Skip to main content
SpringerLink
Log in

Scaling Effects on MODIS Band Ratio of 645 to 859 nm Aggregated from 250 m to 1 km Resolution: A Case Study in Yellow River Estuary

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

In this study, a theoretical model for studying the scaling effects on the two-band ratio of red to near-infrared band (TBRRN) is suggested. The model is used to explain the relationship between scaling error and local scale error; the results revealed that a special scale scaling procedure can be divided into a series smaller scale scaling procedures, and the total scaling error is the sum of the scaling error of these series’ smaller scale scaling procedure. Consequently, under the condition that the local scale is adequately fine, the total scale error at the target scale may be estimated accurately. In order to understand the mechanisms associated with scale in practical remote sensing, TBRRN data with 250 m and 1 km resolution is estimated from MODIS data at 645 and 859 nm, retrieved on September 1, 2009, in the Yellow River estuary, China. It is found that the TBRRN estimated from the 1 km resolution MODIS data is ~2.94 % smaller than as estimated from the 250 m MODIS data. The large scaling error distributes neither in the turbid waters, nor in the low suspended sediment regions, but instead in the high-low suspended sediment concentration transitional zone, which may be attributed to the spatial variable of suspended sediment in the transitional zone. This paper also points out that, owing to the importance of total scale error in achieving NASA’s mission in oceanic remote sensing, the way in which to conveniently and precisely estimate the total scale error of remote sensing parameters may potentially be an important topic in the field of oceanic remote sensing, both in present research and in the future.

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
Fig. 6

Similar content being viewed by others

References

  • Chen, J., Cui, T. W., & Lin, C. S. (2013a). An improved SWIR atmospheric correction model: a direction-based model. IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2013.2278340.

    Google Scholar 

  • Chen, J., Cui, T. W., Tang, J. W., & Song, Q. J. (2014). Remote sensing of diffuse attenuation coefficient using MODIS imageries from turbid coastal waters: a case study in the Bohai Sea. Remote Sensing of Environment, 140, 78–93.

    Article  Google Scholar 

  • Chen, J., D’Sa, E., Cui, T. W., & Zhang, X. H. (2013b). A semi-analytical total suspended retrieval model in turbid coastal waters: a case study in changjiang river estuary. Optics Express, 21(11), 13018–13031.

    Article  Google Scholar 

  • Chen, J., Lu, K., & Fu, J. (2011a). Theoretical model for estimating the scaling error of the two-band ratio of red to near-infrared in inhomogeneous pxiels: simulation using a moving window. Journal of Selected Topics in Earth Observation and Remote Sensing, 4(4), 877–881.

    Article  Google Scholar 

  • Chen, J., Sheng, H., & Sun, J. H. (2013c). An empirical algorithm for hyperspectral remote sensing of chlorophyll-a in turbid waters: a case study on Hyperion sensor. Sensor Letter, 11, 623–631.

    Article  Google Scholar 

  • Chen, J., Wang, B. J., & Gao, X. J. (2012). Scale corection of two-band ratio of red to near-infrared using imagery histogram approach: a case study on indian remote sensing satellite in yellow river estuary. IEEE Journal of Selected Topic on Applied Earth Observation and Remote Sensing, 5(2), 663–668.

    Article  Google Scholar 

  • Chen, J., Wang, W. C., Wang, B. J., & Wen, Z. H. (2010). Estimating the distribution variance of suspended sediment concentration and scaling effect correction based on the eight neighborhood algorithm. Journal of Infrared and Millmeter Waves, 29(6), 440–444.

    Google Scholar 

  • Chen, J., Wen, Z. H., Fu, J., Sun, J. H., Wang, B. J., & Gao, X. J. (2011b). Principle and application of water quality remote sensing (1st ed.). Beijing: Oceanic Publication.

    Google Scholar 

  • Chen, J., Yi, C. L., & Wen, Z. H. (2013d). Systematic underestimation of MODIS global chlorophyll-a concentration estimation algorithm associating with scale effect. IEEE Sensor Journal, 13(5), 1656–1661.

    Article  Google Scholar 

  • Chen, J., Zhang, M. W., Cui, T. W., & Wen, Z. H. (2013e). A review of some important technical problems in respect of satellite remote sensing of chlorophyll-a concentration in coastal waters. IEEE Journal of Selected Topic on Applied Earth Observation and Remote Sensing, 6(5), 2275–2289.

    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, 176–187.

    Article  Google Scholar 

  • Gertner, G., Wang, G., Anderson, A. B., & Howard, H. (2007). Combining stratification and up-scaling method-block cokriging with remote sensing imagery for sampling and mapping an erosion cover factor. Ecological Informatics, 2, 373–386.

    Article  Google Scholar 

  • Hu, C. M., & Carder, K. L. (2002). Atmospheric correction for airborne sensors: comment on a scheme used for CASI. Remote Sensing of Environment, 79, 134–137.

    Article  Google Scholar 

  • Li, Z. L., & Becker, F. (1993). Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sensing of Environment, 43, 67–85.

    Article  Google Scholar 

  • Li, X. W., Wang, J. D., & Straler, A. H. (1999). The applied scale effecting of Plank’s law on nonhomogeneity of blackbody. Chinese Science, E Series, 44(15), 422–426.

    Google Scholar 

  • Li, D. R., & Yuan, X. X. (2002). Error processing and relibillity theory. Wuhan: Wuhan University Press. 1 editor.

    Google Scholar 

  • Milliman, J. D., & Meade, R. H. (1983). World-wide delivery of river sediment to the oceans. Journal of Geology, 91, 1–21.

    Article  Google Scholar 

  • Mobley, C. D., Gentili, B., Gordon, H. R., Jin, Z. H., Kattawar, G. W., Morel, A., et al. (1993). Comparison of numerical models for computing underwater light fields. Applied Optics, 32(36), 7484–7504.

    Article  Google Scholar 

  • Moore, T. S., Campbell, J. W., & Dowell, M. D. (2009). A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sensing of Environment, 113, 2424–2430.

    Article  Google Scholar 

  • Nishihama, M., Wolfe, R., Solomon, D., Patt, F., Blanchette, J., Fleig, A., et al. (1998). MODIS level 1A earth location: Algorithm theoretical basis document version. NASA Technic Document, Version 3.0 SDST-092.

  • Rogerson, P. A. (2001). Statistical methods for geography. London: Great Britain Publishing house.

    Google Scholar 

  • Shi, W., & Wang, M. (2009). An assessment of the black ocean pixel assumption for MODIS SWIR bands. Remote Sensing of Environment, 113, 1587–1597.

    Article  Google Scholar 

  • Su, L. H., Li, X. W., & Liang, X. L. (2003). Simulation of scaling effects of thermal emission from non-isothermal pixels with the typical three-dimensional structure. International Journal of Remote Sensing, 24(19), 3743–3753.

    Google Scholar 

  • Tassan, S., & Ferrari, G. M. (2003). Variability of light absorption by aquatic particles in the near-infrared spectral region. Applied Optics, 42, 4802–4810.

    Article  Google Scholar 

  • Wang, M. H., Son, S. H., & Shi, W. (2009). Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data. Remote Sensing of Environment, 113, 635–644.

    Article  Google Scholar 

  • Woodcock, C. E., & Strahler, A. H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment, 22, 311–332.

    Article  Google Scholar 

  • Wu, H., Li, Z.L. (2009). Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors 9.

Download references

Acknowledgments

We would like to just express our gratitude to two anonymous reviewers for their useful comments and suggestions.

Author information

Authors and Affiliations

  1. Shanxi Agriculture Remote Sensing Information Center, Xian, 710000, China

    Wenting Quan

Authors
  1. Wenting Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenting Quan.

About this article

Cite this article

Quan, W. Scaling Effects on MODIS Band Ratio of 645 to 859 nm Aggregated from 250 m to 1 km Resolution: A Case Study in Yellow River Estuary. J Indian Soc Remote Sens 42, 495–503 (2014). https://doi.org/10.1007/s12524-013-0356-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-013-0356-2

Keywords

Search

Navigation