Optical remote sensing

Definition of Passive optical remote sensing: Passive optical sensors detect natural energy (radiation) that is emitted or reflected by the object or scene being observed. Reflected sunlight is the source of radiation measured by passive optical sensors. [1]. This is the common definition for Passive optical remote sensing, other definitions can be discussed in the article

Remote sensing using satellite and airborne sensors is a powerful, often-preferred tool for monitoring coastal zones, as these detection instruments can provide accurate, large scale, synoptic environmental data essential for understanding and managing marine ecosystems. Optical multi- or hyperspectral sensors enable the detection of in-water properties, such as suspended matter and phytoplankton concentration, and provide information on benthic substrate type, vegetation composition, and bathymetry in optically-shallow waters (see “Applications”).

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Principles

Optical remote sensing using passive satellite or airborne sensors is the spatially resolved detection and utilization of sunlight which has been transmitted through the atmosphere and reflected back from the earth’s surface to the sensor.

In marine and aquatic environments, the sunlight spectrum is modified on its way through the atmosphere, water surface, and water body. Materials in each of these boundary layers absorbs, scatters, and reflects the different light wavelengths in a specific manner. As a result, the reflected light carries spectral information about the composition of these substances.

Sensors

This spectral information is detected differently, in terms of accuracy, for temporal, spatial, spectral, and radiometric resolution, and is characteristic for each different sensor. Multi- or hyperspectral detectors on space or airborne platforms may be chosen depending upon specific requirements, such as cost, area size, or availability.

Data processing

Different remote sensing data processing methods are available, and each evaluates or interprets the spectral information in a slightly different manner. To best retrieve the content information with regards to space and time, the output from a number of different algorithms can be compared against ground-based observational data.

Physics-based retrieval algorithms

Physics-based retrieval algorithms can generally be applied world wide and are very flexible under all conditions covered by the optical models upon which they were based. Once set up, they do not rely on manual adaptations in order to generate map products and are frequently completely independent of input from ground truth measurements.

Empirical algorithms

Empirical algorithms are powerful analytical tools and are often able to exploit the most from remote sensing data. They usually rely on accompanied ground truth measurements and thus are typically not transferable between different types of aquatic and marine ecosystems.

Generic processing systems

Generic processing systems cover a wide range of applications and frequently can be applied to new sites, applications, and sensors due to a systematic, modular approach and easy adaptations for sensor- and site-specific properties.

Geo-rectification

Geo-rectification procedures for imaging data are applied in order to connect the spatial resolved pixel values with geographical coordinates and to deliver geo-coded maps, which can be implemented into Geographical Information Systems GIS. The trigonometric principles and procedures for operational geo-referencing are essentially developed and operational so far. Therefore, sub-pixel accuracies can be achieved for the condition that sufficient navigation data are available. However, in practice the attainable spatial accuracy of operational geo-coded products depends on the accuracy of the satellites or airborne navigation or metadata. Therefore, for high-precision tasks many satellite or airborne imageries have to be spatially refined using manual geo-rectification approaches or automatic matching algorithms.

Restrictions

Restrictions apply, wherever the object specific signals are masked by others or interfered in ambiguous way with regard to both the sensor resolution and the retrieval methods. Therefore, only few restrictions can be stated in general, but many restrictions can be determined only with regard to the specific site to be observed, the sensors used and algorithms applied.

Examples for general restrictions

General restrictions are the masking of clouds for optical signals reflected from the earth surface or geometric recording conditions that effect strong sun glitter contributions to the signal from the water surface.

Examples of case-dependent restrictions

a) Intermediate sun glitter conditions can be treated with only if the radiometric, spectral (and in dependency on the algorithm also spatial) resolution of the sensor is sufficient and the processing approach supports such a correction or consideration of this effect (> example).

b) The spatial resolution of the sensor in most cases must better than the spatial heterogeneity of the target areas to be mapped. Subpixel classification of surfaces is only possible for a very restricted number of spectral endmembers. At least, every contribution of members not implemented into the model will create errors.

c) The spectral behaviour of optical classes must significant differ to each other with respect to the spectral resolution of the sensor. E.g. Chlorophyll and Gelbstoff can not be estimated independently, if the sensor spectral and radiometrical resolution is unsufficient and/or the aquatic system specific absorption spectra are to simular for an independend quantitative estimation.