Sensitivity analysis of a bio-optical model for Italian lakes focused on Landsat-8, Sentinel-2 and Sentinel-3

References

• Aas E. (1987)—Two-stream irradiance model for deep waters. Applied Optics, 26: 2095–2101. doi: http://dx.doi.org/10.1364/AO.26.002095.
   PubMed Web of Science ®Google Scholar
• Aschbacher J., Milagro-Pérez M.P. (2012)—The European Earth monitoring (GMES) programme: Status and perspectives. Remote Sensing of Environment, 120: 3–8. doi: http://dx.doi.org/10.1016/j.rse.2011.08.028.
   Web of Science ®Google Scholar
• Babin M., Stramski D., Ferrari G.M., Claustre H., Bricaud A., Obolensky G., Hoepffner N. (2003)—Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. Journal of Geophysical Research, 108: 3211. doi: http://dx.doi.org/10.1029/2001JC000882.
   Web of Science ®Google Scholar
• Barsi J.A., Markham B.L., Pedelty J.A. (2011)—The operational land imager: spectral response and spectral uniformity. Proceedings SPIE 8153, Earth Observing Systems XVI, 81530G. doi: http://dx.doi.org/10.1117/12.895438.
   Google Scholar
• Brando V.E., Dekker A.G. (2003)—Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing, 41 (6): 1378–1387. doi: http://dx.doi.org/10.1109/TGRS.2003.812907.
   Web of Science ®Google Scholar
• Brando V.E., Anstee J.M., Wettle M., Dekker A.G., Phinn S.R., Roelfsema C. (2009)—A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment, 113: 775–770. doi: http://dx.doi.org/10.1016/j.rse.2008.12.003.
   Google Scholar
• Bresciani M., Bolpagni R., Braga F., Oggioni A., Giardino C. (2012a)—Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data. Journal of Limnology, 71 (1): 180–190. doi: http://dx.doi.org/10.4081/jlimnol.2012.e19.
   Google Scholar
• Bresciani M., Vascellari M., Giardino C., Matta E. (2012b)—Remote Sensing Supports the Definition of the Water Quality Status of Lake Omodeo (Italy). European Journal of Remote Sensing, 45: 349–360. doi: http://dx.doi.org/10.5721/EuJRS20124530.
   Web of Science ®Google Scholar
• Bresciani M., Rossini M., Cogliati S., Colombo R., Morabito G., Matta E., Pinardi M., Giardino C. (2013)—Analysis of intra- and inter-daily chlorophyll-a dynamics in Mantua Superior Lake with spectroradiometric continuous measures. Marine and Freshwater Research, 64: 1–14. doi: http://dx.doi.org/10.1071/MF12229.
   Google Scholar
• Cingolani L., Marchetti G., Martinelli A., Rapi G., Cantucci A. (2005)—Misure per il contenimento del carico diffuso nel Piano di Tutela delle acque della Regione Umbria. 2nd International Conference LIFE, Libri ARPA Umbria, Italy.
   Google Scholar
• Cracknell A.P., Newcombe S.K., Black A.F., Kirby N.E. (2001)—The ADMAP (algal bloom detection, monitoring and prediction) Concerted Action. International Journal of Remote Sensing, 22: 205–247. doi: http://dx.doi.org/10.1080/014311601449916.
   Web of Science ®Google Scholar
• Crosetto M., Tarantola S., Saltelli A. (2000)—Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agriculture, Ecosystems and Environment, 81: 71–79. doi: http://dx.doi.org/10.1016/S0167-8809(00)00169-9.
   Web of Science ®Google Scholar
• Dall'Olmo G., Gitelson A.A. (2005)—Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results. Applied Optics, 44: 412–422. doi: http://dx.doi.org/10.1364/AO.44.000412.
   PubMed Web of Science ®Google Scholar
• Dekker A.G., Malthus T.J., Hoogenboom H.J. (1995)—The remote sensing of inland water quality. In: Advances in Environmental Remote Sensing, Danson F.M. and Plummer S.E. (Eds.), Chichester, United Kingdom Wiley, pp. 123–142.
   Google Scholar
• Dekker A.G., Brando V.E., Anstee J.M., Pinnel N., Kutser T., Hoogenboom H.J., Pasterkamp R., Peters S.W.M., Vos R.J., Olbert C., Malthus T.J. (2001)—Imaging spectrometry of water. In: Imaging Spectrometry: Basic Principles and Prospective Applications, Dordrecht, The Netherlands: Kluwer 4, Remote Sensing and Digital Image Processing, pp. 307–359.
   Google Scholar
• Donlon C., Berruti B., Buongiorno A., Ferreira M.-H., Féménias P., Frerick J., Goryl P., Klein U., Laur H., Mavrocordatos C., Nieke J., Rebhan H., Seitz B., Stroede J., Sciarra R. (2012)—The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission. Remote Sensing of Environment, 120: 37–57. doi: http://dx.doi.org/10.1016/j.rse.2011.07.024.
   Web of Science ®Google Scholar
• Drusch M., Del Bello U., Carlier S., Colin O., Fernandez V., Gascon F., Hoersch B., Isola C., Laberinti P., Martimort P., Meygret A., Spoto F., Sy O., Marchese F., Bargellini P. (2012)—Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120: 25–36. doi: http://dx.doi.org/10.1016/j.rse.2011.11.026.J.
   Web of Science ®Google Scholar
• Duarte M., Velez-Reyes M., Tarantola S., Gilbes F., Armstrong R. (2003)—A probabilistic sensitivity analysis of water-leaving radiance to water constituents in coastal shallow waters. In: Proceedings of SPIE, Ocean-color Remote Sensing: Inherent Optical Properties and Applications I, 5155: 162–173.
   Google Scholar
• Dutang C., Savicky P. (2013)—R package version 1.13-randtoolbox: Generating and Testing Random Numbers.
   Google Scholar
• Garver S.A., Siegel D.A. (1997)—Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: I. Time series from the Sargasso Sea. Journal of Geophysical Research., 102 (C8): 18607–18625. doi: http://dx.doi.org/10.1029/96JC03243.
   Google Scholar
• Gerardino-Neira C., Goodman J., Velez-Reyes M., Rivera W. (2008)—Sensitivity Analysis of a Hyperspectral Inversion Model for Remote Sensing of Shallow Coastal Ecosystems. In: Proceedings of IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2008, Boston, Massachusetts, USA, 8–11 July 2008, pp. 98–101.
   Google Scholar
• Giardino C., Brando V.E., Dekker A.G., Strömbeck N., Candiani, G. (2007)—Assessment of water quality in Lake Garda (Italy) using Hyperion. Remote Sensing of Environment, 109: 183–195. doi: http://dx.doi.org/10.1016/j.rse.2006.12.017.
   Web of Science ®Google Scholar
• Giardino C., Bresciani M., Villa P., Martinelli A. (2010)—Application of remote sensing in water resource management: the case study of Lake Trasimeno, Italy. Water Resources Management. doi: http://dx.doi.org/10.1007/S11269-010-9639-3.
   Google Scholar
• Giardino C., Candiani G., Bresciani M., Lee Z., Gagliano S., Pepe M. (2012)—BOMBER: A tool for estimating water quality and bottom properties from remote sensing images. Computers & Geosciences, 45: 313–318. doi: http://dx.doi.org/10.1016/j.cageo.2011.11.022.
   Web of Science ®Google Scholar
• Giardino C., Bresciani M., Valentini E., Gasperini L., Bolpagni R., Brando V.E. (2014)—Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake. Remote Sensing of Environment, 157: 48–57. doi: http://dx.doi.org/10.1016/j.rse.2014.04.034.
   Google Scholar
• Gitelson A.A., Schalles J.F., Hladik C.M. (2007)—Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study. Remote Sensing of Environment, 109: 464–472. doi: http://dx.doi.org/10.1016/j.rse.2007.01.016.
   Web of Science ®Google Scholar
• Gordon H.R., Brown O.B., Evans R.H., Brown J.W., Smith R.C., Baker K.S., Clark D.K. (1988)—Semianalytic radiance Model of Ocean Color. Journal of Geophysical Research, 93 (D):10909–10924.
   Google Scholar
• Irons J.R., Dwyer J., Barsi, J.A. (2012)—The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment, 122: 11–21. doi: http://dx.doi.org/10.1016/j.rse.2011.08.026.
   Web of Science ®Google Scholar
• Kutser T., Pierson D., Kallio K., Reinart A., Sobek S. (2005)—Mapping lake CDOM by satellite remote sensing. Remote Sensing of Environment, 94: 535–540. doi: http://dx.doi.org/10.1016/j.rse.2004.11.009.
   Web of Science ®Google Scholar
• Lee Z., Carder K.L., Mobley C.D., Steward R.G., Patch J.S. (1999)—Hypespectral remote sensing for shallow waters: 2. Deriving bottom depth sand water properties by optimization. Applied Optics, 38: 3831–3843. doi: http://dx.doi.org/10.1364/AO.38.003831.
   PubMed Web of Science ®Google Scholar
• Lee Z.P., Carder K.L., Arnone R.A. (2002)—Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41 (27): 5755–5772. doi: http://dx.doi.org/10.1364/AO.41.005755.
   PubMed Web of Science ®Google Scholar
• Lee Z.P., Arnone R., Hu C.M., Werdell P.J., Lubac B. (2010)—Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm. Applied Optics, 49 (3): 369–381. doi: http://dx.doi.org/10.1364/AO.49.000369.
   Google Scholar
• Lilburne L., Tarantola S. (2009)—Sensitivity analysis of spatial models. International Journal of Geographical Information Science, 23 (2): 151–168. doi: http://dx.doi.org/10.1080/13658810802094995.
   Web of Science ®Google Scholar
• Lindell T., Pierson D., Premazzi G., Zilioli E. (1999)—Manual for monitoring European lakes using remote sensing techniques. Luxembourg, Office for Official Publications of the European Communities, EUR Report n. 18665 EN.
   Google Scholar
• Lobo F.L, Costa M.P.F., Novo E.M.L.M. (2014)—Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities. Remote Sensing of Environment, 157: 170–184. doi: http://dx.doi.org/10.1016/j.rse.2014.04.030.
   Google Scholar
• Lorenzen C.J. (1967)—Determination of chlorophyllandpheo-pigments: spectrophotometric equations. Limnology and Oceanography, 12: 343–346. doi: http://dx.doi.org/10.4319/LO.1967.12.2.0343.
   Web of Science ®Google Scholar
• Maffione R.A., Dana D.R. (1997)—Instruments and methods for measuring the backwardscattering coefficient of ocean waters. Applied Optics, 36: 6057–6067. doi: http://dx.doi.org/10.1364/AO.36.006057.
   PubMed Web of Science ®Google Scholar
• Malenovsky Z., Rott H., Cihlar J., Schaepman M.E., Garcia-Santos G., Fernandes R., Berger M. (2012)—Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sensing of Environment, 120: 91–101. doi: http://dx.doi.org/10.1016/j.rse.2011.09.02.
   Web of Science ®Google Scholar
• Manache G., Melching C.S. (2008)—Identification of reliable regression- and correlation- based sensitivity measures for importance ranking of water-quality model parameters. Environmental Modelling & Software, 23: 549–562. doi: http://dx.doi.org/10.1016/j.envsoft.2007.08.001.
   Google Scholar
• Mobley C.D. (1994)—Light and water: radiative transfer in natural waters. Academic Press.
   Google Scholar
• Morris M.D. (1991)—Factorial sampling plans for preliminary computational experiments. Technometrics, 33: 161–174. doi: http://dx.doi.org/10.1080/00401706.1991.10484804.
   Web of Science ®Google Scholar
• Odermatt D., Gitelson A., Brando V.E., Schaepman M. (2012)—Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote Sensing of Environment, 118: 116–126. doi: http://dx.doi.org/10.1016/j.rse.2011.11.013.
   Web of Science ®Google Scholar
• Olmanson L.G., Brezonik P.L., Bauer M.E. (2013)—Geospatial and Temporal Analysis of a 20-Year Record of Landsat-Based Water Clarity in Minnesota's 10,000 Lakes. Journal of the American Water Resources Association, 50 (3): 748–761. doi: http://dx.doi.org/10.1111/jawr.12138.
   Google Scholar
• Pahlevan N., Lee Z., Wei J., Schaaf C.B., Schott J.R., Berk A. (2014)—On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing. Remote Sensing of Environment, 154: 272–284. doi: http://dx.doi.org/10.1016/j.rse.2014.08.001.
   Web of Science ®Google Scholar
• Palmer S.C., Hunter P.D., Lankester T., Hubbard S., Spyrakos E., Tyler A.N., Présing M., Horvàth H., Lamb A., Balzter H., Tóth V.R. (2014)—Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake. Remote Sensing of Environment, 157: 158–169. doi: http://dx.doi.org/10.1016/j.rse.2014.07.024.
   Google Scholar
• Pasterkamp R., Dekker A.G., Hoogenboom H.J., Rijkeboer M., Hakvoort J.H. (1999)—The effect of the specific inherent optical properties on assessing water quality in Dutch inland waters using matrix inversion. IEEE International Geoscience and Remote Sensing Symposium, 4: 2095–2097.
   Google Scholar
• Pierson D.C., Strömbeck N. (2001)—Estimation of radiance reflectance and the concentrations of optically active substances in Lake Malaren, Sweden, based on direct and inverse solutions of a simple model. The Science of the Total Environment, 268: 171–188. doi: http://dx.doi.org/10.1016/S0048-9697(00)00680-X.
   PubMed Web of Science ®Google Scholar
• Salmaso N., Mosello R. (2010)—Limnological research in the deep southern subalpine lakes: synthesis, directions and perspectives. Advances in Oceanography and Limnology, 1 (1): 29–66. doi: http://dx.doi.org/10.1080/19475721003735773.
   Google Scholar
• Saltelli A., Chan K., Scott M. (2000)—Sensitivity Analysis. Probability and Statistics Series. John Wiley & Sons, New York, NY, USA, pp. 475.
   Google Scholar
• Saltelli A. (2002)—Making best use of model valuations to compute sensitivity indices. Computer Physics Communications, 145: 280–297. doi: http://dx.doi.org/10.1016/S0010-4655(02)00280-1.
   Web of Science ®Google Scholar
• Saltelli A., Tarantola S., Campolongo F., Ratto M. (2004)—Sensitivity analysis in practice: a guide to assessing scientific models. John Wiley & Sons.
   Google Scholar
• Saltelli A., Ratto M., Tarantola S., Campolongo F., European Commission, Joint Research Centre of Ispra (2006)—Sensitivity analysis practices: strategies for model-based inference. Reliability Engineering and System Safety, 91 (10–11): 1109–1125. doi: http://dx.doi.org/10.1016/j.ress.2005.11.014.
   Web of Science ®Google Scholar
• Saltelli A., Annoni P., Azzini I., Campolongo F., Ratto M., Tarantola S. (2010)—Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181 (2): 259–270. doi: http://dx.doi.org/10.1016/j.cpc.2009.09.018.
   Web of Science ®Google Scholar
• Sobol’ I.M. (1993)—Sensitivity estimates for nonlinear mathematical models. Mathematical Modeling and Computational Experiment, 14: 407–414.
   Google Scholar
• Strömbeck N., Pierson D.C. (2001)—The effects of variability in the inherent optical properties on estimations of chlorophyll a by remote sensing in Swedish freshwaters. Science of the Total Environment, 268: 123–137. doi: http://dx.doi.org/10.1016/S0048-9697(00)00681-1.
   PubMed Web of Science ®Google Scholar
• Ufermann S., Robinson I. S. (2002)—On the sensitivity of semi-analytical algorithms for the retrieval of water quality parameters from optical measurements in coastal waters. IGARSS 2002, IEEE International, 3: 1456–1458. doi: http://dx.doi.org/10.1109/IGARSS.2002.1026147.
   Google Scholar
• Vanhellemont Q., Ruddick K. (2014)—Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sensing of Environment, 145: 105–115. doi: http://dx.doi.org/10.1016/j.rse.2014.01.009.
   Web of Science ®Google Scholar
• Van Der Linde D. (1998)—Protocol for Total Suspended Matter (Ispra: CEC-JRC). Technical Note.
   Google Scholar
• Zambrano-Bigiarini M. (2013)—Sobol sensitivity.R. Available online at: http://ipsc.jrc.ec.europa.eu/fileadmin/repository/eas/sensitivity/software/sobol_sensitivity.R (active at 7th of July 2013).
   Google Scholar