Kevin Ruddick

Bio: Kevin Ruddick is an academic researcher from Royal Belgian Institute of Natural Sciences. The author has contributed to research in topics: Atmospheric correction & Colored dissolved organic matter. The author has an hindex of 33, co-authored 126 publications receiving 3832 citations.

Seaborne measurements of near infrared water-leaving reflectance: The similarity spectrum for turbid waters

Abstract: Theory and seaborne measurements are presented for the near infrared (NIR: 700‐900 nm) water-leaving reflectance in turbid waters. According to theory, the shape of the NIR spectrum is determined largely by pure water absorption and is thus almost invariant. A ‘‘similarity’’ NIR reflectance spectrum is defined by normalization at 780 nm. This spectrum is calculated from seaborne reflectance measurements and is compared with that derived from laboratory water absorption measurements. Factors influencing the shape of the similarity spectrum are analyzed theoretically and by radiative transfer simulations. These simulations show that the similarity spectrum is valid for waters ranging from moderately turbid (e.g., water-leaving reflectance at 780 nm of order 10 24 or total suspended matter concentration of order 0.3 g m 23 ) to extremely turbid (e.g., reflectance at 780 nm of order 10 21 ). Measurement uncertainties are analyzed, and the air-sea interface correction is shown to be critical for low reflectances. Applications of the NIR similarity spectrum to atmospheric correction of ocean color data and to the quality control of seaborne, airborne, and spaceborne reflectance measurements in turbid waters are outlined.

Sensor capability and atmospheric correction in ocean colour remote sensing

Abstract: Accurate correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review

Abstract: Forthcoming superspectral satellite missions dedicated to land monitoring, as well as planned imaging spectrometers, will unleash an unprecedented data stream. The processing requirements for such large data streams involve processing techniques enabling the spatio-temporally explicit quantification of vegetation properties. Typically retrieval must be accurate, robust and fast. Hence, there is a strict requirement to identify next-generation bio-geophysical variable retrieval algorithms which can be molded into an operational processing chain. This paper offers a review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using optical remote sensing imagery. We can categorize these methods into (1) parametric regression, (2) non-parametric regression, (3) physically-based and (4) hybrid methods. Hybrid methods combine generic capabilities of physically-based methods with flexible and computationally efficient methods, typically non-parametric regression methods. A review of the theoretical basis of all these methods is given first and followed by published applications. This paper focusses on: (1) retrievability of bio-geophysical variables, (2) ability to generate multiple outputs, (3) possibilities for model transparency description, (4) mapping speed, and (5) possibilities for uncertainty retrieval. Finally, the prospects of implementing these methods into future processing chains for operational retrieval of vegetation properties are presented and discussed.

Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water

Abstract: The pigment phycocyanin (PC) is a marker for cyanobacterial presence in eutrophic inland water. We present a reflectance band‐ratio algorithm for retrieval of cyanobacterial PC. The model conforms to the band settings of the Medium Resolution Imaging Spectrometer. The parameters of the algorithm were optimized using reflectance and absorption data from two highly eutrophic lakes. Using measured specific absorption coefficients for PC [ a (620)] * for every sample, the error in the predicted PC concentrations was 19.7% (r 2 5 0.94, n 5 34) for measured PC concentrations up to 80 mg m 23 . Applying a fixed value of a (620) caused an overestimation of the PC content * that increased toward lower PC concentrations. The PC prediction best matched observed values during periods of high relative abundance of cyanobacteria in the plankton community. The results suggest strong seasonal variation in a (620). The presence of pigments other than PC and chlorophyll a (Chl a) and a variable influence of Chl a * on retrieved absorption at 620 nm are potential causes of errors in PC retrieval. The algorithm in its current form is considered to be suitable for detection of the PC concentration in turbid, cyanobacteria-dominated waters. Eutrophic inland waters often exhibit blooms of cyanobacteria. Notorious for their negative impact on water quality, cyanobacterial blooms have been increasingly subject of water management and scientific studies. The hazards of toxic cyanobacterial blooms call for frequent and rapid monitoring of water bodies. Remote sensing provides insights into the distribution of blooms for a large number of lakes or reservoirs simultaneously. The concentration of chlorophyll a (Chl a) as a general indicator for plankton biomass can be assessed using imagery from a wide range of air- and spaceborn sensors (Vos et al. 2003). Recent advances in spaceborn remote sensing technology broaden the perspectives of monitoring toward the identification and quantification of plankton groups. Algorithms for the retrieval of Chl a from turbid water reflectance were already being developed (Gons et al. 2002). Now, the retrieval of the pigment phycocyanin (PC), which is characteristic of the presence of cyanobacterial, is being attempted. It is known that the presence of PC can be detected from spectral reflectance (Dekker et al. 1