomass using pulse-amplitude modulated minimum fluorescence. Eur. J. Phycol. 2002, 37, 485–492. [Google Scholar] [CrossRef]
- Birk, S.; Bonne, W.; Borja, A.; Brucet, S.; Courrat, A.; Poikane, S.; Solimini, A.; van de Bund, W.; Zampoukas, N.; Hering, D. Three hundred ways to assess Europe’s surface waters: An almost complete overview of biological methods to implement the Water Framework Directive. Ecol. Indic. 2012, 18, 31–41. [Google Scholar] [CrossRef]
- Chang, N.B.; Imen, S.; Vannah, B. Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: A 40-year perspective. Crit. Rev. Environ. Sci. Technol. 2015, 45, 101–166. [Google Scholar] [CrossRef]
- Simisa, S.G.H.; Ruiz-Verdúb, A.; Domínguez-Gómezb, J.A.; Peña-Martinezb, R.; Petersc, S.W.M.; Gonsa, H.J. Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass. Remote Sens. Environ. 2007, 106, 414–427. [Google Scholar] [CrossRef]
- Zhang, C.Y.; Hu, C.M.; Shang, S.L. Bridging between SeaWiFS and MODIS for continuity of chlorophyll-a concentration assessments off Southeastern China. Remote Sens. Environ. 2006, 102, 250–263. [Google Scholar] [CrossRef]
- Kudela, R.M.; Palacios, S.L.; Austerberry, D.C.; Accorsi, E.K.; Guild, L.S.; Torres-Perez, J. Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sens. Environ. 2015, 167, 196–205. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.M.; Wang, Q.; Wu, Q.C.; Zhao, S.H.; Xu, X.; Wang, Y.F.; Hang, C.C. Estimation of Chlorophyll a Concentration Using NIR/Red Bands of MERIS and Classification Procedure in Inland Turbid Water. Trans. Geosci. Remote Sens. 2011, 50, 988–997. [Google Scholar] [CrossRef]
- Zeng, Q.; Zhang, H.D.; Chen, X.L.; Tian, L.Q.; Li, W.K.; Wang, G.L. Evaluation on the atmospheric correction methods for water color remote sensing by using MERIS image: A case study on chlorophyll-a concentration of Lake Poyang. J. Lake Sci. 2016, 28, 1306–1315. (In Chinese) [Google Scholar] [CrossRef] [Green Version]
- Duan, H.T.; Zhang, Y.Z.; Zhang, B.; Song, S.K.; Wang, Z.M. Assessment of chlorophyll-a concentration and trophic state for Lake Chagan using Landsat TM and field spectral data. Environ. Monit. Assess. 2007, 129, 295–308. [Google Scholar] [CrossRef] [PubMed]
- Fang, X.; Duan, H.T.; Cao, Z.G.; Shen, M.; Ge, X.S. Remote monitoring of cyanobacterial blooms using multi-source satellite data: A case of Yuqiao Reservoir, Tianjin. J. Lake Sci. 2018, 30, 967–978. (In Chinese) [Google Scholar] [CrossRef]
- Wen, J.G.; Xiao, Q.; Yang, Y.P.; Liu, Q.H.; Zhou, Y. Remote sensing estimation of aquatic chlorophyll-a concentration based on Hyperion data in Lake Taihu. J. Lake Sci. 2006, 18, 327–336. (In Chinese) [Google Scholar]
- Wang, S.S.; Li, Y.M.; Wang, Y.B.; Wang, S.; Du, C.G. Suitability of the retrieval models for estimating chlorophyll concentration in Lake Taihu. J. Lake Sci. 2015, 27, 150–162. (In Chinese) [Google Scholar]
- Huang, C.C.; Wang, X.L.; Yang, H.; Li, Y.M.; Wang, Y.H.; Chen, X.; Xu, L.J. Satellite data regarding the eutrophication response to human activities in the plateau lake Dianchi in China from 1974 to 2009. Sci. Total Environ. 2014, 485–486, 1–11. [Google Scholar] [CrossRef]
- O’Reilly, J.E.; Maritorena, S.; Siegel, D.A.; O’Brien, M.C.; Toole, D.; Mitchell, B.G.; Kahru, M.; Chavez, F.P.; Strutton, P.; Cota, C.F.; et al. SeaWiFS Postlaunch Calibration and Validation Analyses; Part 3; NASA Tech; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 2000; p. 49.
- Gurlin, D.; Gitelson, A.A.; Moses, W.J. Remote estimation of chl-a concentration in turbid productive waters—return to a simple two-band NIR-red model? Remote Sens. Environ. 2011, 115, 3479–3490. [Google Scholar] [CrossRef]
- Matthews, M.W.; Bernard, S.; Robertson, L. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sens. Environ. 2012, 124, 637–652. [Google Scholar] [CrossRef]
- Liu, G.; Li, L.; Song, K.S.; Li, Y.M.; Lyu, H.; Wen, Z.D.; Fang, C.; Bi, S.; Sun, X.P.; Wang, Z.M. An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters. Remote Sens. Environ. 2020, 239, 1–26. [Google Scholar] [CrossRef]
- Ioannou, I.; Gilerson, A.; Gross, B.; Moshary, F.; Ahmed, S. Deriving ocean color products using neural networks. Remote Sens. Environ. 2013, 134, 78–91. [Google Scholar] [CrossRef]
- Italian Space Agency. The Satellite of the Italian Space Agency Launched from the European Base of Korou Aboard a Vega Launcher. Available online: https://www.asi.it (accessed on 7 November 2020).
- Alonso, K.; Bachmann, M.; Burch, K.; Carmona, E.; Cerra, D.; Reyes, R.D.L.; Dietrich, D.; Heiden, U.; Hölderlin, A.; Ickes, J.; et al. Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors 2019, 19, 4471. [Google Scholar] [CrossRef] [Green Version]
- Zhuhai Orbita Aerospace Technology Co., Ltd. The Registration System of Orbita Hyperspectral Competition. Basic Information of Zhuhai No. 1 OHS Hyperspectral Satellite. Available online: https://ohs.obtdata.com/#/newsDetail?id=9 (accessed on 30 May 2019).
- Deng, S.B.; Chen, Q.J.; Du, H.J.; Xu, E.H. ENVI Remote Sensing Image Processing Method, 2nd ed.; Higher Education Press: Beijing, China, 2014; pp. 277–324. (In Chinese) [Google Scholar]
- Liu, D.; Pan, D.; Bai, Y.; He, X.; Wang, D.; Wei, J.A.; Zhang, L. Remote sensing observation of particulate organic carbon in the Pearl River estuary. Remote Sens. 2015, 7, 8683–8704. [Google Scholar] [CrossRef] [Green Version]
- Mueller, J.L.; Morel, A.; Frouin, R.; Davis, C.; Arnone, R.; Carder, K. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation. Radiometric Measurements and Data Analysis Protocols, 3rd ed.; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 2003; pp. 1–63.
- Morel, A.; Mueller, J.L. Normalized Water-Leaving Radiance and Remote Sensing Reflectance: Bidirectional Reflectance and Other Factors. Available online: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20020044099.pdf#page=54 (accessed on 18 June 2020).
- Mobley, C.D. Estimation of the remote-sensing reflectance from above-surface measurements. Appl. Opt. 1999, 38, 7442–7455. [Google Scholar] [CrossRef]
- Tang, J.W.; Tian, G.L.; Wang, X.Y.; Wang, X.M.; Song, Q.J. The methods of water spectra measurement and analysis I: Above-water method. J. Remote Sens. Beijing 2004, 8, 37–44. (In Chinese) [Google Scholar]
- Shi, K.; Zhang, Y.; Zhou, Y.; Liu, X.; Zhu, G.; Qin, B.; Gao, G. Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors. Sci. Rep. 2017, 7, 40326. [Google Scholar] [CrossRef] [Green Version]
- American Public Health Association; American Water Works Association; Water Environment Federation. Standard Methods for the Examination of Water and Wastewater; American Public Health Association: Washington, DC, USA, 1998; pp. 1–40. [Google Scholar]
- Huang, X. Eco-Investigation. Observation and Analysis of Lakes; Standard Press of China: Beijing, China, 1999. (In Chinese) [Google Scholar]
- Zheng, Z.B.; Li, Y.M.; Guo, Y.L.; Xu, Y.F.; Liu, G.; Du, C.G. Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China. Remote Sens. 2015, 7, 13975–13999. [Google Scholar] [CrossRef] [Green Version]
- Sun, D.Y.; Li, Y.M.; Wang, Q.; Lyu, H.; Le, C.F.; Huang, C.C.; Gong, S.Q. Detection of suspended-matter concentrations in the shallow subtropical Lake Taihu, China, using the SVR model based on DSFS. IEEE Geosci. Remote Sens. 2010, 7, 816–820. [Google Scholar] [CrossRef]
- Zhu, B.; Wang, X.H.; Tang, L.L.; Li, C.R. Review on Methods for SNR Estimation of Optical Remote Sensing Imagery. Remote Sens. Technol. Appl. 2010, 25, 303–309. (In Chinese) [Google Scholar]
- Wang, X.W.; Wang, C.X.; Zhang, Y.Y.; Li, C.R. Research on SNR of Point Target Image. Electron. Opt. Control. 2010, 17, 18–21. (In Chinese) [Google Scholar]
- Neil, C.; Spyrakos, E.; Hunter, P.D.; Tyler, A.N. A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sens. Environ. 2019, 229, 159–178. [Google Scholar] [CrossRef]
- Liu, G.; Simis, S.G.H.; Li, L.; Wang, Q.; Li, Y.M.; Song, K.S.; Lyu, H.; Zheng, Z.B.; Shi, K. A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters from Simulated MERIS and OLCI Data. IEEE Geosci. Remote Sens. 2017, 56, 1374–1385. [Google Scholar] [CrossRef]
- Sun, D.Y.; Hu, C.M.; Qiu, Z.F.; Shi, K. Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: A case study in Lake Dianchi. Opt. Express 2015, 23, 3055–3074. [Google Scholar] [CrossRef] [Green Version]
- Ruddick, K.G.; Gons, H.J.; Rijkeboer, M.; Tilstone, G. Optical remote sensing of chlorophyll-a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties. Appl. Opt. 2001, 40, 3575–3585. [Google Scholar] [CrossRef] [PubMed]
- Dall’Olmo, G.; Gitelson, A.A. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: Experimental results. Appl. Opt. 2005, 44, 412–422. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Le, C.F.; Li, Y.M.; Zha, Y.; Sun, D.Y.; Huang, C.C.; Lu, H. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China. Remote Sens. Environ. 2009, 113, 1175–1182. [Google Scholar] [CrossRef]
- Neville, R.A.; Gower, J.F.R. Passive remote sensing of phytoplankton via chlorophyll a fluorescence. J. Geophys. Res. 1977, 82, 3487–3493. [Google Scholar] [CrossRef]
- Li, J.; Chen, X.L.; Tian, L.Q.; 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. J. Photogramm. Remote Sens. 2015, 106, 145–156. [Google Scholar] [CrossRef]
- Bi, S.; Li, Y.M.; Lyu, H.; Zhu, L.; Mou, M.; Lei, S.H.; Xu, J.; Wen, S.; Ding, X.L. Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data. J. Lake Sci. 2018, 30, 701–712. (In Chinese) [Google Scholar] [CrossRef]
- Yang, W.; Matsushita, B.K.; Chen, J.; Fukushima, T.; Ma, R.H. An Enhanced Three-Band Index for Estimating Chlorophyll-a in Turbid Case-II Waters: Case Studies of Lake Kasumigaura, Japan, and Lake Dianchi, China. IEEE Geosci. Remote Sens. 2010, 7, 655–659. [Google Scholar] [CrossRef] [Green Version]
- Guo, Y.L.; Li, Y.M.; Li, Y.; Lyu, H.; Liu, G.; Wang, X.D.; Zhang, S.M. A Three Band Chlorophyll-a Concentration Estimation Model Based on GOCI Imagery. J. Environ. Sci. China 2015, 36, 3175–3185. [Google Scholar] [CrossRef]
- Härmä, P.; Vepsäläinen, J.; Zha, Y.; Hannonen, T.; Pyhälahti, T.; Kämäri, J.; Kallio, K.; Eloheimo, K.; Koponen, S. Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland. Remote Sens. Environ. 2001, 268, 107–121. [Google Scholar] [CrossRef]
- Du, C.; Wang, S.X.; Zhou, Y.; Yan, F.L. Remote chlorophyll a retrieval in Taihu Lake by three-band model using Hyperion hyperspectral data. J. Environ. Sci. China 2009, 30, 2904–2910. [Google Scholar]
- Lyu, H.; Li, X.J.; Wang, Y.N.; Jin, Q.; Cao, K.; Wang, Q.; Li, Y.M. Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes. Sci. Total Environ. 2015, 530–531, 373–382. [Google Scholar] [CrossRef] [PubMed]
Total Dataset | Sampling Time (YYYY/MM/DD) | Training Dataset | Validation Dataset | Usage |
---|---|---|---|---|
N = 24 | 2009/09/19–2009/09/20 | N = 16 | N = 8 | Simulated OHS-based model calibration and validation |
N = 30 | 2017/04/13–2017/04/16 | N = 20 | N = 10 | |
N = 39 | 2017/11/13–2017/11/15 | N = 25 | N = 14 | |
N = 10 * | 2019/04/02 | N = 0 | N = 10 | Validation of derived Chl-a from OHS imagery |
Sampling Time (YYYY/MM/) | Parameters | Maximum | Minimum | Mean | S.D. | C.V. |
---|---|---|---|---|---|---|
Aggregated | Chl-a (µg/L) | 187.01 | 38.97 | 87.35 | 24.14 | 27.64% |
(N = 93) | TSM (mg/L) | 66.60 | 20.98 | 37.04 | 8.19 | 22.11% |
OSM (mg/L) | 52.10 | 11.16 | 23.54 | 8.41 | 35.73% | |
ISM (mg/L) | 28.10 | 0.00 | 13.50 | 4.84 | 35.88% | |
OSM/TSM | 1.00 | 0.41 | 0.63 | 0.12 | 19.82% | |
ISM/TSM | 0.58 | 0.00 | 0.37 | 0.12 | 33.41% | |
2009/09 | Chl-a (µg/L) | 156.69 | 38.97 | 93.90 | 32.91 | 35.05% |
(N = 24) | TSM (mg/L) | 66.60 | 24.70 | 44.40 | 9.59 | 21.59% |
OSM (mg/L) | 52.10 | 16.20 | 35.52 | 8.35 | 23.50% | |
ISM (mg/L) | 22.80 | 0.00 | 8.88 | 4.49 | 50.55% | |
OSM/TSM | 1.00 | 0.41 | 0.80 | 0.10 | 12.98% | |
ISM/TSM | 0.58 | 0.00 | 0.20 | 0.10 | 52.94% | |
2017/04 | Chl-a (µg/L) | 107.53 | 61.86 | 79.69 | 10.67 | 13.39% |
(N = 30) | TSM (mg/L) | 50.00 | 20.98 | 33.47 | 6.51 | 19.46% |
OSM (mg/L) | 22.35 | 11.16 | 17.85 | 2.63 | 14.76% | |
ISM (mg/L) | 28.10 | 8.76 | 15.62 | 4.67 | 29.88% | |
OSM/TSM | 0.69 | 0.44 | 0.54 | 0.06 | 11.03% | |
ISM/TSM | 0.56 | 0.31 | 0.46 | 0.06 | 12.93% | |
2017/11 | Chl-a (µg/L) | 187.01 | 59.67 | 90.04 | 25.51 | 28.33% |
(N = 39) | TSM (mg/L) | 54.37 | 26.89 | 36.04 | 6.07 | 16.83% |
OSM (mg/L) | 36.15 | 15.50 | 21.80 | 4.32 | 19.82% | |
ISM (mg/L) | 22.10 | 7.14 | 14.24 | 3.46 | 24.29% | |
OSM/TSM | 0.74 | 0.50 | 0.61 | 0.07 | 11.33% | |
ISM/TSM | 0.50 | 0.26 | 0.39 | 0.07 | 17.42% |
Model Name | Variable (x) | For Form | R2 | RMSE (µg/L) | MAPE |
---|---|---|---|---|---|
Blue-Green Band Ratio | B2/B7 | Chl-a_BG = −154.84x + 156.71 | 0.144 | 22.97 | 24.78% |
NIR-Red Band Ratio | B17/B14 | Chl-a_NR = 56.226x + 0.2191 | 0.729 | 17.78 | 17.16% |
Three-band | (1/B15–1/B17)B19 | Chl-a_TB = 137.35x + 59.741 | 0.809 | 15.55 | 16.31% |
Four-band | (1/B13–1/B15) (1/B19–1/B16) | Chl-a_FB = −0.0002x + 89.498 | 0.001 | 24.44 | 28.17% |
FLH | FLH | Chl-a_FLH = −11510x + 75.573 | 0.636 | 22.40 | 21.59% |
Images | Coastal Aerosol (435–451 nm) | Blue (452–512 nm) | Green (532–600 nm) | Red (635–673 nm) | NIR (845–892 nm) |
---|---|---|---|---|---|
OHS-10 m | 25.91 | 24.31 | 22.99 | 20.34 | 17.80 |
OHS-30 m | 26.03 | 24.25 | 22.88 | 20.33 | 17.77 |
OHS-100 m | 26.62 | 24.71 | 22.70 | 20.32 | 18.07 |
Landsat-8 OLI-30 m | 166.87 | 139.23 | 110.25 | 74.18 | 67.97 |
HJ-1 HSI-100 m | 4.41 | 7.71 | 11.53 | 10.07 | 5.61 |
EO-1 Hyperion-30 m | 38.63 | 51.14 | 44.97 | 36.63 | 19.54 |
Sentinel-2 MSI-10 m | —— | 82.13 | 43.66 | 58.40 | 5.75 |
Sentinel-2 MSI-20 m | —— | —— | —— | —— | 5.18 |
Sentinel-2 MSI-60 m | 127.60 | —— | —— | —— | —— |
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Zhang, R.; Zheng, Z.; Liu, G.; Du, C.; Du, C.; Lei, S.; Xu, Y.; Xu, J.; Mu, M.; Bi, S.; et al. Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes. Remote Sens. 2021, 13, 2821. https://doi.org/10.3390/rs13142821
Zhang R, Zheng Z, Liu G, Du C, Du C, Lei S, Xu Y, Xu J, Mu M, Bi S, et al. Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes. Remote Sensing. 2021; 13(14):2821. https://doi.org/10.3390/rs13142821
Chicago/Turabian StyleZhang, Runfei, Zhubin Zheng, Ge Liu, Chenggong Du, Chao Du, Shaohua Lei, Yifan Xu, Jie Xu, Meng Mu, Shun Bi, and et al. 2021. "Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes" Remote Sensing 13, no. 14: 2821. https://doi.org/10.3390/rs13142821