From Meters to Kilometers: A Look at Ocean-Color Scales of Variability, Spatial Coherence, and the Need for Fine-Scale Remote Sensing in Coastal Ocean Optics Public Deposited

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  • The physical, biological, chemical, and optical processes of the ocean operate on a wide variety of spatial and temporal scales, from seconds to decades and from micrometers to thousands of kilometers (Dickey et al., this issue; Dickey, 1991). These processes drive the accumulation and loss of living and nonliving mass constituents in the water column (e.g., nutrients, phytoplankton, detritus, sediments). These mass constituents frequently have unique optical characteristics that alter the clarity and color of the water column (e.g., Preisendorfer, 1976). This alteration of the ocean color, or more specifically the change in the spectral “water-leaving radiance,” L𝓌(λ), has led to the development of optical techniques to sample and study the change in biological and chemical constituents (Schofield et al., this issue). Thus, these optical techniques provide a mechanism to study the effects of underlying biogeochemical processes. In addition, because time- and space-dependent changes in L𝓌(λ) may be measured remotely, optical oceanography provides a way to sample ecological interactions over a wide range of spatial and temporal scales. The question often posed by scientists trying to resolve problems involving the temporal and spatial variation of oceanic properties is: “What is the optimal time/ space sampling frequency?” The obvious answer is that the sampling frequency should be one half the frequency of the variation (i.e., Nyquist frequency) of the property of interest. However, therein lies the rub for the oceanographer: the range of the relevant scales is large, and the range of available resources and/or actual engineering capabilities to sample all relevant scales is often small. Hence, the decisions affecting resource allocation become critical in order to maximize the total data information in both quantity and quality. While these scientific resource decisions are rarely discussed in explicit terms of cost-benefit analysis, such discussions should be integral parts of the scientific design of instruments, platforms, and experiments aimed at resolving oceanic processes. The practical examples of this problem in remote sensing include: “What is the optimal repeat coverage frequency?” and “What is the optimal Ground Sample Distance (GSD) or pixel size of the data?” For the optical oceanographer, there is also the issue of optimal spectral coverage needed to resolve the optical constituents of interest (Chang et al., this issue). The sum of these considerations feed into the sensor, deployment platform, and deployment schedule decisions. For polarorbiting and geo-stationary satellites that cost hundreds of millions of dollars, as well as airborne sensors that have smaller upfront costs but higher deployment costs, the decision of sampling frequency directly impacts the scientific use of the data stream, and what processes may be addressed with data streams collected by these sensors. These scientific cost-benefit analyses extend beyond the cost in dollars because the typical lifetime and replacement cycle of these sensors is on the order of years to decades, and a poorly designed sensor package is very difficult to replace. In 2001, the Office of Naval Research (ONR) sponsored the Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) (Dickey et al., this issue), which presented the opportunity to study the question of scales of variability in remote-sensing data. Hyperspectral airborne sensors were deployed on several platforms at various altitudes. This coverage was supplemented by numerous space-borne, remote-sensing satellites. The airborne instruments included two versions of the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS 1 and PHILLS 2) (Davis et al., 2002) operating at an altitude of less than 10,000 feet and 30,000 feet, respectively, as well as the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor operating at 60,000 feet. These sensors provided hyperspectral data at 2 m, 9 m, and 20 m GSDs, respectively. The satellite data collected included the multi-spectral images from Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Fengyun 1 C (FY1-C), Oceansat as well as the multispectral polarimeter Multiangle Imaging SpectroRadiometer (MISR) sensor and sea surface temperature (SST) sensor Advanced Very High Resolution Radiometer (AVHRR). These collections provided a wealth of remote- sensing and field data during a spatially and temporally intense oceanographic field campaign, and they offered the ability to begin to address the issue of optimal sampling scales for the coastal ocean. The use of these multiple remote-sensing data streams requires the calibration, validation, and atmospheric correction of the sensor signals to retrieve estimates of L𝓌(λ), or “remote sensing reflectance,” Rᵣₛ(λ), a normalized measure of the L𝓌(λ). Our goals in this paper are to illuminate some of the issues of remote sensing spatial scaling in the nearshore environment and attempt to derive some understanding of appropriate sampling scales in the nearshore environment. We will focus on the data collected by a single sensor (PHILLS 2) to reduce uncertainties in the analysis that may result from the different data processing techniques applied to each of the individual sensors’ data.
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  • Bissett, W. P., Arnone, R. A., Davis, C. O., Dickey, T. D., Dye, D., Kohler, D. D. R., & Gould Jr., R. W. (2004, June). From Meters to Kilometers: A Look at Ocean-Color Scales of Variability, Spatial Coherence, and the Need for Fine-Scale Remote Sensing in Coastal Ocean Optics. Oceanography, 17(2), 32-43. doi:10.5670/oceanog.2004.45
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  • 17
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  • 2
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  • This work was supported by the Offi ce ofNaval Research.
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  • description.provenance : Submitted by Deborah Campbell ( on 2013-03-01T17:15:15ZNo. of bitstreams: 1DavisCurtissOEarthOceanAtmosphericSciencesFrom MetersKilometers.pdf: 989747 bytes, checksum: 14b38fe8e4f6ae6510176a196902ea0c (MD5)
  • description.provenance : Made available in DSpace on 2013-03-01T17:15:15Z (GMT). No. of bitstreams: 1DavisCurtissOEarthOceanAtmosphericSciencesFrom MetersKilometers.pdf: 989747 bytes, checksum: 14b38fe8e4f6ae6510176a196902ea0c (MD5) Previous issue date: 2004-06



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