Skip to main content

Advertisement

SpringerLink
Log in

Applications of Imaging Spectrometry in Inland Water Quality Monitoring—a Review of Recent Developments

  • Published:
Water, Air, & Soil Pollution Aims and scope Submit manuscript

Abstract

Inland waters represent complex and highly variable ecosystems, which are also of immense recreational and economic values to humans. The maintenance of high quality of inland water status necessitates development of means for rapid quality monitoring. Imaging spectrometry techniques are proven technology that can provide useful information for the estimation of inland water quality attributes due to fast speed, noninvasiveness, ease of use, and in situ operation. Although there have been many studies conducted on the use of imaging spectrometry for marine water quality monitoring and assessment, relatively few studies have considered inland water bodies. The aim of this review is to present an overview of imaging spectrometry technologies for the monitoring of inland waters including spaceborne and airborne and field or ground-based hyperspectral systems. Some viewpoints on the current situation and suggestions for future research directions are also proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AISA:

Airborne imaging spectrometer for applications

ALI:

Advanced land imager

AVIRIS:

Airborne visible infrared imaging spectrometer

CASI:

Compact airborne spectrographic imager

CDOM:

Colored dissolved organic matter

COD:

Concentrations of chemical oxygen demand

CTHIS:

Chromo-tomographic hyperspectral imaging spectrometer

DP:

Dissolved phosphorus

DSF:

Definitive spectral factors

ETM+:

Enhanced thematic mapper

GA-PLS:

Genetic algorithm-partial least square

GLI:

Global imager

HICO:

Hyperspectral imager for the coastal ocean

HJ-1A/1B:

Huan Jing-1A/1B

HSI:

Hyperspectral imaging

HyMap:

HyMap imaging spectrometer

LISS:

Linear imaging self-scanning sensor

MERIS:

Medium resolution imaging spectrometer

MIVIS:

Multispectral infrared visible imaging spectrometer

MODIS:

Moderate resolution imaging spectroradiometer

NIR:

Near infrared

NH3-N:

Ammonia nitrogen

NO3-N:

Nitrate nitrogen

NVSS:

Nonvolatile suspended solids

OC4v4:

Ocean Color 4 Version 4

PMI:

Programmable multispectral imager

R 2 :

Coefficient of determination

ROSIS:

Reflective optics system imaging spectrometer

SAMO-LUT:

Semi-analytical model-optimizing and look-up-table

SeaWiFS:

Sea wide field-of-view sensor

SPIM:

Suspended particulate inorganic material

SD:

Secchi depth

SS:

Suspended sediment

SSC:

Suspended sediment concentration

SPM:

Suspended particulate matter

SVR:

Support vector regression

TM:

Thematic mapper

TN:

Total nitrogen

TP:

Total phosphorus

TSM:

Total suspended matter

TSS:

Total suspended solids

References

  • Abd-Elrahman, A., Croxton, M., Pande-Chettri, R., Toor, G. S., Smith, S., & Hill, J. (2011). In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4), 463–472.

    Article  Google Scholar 

  • Ammenberg, P., Flink, P., Lindell, T., Pierson, D., & Strombeck, N. (2002). Bio-optical modelling combined with remote sensing to assess water quality. International Journal of Remote Sensing, 23(8), 1621–1638.

    Article  Google Scholar 

  • Barbin, D. F., ElMasry, G., Sun, D.-W., & Allen, P. (2013). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry, 138(2–3), 1162–1171.

    Article  CAS  Google Scholar 

  • Barbini, R., Colao, F., Fantoni, R., Fiorani, L., Okladnikov, I. G., & Palucci, A. (2005). Comparison of SeaWiFS, MODIS-Terra and MODIS-Aqua in the Southern Ocean. International Journal of Remote Sensing, 26(11), 2471–2478.

    Article  Google Scholar 

  • Bertram, J., & Balance, R. (1996). A practical guide to the design and implementation of fresh water quality studies and monitoring programmes. Published on behalf of United Nations Environmental Programme (UNEP) and World Health Organization (WHO), E & FN Spon, London, UK, 172–177.

  • Binding, C., Greenberg, T., Jerome, J., Bukata, R., & Letourneau, G. (2010a). An assessment of MERIS algal products during an intense bloom in Lake of the Woods. Journal of Plankton Research, 33(5), 793–806.

    Article  Google Scholar 

  • Binding, C., Jerome, J., Bukata, R., & Booty, W. (2010b). Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery. International Journal of Remote Sensing, 31(19), 5239–5255.

    Article  Google Scholar 

  • Burd, A. B., & Jackson, G. A. (2002). Modeling steady-state particle size spectra. Environmental Science and Technology, 36(3), 323–327.

    Article  CAS  Google Scholar 

  • Campbell, G., Phinn, S. R., Dekker, A. G., & Brando, V. E. (2011). Remote sensing of water quality in an Australian tropical freshwater impoundment using matrix inversion and MERIS images. Remote Sensing of Environment, 115(9), 2402–2414.

    Article  Google Scholar 

  • Chawira, M., Dube, T., & Gumindoga, W. (2013). Remote sensing based water quality monitoring in Chivero and Manyame lakes of Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 66, 38–44.

    Article  Google Scholar 

  • Chebud, Y., Naja, G. M., Rivero, R. G., & Melesse, A. M. (2012). Water quality monitoring using remote sensing and an artificial neural network. Water, Air, and Soil Pollution, 223(8), 4875–4887.

    Article  CAS  Google Scholar 

  • Cheng, C., Wei, Y., Xu, J., & Yuan, Z. (2013). Remote sensing estimation of chlorophyll a and suspended sediment concentration in turbid water based on spectral separation. Optik - International Journal for Light and Electron Optics, 124(24), 6815–6819.

    Article  CAS  Google Scholar 

  • Cheng, J.-H., & Sun, D.-W. (2015). Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis. LWT--Food Science and Technology, 62(2), 1060–1068.

    Article  CAS  Google Scholar 

  • Cheng, J.-H., Sun, D.-W., Pu, H., & Zhu, Z. (2015). Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. Food Chemistry, 185, 245–253.

    Article  CAS  Google Scholar 

  • Cheng, J.-H., Sun, D.-W., Qu, J.-H., Pu, H.-B., Zhang, X.-C., Song, Z., Chen, X., & Zhang, H. (2016). Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet. Journal of Food Engineering, 182, 9–17.

    Article  CAS  Google Scholar 

  • Clark, R. N. (1981). Water frost and ice: the near‐infrared spectral reflectance 0.65–2.5 μm. Journal of Geophysical Research - Solid Earth, 86(B4), 3087–3096.

    Article  CAS  Google Scholar 

  • Curcio, J. A., & Petty, C. C. (1951). The near infrared absorption spectrum of liquid water. JOSA, 41(5), 302–302.

    Article  CAS  Google Scholar 

  • D’Alimonte, D., Zibordi, G., & Berthon, J.-F. (2007). A statistical index of bio-optical seawater types. IEEE Transactions on Geoscience and Remote Sensing, 45(8), 2644–2651.

    Article  Google Scholar 

  • Dall’Olmo, G., Gitelson, A. A., Rundquist, D. C., Leavitt, B., Barrow, T., & Holz, J. C. (2005). Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands. Remote Sensing of Environment, 96(2), 176–187.

    Article  Google Scholar 

  • Dekker, A. G., Malthus, T. J., & Hoogenboom, H. J. (1995). The remote sensing of inland water quality. In S. E. Plummer, F. M. Danson (Eds.), Advances in environmental remote sensing (pp. 123–142). New York: Wiley.

  • Devred, E., Sathyendranath, S., Stuart, V., & Platt, T. (2011). A three component classification of phytoplankton absorption spectra: application to ocean-color data. Remote Sensing of Environment, 115(9), 2255–2266.

    Article  Google Scholar 

  • Dierssen, H. M., Chlus, A., & Russell, B. (2015). Hyperspectral discrimination of floating mats of seagrass wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay using airborne remote sensing. Remote Sensing of Environment, 167(15), 247–258.

    Article  Google Scholar 

  • Duane Nellis, M., Harrington, J. A., Jr., & Wu, J. (1998). Remote sensing of temporal and spatial variations in pool size, suspended sediment, turbidity, and Secchi depth in Tuttle Creek Reservoir, Kansas: 1993. Geomorphology, 21(3), 281–293.

    Article  Google Scholar 

  • Dupouy, C., Neveux, J., Ouillon, S., Frouin, R., Murakami, H., Hochard, S., & Dirberg, G. (2010). Inherent optical properties and satellite retrieval of chlorophyll concentration in the lagoon and open ocean waters of New Caledonia. Marine Pollution Bulletin, 61(7), 503–518.

    Article  CAS  Google Scholar 

  • Elmasry, G., Barbin, D. F., Sun, D.-W., & Allen, P. (2012). Meat quality evaluation by hyperspectral imaging technique: an overview. Critical Reviews in Food Science and Nutrition, 52(8), 689–711.

    Article  Google Scholar 

  • ElMasry, G., Sun, D.-W., & Allen, P. (2013). Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. Journal of Food Engineering, 117(2), 235–246.

    Article  CAS  Google Scholar 

  • Feng, Y.-Z., & Sun, D.-W. (2013). Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 109, 74–83.

    Article  CAS  Google Scholar 

  • Feng, Y.-Z., ElMasry, G., Sun, D.-W., Scannell, A. G. M., Walsh, D., & Morcy, N. (2013). Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets. Food Chemistry, 138(2–3), 1829–1836.

    Article  CAS  Google Scholar 

  • Giardino, C., Bresciani, M., Pilkaityte, R., Bartoli, M., & Razinkovas, A. (2010). In situ measurements and satellite remote sensing of case 2 waters: first results from the Curonian Lagoon. Oceanologia, 52(2), 197–210.

    Article  Google Scholar 

  • Gitelson, A. (1992). The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13(17), 3367–3373.

    Article  Google Scholar 

  • Gitelson, A. A. (1993). Nature of the peak near 700 nm on the radiance spectra and its application for remote estimation of phytoplankton pigments in inland waters. Proc. SPIE 1971, 8th Meeting on Optical Engineering in Israel: Optical Engineering and Remote Sensing, 170 (August 13, 1993). doi:10.1117/12.150992.

  • Gitelson, A., Garbuzov, G., Szilagyi, F., Mittenzwey, K. H., Karnieli, A., & Kaiser, A. (1993). Quantitative remote sensing methods for real-time monitoring of inland waters quality. International Journal of Remote Sensing, 14(7), 1269–1295.

    Article  Google Scholar 

  • Gitelson, A. A., Dall’Olmo, G., Moses, W., Rundquist, D. C., Barrow, T., Fisher, T. R., Gurlin, D., & Holz, J. (2008). A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: validation. Remote Sensing of Environment, 112(9), 3582–3593.

    Article  Google Scholar 

  • Gitelson, A. A., Gurlin, D., Moses, W. J., & Barrow, T. (2009). A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environmental Research Letters, 4(4), 045003.

    Article  CAS  Google Scholar 

  • Gitelson, A. A., Gao, B.-C., Li, R.-R., Berdnikov, S., & Saprygin, V. (2011). Estimation of chlorophyll-a concentration in productive turbid waters using a hyperspectral imager for the coastal ocean—the Azov Sea case study. Environmental Research Letters, 6(2), 024023.

    Article  CAS  Google Scholar 

  • Glasgow, H. B., Burkholder, J. M., Reed, R. E., Lewitus, A. J., & Kleinman, J. E. (2004). Real-time remote monitoring of water quality: a review of current applications, and advancements in sensor, telemetry, and computing technologies. Journal of Experimental Marine Biology and Ecology, 300(1), 409–448.

    Article  Google Scholar 

  • Goetz, A. (1992). Principles of narrow band spectrometry in the visible and IR: instruments and data analysis. Imaging Spectroscopy: Fundamental and Prospective Applications. Dordrecht: Kluwer Academic, 12898, pp. 21–32.

  • Goetz, A. F., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147–1153.

    Article  CAS  Google Scholar 

  • González Vilas, L., Spyrakos, E., & Torres Palenzuela, J. M. (2011). Neural network estimation of chlorophyll-a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain). Remote Sensing of Environment, 115(2), 524–535.

    Article  Google Scholar 

  • Gordon, H. R., & Morel, A. Y. (1983). Remote assessment of ocean color for interpretation of satellite visible imagery: a review (pp. 96–102). New York: Halliday Lithograph.

    Book  Google Scholar 

  • Gordon, H. R., Brown, O. B., & Jacobs, M. M. (1975). Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Applied Optics, 14(2), 417–427.

    Article  CAS  Google Scholar 

  • Guo, Y., Li, Y., Zhu, L., Wang, Q., Lv, H., Huang, C., & Li, Y. (2016). An inversion-based fusion method for inland water remote monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12), 5599–5611.

    Article  Google Scholar 

  • Hadjimitsis, D. G., & Clayton, C. (2009). Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data. Environmental Monitoring and Assessment, 159(1), 281–292.

    Article  CAS  Google Scholar 

  • Hadjimitsis, D. G., Hadjimitsis, M. G., Toulios, L., & Clayton, C. (2010). Use of space technology for assisting water quality assessment and monitoring of inland water bodies. Physics and Chemistry of the Earth, Parts A/B/C, 35(1), 115–120.

    Article  Google Scholar 

  • Hakvoort, H., De Haan, J., Jordans, R., Vos, R., Peters, S., & Rijkeboer, M. (2002). Towards airborne remote sensing of water quality in The Netherlands—validation and error analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 57(3), 171–183.

    Article  Google Scholar 

  • Han, L., & Jordan, K. J. (2005). Estimating and mapping chlorophyll-a concentration in Pensacola Bay, Florida using Landsat ETM+ data. International Journal of Remote Sensing, 26(23), 5245–5254.

    Article  Google Scholar 

  • Han, L., & Rundquist, D. C. (1997). Comparison of NIR/RED ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: a case study in a turbid reservoir. Remote Sensing of Environment, 62(3), 253–261.

    Article  Google Scholar 

  • He, W., Chen, S., Liu, X., & Chen, J. (2008). Water quality monitoring in a slightly-polluted inland water body through remote sensing—case study of the Guanting Reservoir in Beijing, China. Frontiers of Environmental Science & Engineering in China, 2(2), 163–171.

    Article  Google Scholar 

  • Hoogenboom, H., Dekker, A., & Althuis, I. A. (1998). Simulation of AVIRIS sensitivity for detecting chlorophyll over coastal and inland waters. Remote Sensing of Environment, 65(3), 333–340.

    Article  Google Scholar 

  • Horion, S., Bergamino, N., Stenuite, S., Descy, J.-P., Plisnier, P.-D., Loiselle, S., & Cornet, Y. (2010). Optimized extraction of daily bio-optical time series derived from MODIS/Aqua imagery for Lake Tanganyika, Africa. Remote Sensing of Environment, 114(4), 781–791.

    Article  Google Scholar 

  • Ignat, T., Lurie, S., Nyasordzi, J., Ostrovsky, V., Egozi, H., Hoffman, A. et al. (2014). Forecast of apple internal quality indices at harvest and during storage by vis-nir spectroscopy. Food and Bioprocess Technology, 7(10), 2951–2961.

  • Kabbara, N., Benkhelil, J., Awad, M., & Barale, V. (2008). Monitoring water quality in the coastal area of Tripoli (Lebanon) using high-resolution satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 63(5), 488–495.

    Article  Google Scholar 

  • Kageyama, Y., Takahashi, J., Nishida, M., Kobori, B., & Nagamoto, D. (2016). Analysis of water quality in Miharu Dam reservoir, Japan, using UAV data. IEEJ Transactions on Electrical and Electronic Engineering, 11, S183–S185.

    Article  CAS  Google Scholar 

  • Kallio, K., Kutser, T., Hannonen, T., Koponen, S., Pulliainen, J., Vepsäläinen, J., & Pyhälahti, T. (2001). Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons. Science of the Total Environment, 268(1), 59–77.

    Article  CAS  Google Scholar 

  • Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry, 141(1), 389–396.

    Article  CAS  Google Scholar 

  • Kokhanovsky, A. A., Breon, F. M., Cacciari, A., Carboni, E., Diner, D., Di Nicolantonio, W., … & Li, Z. 2007. Aerosol remote sensing over land: a comparison of satellite retrievals using different algorithms and instruments. Atmospheric Research, 85(3), 372–394.

  • Koponen, S., Pulliainen, J., Kallio, K., & Hallikainen, M. (2002). Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment, 79(1), 51–59.

    Article  Google Scholar 

  • Koponen, S., Attila, J., Pulliainen, J., Kallio, K., Pyhälahti, T., Lindfors, A., Rasmus, K., & Hallikainen, M. (2007). A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland. Continental Shelf Research, 27(2), 228–244.

    Article  Google Scholar 

  • Koponen, S., Ruiz-Verdu, A., Heege, T., Heblinski, J., Sorensen, K., Kallio, K., Pyhälahti, T., Doerffer, R., Brockmann, C., & Peters, M. (2008). Development of MERIS lake water algorithms. ESA Validation Report, p. 5.

  • Kudela, R. M., Palacios, S. L., Austerberry, D. C., Accorsi, E. K., Guild, L. S., & Torres-Perez, J. (2015). Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment, 167, 196–205.

    Article  Google Scholar 

  • Kutser, T., Pierson, D. C., Kallio, K. Y., Reinart, A., & Sobek, S. (2005a). Mapping lake CDOM by satellite remote sensing. Remote Sensing of Environment, 94(4), 535–540.

    Article  Google Scholar 

  • Kutser, T., Pierson, D. C., Tranvik, L., Reinart, A., Sobek, S., & Kallio, K. (2005b). Using satellite remote sensing to estimate the colored dissolved organic matter absorption coefficient in lakes. Ecosystems, 8(6), 709–720.

    Article  Google Scholar 

  • Le, C., Li, Y., Zha, Y., Sun, D., Huang, C., & Lu, H. (2009). A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China. Remote Sensing of Environment, 113(6), 1175–1182.

    Article  Google Scholar 

  • Li, L., Li, L., Song, K., Li, Y., Tedesco, L. P., Shi, K., & Li, Z. (2013). An inversion model for deriving inherent optical properties of inland waters: establishment, validation and application. Remote Sensing of Environment, 135, 150–166.

    Article  Google Scholar 

  • Liu, D., Sun, D.-W., & Zeng, X.-A. (2014). Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food and Bioprocess Technology, 7(2), 307–323.

    Article  Google Scholar 

  • Ma, J., Sun, D.-W., & Pu, H. (2016). Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. Food Chemistry, 197(Part: A), 848–854.

    Article  CAS  Google Scholar 

  • Mahasandana, S., Tripathi, N. K., & Honda, K. (2009). Sea surface multispectral index model for estimating chlorophyll a concentration of productive coastal waters in Thailand. Canadian Journal of Remote Sensing, 35(3), 287–296.

    Article  Google Scholar 

  • Markogianni, V., Dimitriou, E., & Karaouzas, I. (2014). Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications. Environmental Monitoring and Assessment, 186(8), 5009–5026.

    Article  CAS  Google Scholar 

  • Matsushita, B., Yang, W., Yu, G., Oyama, Y., Yoshimura, K., & Fukushima, T. (2015). A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 28–37.

    Article  Google Scholar 

  • Matthews, M. W., Bernard, S., & Winter, K. (2010). Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS. Remote Sensing of Environment, 114(9), 2070–2087.

    Article  Google Scholar 

  • Mishra, S., & Mishra, D. R. (2012). Normalized difference chlorophyll index: a novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394–406.

    Article  Google Scholar 

  • Morel, A., & Gentili, B. (2009). A simple band ratio technique to quantify the colored dissolved and detrital organic material from ocean color remotely sensed data. Remote Sensing of Environment, 113(5), 998–1011.

    Article  Google Scholar 

  • Moses, W. J., Gitelson, A. A., Berdnikov, S., & Povazhnyy, V. (2009a). Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data—successes and challenges. Environmental Research Letters, 4(4), 045005.

    Article  CAS  Google Scholar 

  • Moses, W. J., Gitelson, A. A., Berdnikov, S., & Povazhnyy, V. (2009b). Satellite estimation of chlorophyll-concentration using the red and NIR bands of MERIS—the Azov Sea case study. IEEE Geoscience and Remote Sensing Letters, 6(4), 845–849.

    Article  Google Scholar 

  • Moses, W. J., Gitelson, A. A., Berdnikov, S., Saprygin, V., & Povazhnyi, V. (2012a). Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters—the Azov Sea case study. Remote Sensing of Environment, 121, 118–124.

    Article  Google Scholar 

  • Moses, W. J., Gitelson, A. A., Perk, R. L., Gurlin, D., Rundquist, D. C., Leavitt, B. C., Barrow, T. M., & Brakhage, P. (2012b). Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data. Water Research, 46(4), 993–1004.

    Article  CAS  Google Scholar 

  • Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371.

    Article  Google Scholar 

  • Murphy, R., Tolhurst, T., Chapman, M., & Underwood, A. (2005). Estimation of surface chlorophyll‐a on an emersed mudflat using field spectrometry: accuracy of ratios and derivative‐based approaches. International Journal of Remote Sensing, 26(9), 1835–1859.

    Article  Google Scholar 

  • Murphy, R., Underwood, A., Tolhurst, T., & Chapman, M. (2008). Field-based remote-sensing for experimental intertidal ecology: case studies using hyperspatial and hyperspectral data for New South Wales (Australia). Remote Sensing of Environment, 112(8), 3353–3365.

    Article  Google Scholar 

  • Nechad, B., Ruddick, K., & Park, Y. (2010). Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sensing of Environment, 114(4), 854–866.

    Article  Google Scholar 

  • Nieke, J., Schwarzer, H. H., Neumann, A., & Zimmermann, G. (1997). Imaging spaceborne and airborne sensor systems in the beginning of the next century. In Aerospace Remote Sensing (pp. 581–592). International Society for Optics and Photonics.

  • 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.

    Article  Google Scholar 

  • Olmanson, L. G., Brezonik, P. L., & Bauer, M. E. (2013). Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota. Remote Sensing of Environment, 130, 254–265.

    Article  Google Scholar 

  • Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., & Gualtieri, A. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113, S110–S122.

    Article  Google Scholar 

  • Pu, H., Kamruzzaman, M., & Sun, D.-W. (2015). Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends in Food Science & Technology, 45(1), 86–104.

    Article  CAS  Google Scholar 

  • Pulliainen, J., Kallio, K., Eloheimo, K., Koponen, S., Servomaa, H., Hannonen, T., Tauriainen, S., & Hallikainen, M. (2001). A semi-operative approach to lake water quality retrieval from remote sensing data. Science of the Total Environment (Amsterdam), 268(1), 79–93.

    Article  CAS  Google Scholar 

  • Richards, J. A. (2005). Analysis of remotely sensed data: the formative decades and the future. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 422–432.

    Article  Google Scholar 

  • Sanders, T. G. (1983). Design of networks for monitoring water quality (pp. 5–22). Littleton: Water Resources Publication.

    Google Scholar 

  • Santini, F., Alberotanza, L., Cavalli, R. M., & Pignatti, S. (2010). A two-step optimization procedure for assessing water constituent concentrations by hyperspectral remote sensing techniques: an application to the highly turbid Venice lagoon waters. Remote Sensing of Environment, 114(4), 887–898.

    Article  Google Scholar 

  • Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34(4), 710–714.

    Article  Google Scholar 

  • Schalles, J. F., Gitelson, A. A., Yacobi, Y. Z., & Kroenke, A. E. (1998). Estimation of chlorophyll a from time series measurements of high spectral resolution reflectance in an eutrophic lake. Journal of Phycology, 34(2), 383–390.

    Article  Google Scholar 

  • Shi, K., Li, Y., Li, L., Lu, H., Song, K., Liu, Z., Xu, Y., & Li, Z. (2013). Remote chlorophyll-a estimates for inland waters based on a cluster-based classification. Science of the Total Environment (Amsterdam), 444, 1–15.

    Article  CAS  Google Scholar 

  • Song, K., Li, L., Tedesco, L. P., Li, S., Clercin, N. A., Hall, B. E., Li, Z., & Shi, K. (2012). Hyperspectral determination of eutrophication for a water supply source via genetic algorithm–partial least squares (GA–PLS) modeling. Science of the Total Environment (Amsterdam), 426, 220–232.

    Article  CAS  Google Scholar 

  • Staenz, K., & Held, A. (2012). Summary of current and future terrestrial civilian hyperspectral spaceborne systems. IEEE International. Geoscience and Remote Sensing Symposium, pp. 123–126.

  • Strobl, R. O., & Robillard, P. D. (2008). Network design for water quality monitoring of surface freshwaters: a review. Journal of Environmental Management, 87(4), 639–648.

    Article  Google Scholar 

  • Su, T.-C., & Chou, H.-T. (2015). Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: a case study of Tain-Pu reservoir in Kinmen, Taiwan. Remote Sensing, 7(8), 10078–10097.

    Article  Google Scholar 

  • Sun, D.-W. (2010). Hyperspectral imaging for food quality analysis and control (pp. 175–449). San Diego: Academic/Elsevier.

    Google Scholar 

  • Sun, D. Y., Li, Y. M., Wang, Q., Lv, H., Le, C. F., Huang, C. C., & Gong, S. Q. (2010). Detection of suspended-matter concentrations in the shallow subtropical lake Taihu, China, using the SVR model based on DSFs. IEEE Geoscience and Remote Sensing Letters, 7(4), 816–820.

    Article  Google Scholar 

  • Sun, D., Li, Y., Le, C., Shi, K., Huang, C., Gong, S., & Yin, B. (2013). A semi-analytical approach for detecting suspended particulate composition in complex turbid inland waters (China). Remote Sensing of Environment, 134, 92–99.

    Article  Google Scholar 

  • Thiemann, S., & Kaufmann, H. (2000). Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg Lake District, Germany. Remote Sensing of Environment, 73(2), 227–235.

    Article  Google Scholar 

  • Thiemann, S., & Kaufmann, H. (2002). Lake water quality monitoring using hyperspectral airborne data—a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake District, Germany. Remote Sensing of Environment, 81(2), 228–237.

    Article  Google Scholar 

  • Vagni, F. (2007). Survey of hyperspectral and multispectral imaging technologies. Rto Technical Report TR-SET-065-P3.

  • van der Meer, F., & De Jong, S. M. (2001). Imaging spectrometry: basic principles and prospective applications (pp. 327–357). Dordrecht: Springer.

    Google Scholar 

  • Varshney, P. K., & Arora, M. K. (2004). Advanced image processing techniques for remotely sensed hyperspectral data. Berlin: Springer.

    Book  Google Scholar 

  • Wang, J.-J., & Lu, X. (2010). Estimation of suspended sediment concentrations using Terra MODIS: an example from the Lower Yangtze River, China. Science of the Total Environment, 408(5), 1131–1138.

    Article  CAS  Google Scholar 

  • Wang, J., Lu, X., & Zhou, Y. (2007). Retrieval of suspended sediment concentrations in the turbid water of the Upper Yangtze River using Landsat ETM+. Chinese Science Bulletin, 52(2), 273–280.

    Article  Google Scholar 

  • Wang, J. J., Lu, X. X., Liew, S. C., & Zhou, Y. (2009). Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM+: an example from the Yangtze River, China. Earth Surface Processes and Landforms, 34(8), 1082–1092.

    Article  Google Scholar 

  • Wang, M., Shi, W., & Tang, J. (2011). Water property monitoring and assessment for China’s inland Lake Taihu from MODIS-Aqua measurements. Remote Sensing of Environment, 115(3), 841–854.

    Article  Google Scholar 

  • Wiangwang, N. (2006). Hyperspectral data modeling for water quality studies in Michigan’s inland lakes. Michigan State University, East Lansing, p. 243

  • Witter, D. L., Ortiz, J. D., Palm, S., Heath, R. T., & Budd, J. W. (2009). Assessing the application of SeaWiFS ocean color algorithms to Lake Erie. Journal of Great Lakes Research, 35(3), 361–370.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D.-W. (2013). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and aassessment: a review - part II: applications. Innovative Food Science & Emerging Technologies, 19, 5–28.

    Google Scholar 

  • Wulfkuehler, S., Stark, S., Dietz, J., Schmidt, H., Weiss, A., & Carle, R. (2014). Effect of water jet cutting and moderate heat treatment on quality of fresh-cut red oak leaf lettuce (Lactuca sativa L. var. crispa). Food and Bioprocess Technology, 7(12), 3478–3492.

  • Xiao, Y., Ferreira, J. G., Bricker, S. B., Nunes, J. P., Zhu, M., & Zhang, X. (2007). Trophic assessment in Chinese coastal systems—review of methods and application to the Changjiang (Yangtze) Estuary and Jiaozhou Bay. Estuaries and Coasts, 30(6), 901–918.

    Article  Google Scholar 

  • Xiong, Z., Sun, D.-W., Pu, H., Xie, A., Han, Z., & Luo, M. (2015). Non-destructive prediction of thiobarbituric acid reactive substances (TSARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chemistry, 179, 175–181.

    Article  CAS  Google Scholar 

  • Yang, W., Matsushita, B., Chen, J., & Fukushima, T. (2011). Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables. Remote Sensing of Environment, 115(5), 1247–1259.

    Article  Google Scholar 

  • Zhou, Z., & Zhao, Y. (2011). Research on the water quality monitoring system for inland lakes based on remote sensing. Procedia Environmental Sciences, 10, 1707–1711.

    Article  Google Scholar 

  • Zhou, L., Roberts, D. A., Ma, W., Zhang, H., & Tang, L. (2014). Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 41–47.

    Article  Google Scholar 

  • Zimba, P. V., & Gitelson, A. (2006). Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: model tuning and accuracy optimization. Aquaculture, 256(1), 272–286.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the International S&T Cooperation Program of China (2015DFA71150) for its support. This research was also supported by the Collaborative Innovation Major Special Projects of Guangzhou City (201508020097, 201604020007, 201604020057), the Guangdong Provincial Science and Technology Plan Projects (2015A020209016, 2016A040403040), the Key Projects of Administration of Ocean and Fisheries of Guangdong Province (A201401C04), the National Key Technologies R&D Program (2015BAD19B03), the International and Hong Kong–Macau–Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology & Equipment (2015KGJHZ001), the Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products, and the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2016LM2154).

Author information

Authors and Affiliations

  1. School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Hongbin Pu, Dan Liu, Jia-Huan Qu & Da-Wen Sun

  2. Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China

    Hongbin Pu, Dan Liu, Jia-Huan Qu & Da-Wen Sun

  3. Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou, China

    Hongbin Pu, Dan Liu, Jia-Huan Qu & Da-Wen Sun

  4. Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland

    Da-Wen Sun

Authors
  1. Hongbin Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jia-Huan Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Da-Wen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Da-Wen Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pu, H., Liu, D., Qu, JH. et al. Applications of Imaging Spectrometry in Inland Water Quality Monitoring—a Review of Recent Developments. Water Air Soil Pollut 228, 131 (2017). https://doi.org/10.1007/s11270-017-3294-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11270-017-3294-8

Keywords

Search

Navigation