The deconvolution algorithm implemented throughout this chapter has the potential to autonomously and efficiently remove instrument effects from optical deconvolution problems. The removal of instrumental spatial distortion is greatly beneficial to the quality of satellite imagery because it prevents errors from being propagated and magnified by downstream processing. This process improves all MODIS ocean colour data products which are commonly used in many scientific fields.
The Multiscale Entropy deconvolution algorithm provides advantages to both terrestrial and marine environments where spatial contrast is observed. Coastal water measurements that are compromised by bright land masses receive particular improvement. This is crucial to the sustainable management of water regions in densely populated coastal zones. Open-ocean measurements containing high- radiance clouds that contrast with dark ocean also receive vast improvements in data measurement quality. This can reduce the quantity of ocean measurements that are removed due to cloud contamination and improves the spatial coverage of satellite data products.
The primary research contributions in this chapter include: a multi-detector convolution process which is fundamental to the operation of the deconvolution algorithm; a detector saturated radiometric correction method that allows MODIS measurements beyond the dynamic range of the detectors to be estimated and more accurately contribute to the deconvolution; and the development of a combination of efficient computational methods that culminate in an accurate and robust deconvolution algorithm. These components are unique and critical to the successful deconvolution of MODIS Aqua imagery.
Validation
5.1
Introduction
Remote sensing data is a useful resource for the management of coastal and inland water bodies. Satellite imagery combined with physical and biological models can be used to forecast harmful algal blooms, track sediment transport and monitor general water quality (Stumpf and Tomlinson, 2005; Ruhl and Schoellhamer, 2001; Brando and Dekker, 2003). Many climate studies use remote sensing satellite products and rely on this information to be highly accurate (Behrenfeld et al., 2006; Higurashi and Nakajima, 2000; Achard et al., 2002; Kaufman et al., 2002). Remote sensing ocean colour products are becoming increasingly important for the management of water quality in coastal zones (Platt et al., 2008). As such, it is vital to quantify the accuracies and uncertainties of remote sensing products so that their use in marine research can be made with confidence. This is often achieved by comparing remote sensing products with in-situ validation measurements and, in the case of calibration, incorporating correction factors into the data processing stream.
Satellite instruments measure top-of-atmosphere radiance that comprise a combination of natural physical processes. For passive satellite sensors, the signal recorded at satellite height is the reflection of sunlight off the surface of the Earth. Light recorded at the satellite sensor is affected by atmospheric, oceanic and coupled atmosphere-ocean processes. Coastal marine measurements can additionally contain bathymetric effects where reflections occur at the surface and off the shallow
seabed. It is important to note that only approximately 10% of the recorded top- of-atmosphere signal corresponds to the water signal, with the remaining 90% being attributed to atmospheric effects (Yang and Gordon, 1997). To derive marine products, it is necessary to remove the atmospheric component of the signal using a process called atmospheric correction (Kaufman, 1989; Vermote et al., 2002; Wang, 2010). In fact, the production of ocean colour and other water-based products implicitly refers to the application of atmospheric correction methods. After atmospheric correction is applied, the remaining signal is further processed to account for variables such as solar zenith angle, Earth-Sun distance, sensor waveband response and a range of sensor transmittance processes. Once this processing is complete, one of the most important output products for marine purposes is the spectral remote sensing reflectance,Rrs(λ), that describes the fraction of the incident
light that is reflected from the surface of the water under consistent lighting conditions. Remote sensing reflectance is a primary data product used to derive many higher-level marine products including chlorophyll-a concentration, Total Suspended Matter (TSM) (also known as suspended solids or suspended particulate matter) and the spectral diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd(490)). These products are readily used in marine research and will be the focus
of the in-situ validation efforts in this chapter.
The optical properties of open-ocean waters are reasonably well understood and have attracted a significant amount of research (Gordon and Clark, 1980; Dekker, 1993; Lee et al., 1999). However, optically complex coastal waters provide increased signal processing difficulty due to shallow water processes and higher concentrations of in-water constituents including algal blooms, Coloured Dissolved Organic Matter (CDOM) (also known as yellow substance, or gelbstoff), suspended particulate matter, coccolithophores, detritus, and bacteria (Sathyendranath, 2000). Water is typically classified into two optical cases (Morel and Prieur, 1977; Mobley et al., 2004). Case 1 waters are predominantly affected by phytoplankton and its subsequent by-products. Open-ocean environments are generally classified as case 1 waters and behave predictably from a remote sensing standpoint. Case 2 waters contain much larger quantities of TSM and CDOM which significantly
increase their optical complexity. Many coastal zones are classified as optical case 2 waters because they often contain higher concentrations of suspended particulate matter from terrestrial run-off. This classification system is commonly used for computational modelling. Predictive bio-optical models can estimate the inherent optical properties of case 1 water such as absorption, scattering and backscattering coefficients, as well as the apparent optical properties which include reflectance and the diffuse attenuation coefficient. Chlorophyll-based bio-optical models also use this classification scheme and specialise at estimating chlorophyll concentrations from satellite measurements of ocean colour.
Instrumental distortion, which is also referred to as adjacency effects or stray light from land, occurs in high-contrast scenes where bright land or clouds contaminate the water signal. This source of contamination is an important factor for water-based measurements in coastal regions. In this chapter, instrumental distortion effects are removed from MODIS Aqua imagery using Multiscale Entropy deconvolution (Chapter 4), and the results are compared with accurate in-situ measurements. A comparison of both the original (convolved) and deconvolved MODIS products against the in-situ measurements will allow for any improvement to be quantified. Further deconvolution validation is investigated by comparing MODIS Aqua imagery with a high-resolution QuickBird image containing an Antarctica ice edge. This is a good example of a high-contrast satellite image that should substantially benefit from deconvolution.
The first area of investigation is the north-west Baltic Sea which contains unique brackish waters with low salinity due to restricted salt input water from the North Sea. It is estimated that a significant quantity of freshwater (upwards of 450km3)
contributes annually to the Baltic Sea from surrounding catchment areas (Ryd´en and Karlsson, 2012). The primary circulation driver is surface wind that inputs Ekman transport forces into the waterbody (Jansson, 2003). It is common for cyanobacterial blooms to become extensive in the Baltic Sea throughout the summer period. The atmospheric conditions over the Baltic Sea are typically relatively clear (Carlund et al., 2005). The Baltic Sea has particularly high CDOM concentrations which result in high absorption of short wavelengths ranging from ultraviolet to green.
Additionally, red wavelengths are heavily absorbed by water. This leads to less reflectance and a decrease in the signal-to-noise ratio in these parts of the spectrum. For this reason, accurate atmospheric correction is vital to produce good ocean colour products for the Baltic Sea region. The Baltic Sea is generally classified as optical case 2 waters due to its high CDOM content (Kratzer et al., 2003, 2008; Kratzer and Tett, 2009). This further increases the difficulty of deriving accurate marine products in this region because of the complex interaction of shallow coastal waters and high concentrations of particulate matter.