The Geostationary Ocean Color imager (GOCI) is the first geostationary ocean color satellite sensor that collects hourly images eight times per day during daylight. This high frequency image acquisition makes it possible to study more detailed dynamics of red tide blooms, sediment plumes, and colored dissolved organic matter plumes, and can aid in the prediction of biophysical phenomena. We apply the red band difference and the fluorescence line height algorithms to GOCI imagery to separate waters with high algal and nonalgal particles and validate the results with the MODIS imagery. We also track optical features using hourly GOCI imagery and assess their movement through comparisons with predicted ocean currents derived from the navy coastal ocean model and tidal data.
Standard oceanographic processing of the visible infrared imaging radiometer suite (VIIRS) and the moderate resolution imaging spectroradiometer (MODIS) data uses established atmospheric correction approaches to generate normalized water-leaving radiances (nLw) and bio-optical products. In many cases, there are minimal differences between temporally and spatially coincident MODIS and VIIRS bio-optical products. However, due to factors such as atmospheric effects, sensor, and solar geometry differences, there are cases where the sensors’ derived products do not compare favorably. When these cases occur, selected nLw values from one sensor can be used to vicariously calibrate the other sensor. Coincident VIIRS and MODIS scenes were used to test this cross-sensor calibration method. The VIIRS sensor was selected as the “base” sensor providing “synthetic” in situnLw data for vicarious calibration, which computed new sensor gain factors used to reprocess the coincident MODIS scene. This reduced the differences between the VIIRS and MODIS bio-optical measurement. Chlorophyll products from standard and cross-sensor calibrated MODIS scenes were fused with the VIIRS chlorophyll product to demonstrate the ability for this cross-sensor calibration and product fusion method to remove atmospheric and cloud features. This cross-sensor calibration method can be extended to other current and future sensors.
Clouds cause a serious problem for optical satellite sensors. Clouds not only conceal the ground, they also cast shadows, which cause either a reduction or total loss of information in an image, by reducing the illumination falling on the shadowed pixels. Ocean color bio-optical inversion algorithms rely on measurements of remote sensing reflectance (Rrs (λ )) at each pixel. If shadows are not removed properly across a scene, erroneous Rrs (λ) values will be calculated for the shadowed pixels, leading to incorrect retrievals of ocean color products such as chlorophyll. The cloud shadow issue becomes significant especially for high-resolution sensors such as the Hyperspectral Imager for the Coastal Ocean (HICO). On the other hand, the contrast of pixels in and outside a shadow provides opportunities to remove atmospheric contributions for ocean color remote sensing. Although identifying cloud is relatively straightforward using simple brightness thresholds, identifying their shadows especially over water is quite challenging because the brightness of the shadows is very close to the brightness of neighboring sunny regions especially in deep waters. In this study, we present automated procedures for our recently proposed cloud shadow detection technique called the Cloud Shadow Algorithm (CSA) and Lee et al. (2007) cloud and shadow atmospheric correction algorithm. We apply both automated procedures to HICO imagery and show examples of the results.
The detection and monitoring of harmful algal blooms using in-situ field measurements is both labor intensive and is
practically limited on achievable temporal and spatial resolutions, since field measurements are typically carried out at a
series of discrete points and at discrete times, with practical limitations on temporal continuity. The planning and
preparation of remedial measures to reduce health risks, etc., requires detection approaches which can effectively cover
larger areas with contiguous spatial resolutions, and at the same time offer a more comprehensive and contemporaneous
snapshot of entire blooms as they occur. This is beyond capabilities of in-situ measurements and it is in this context that
satellite Ocean Color sensors offer potential advantages for bloom detection and monitoring. In this paper we examine
the applications and limitations of an approach we have recently developed for the detection of K. brevis blooms from
satellite Ocean Color Sensors measurements, the Red Band Difference Technique, and compare it to other detection
algorithm approaches, including a new statistical based approach also proposed here. To achieve more uniform standards
of comparisons, the performance of different techniques for detection are applied to the same specific verified blooms
occurring off the West Florida Shelf (WFS) that have been verified by in-situ measurements.
Harmful Algal Blooms (HABs) can lead to severe economical and ecological impacts particularly in the coastal areas
and can threaten human and marine health. About three-quarter of these toxic blooms are caused by dinoflagellates
species which are well known to migrate vertically. During the day, they migrate up to the surface for photosynthesis,
and consequently, their dense aggregations produce strong bio-optical signals that are detectable by space borne optical
satellite sensors. In this study we use our recently developed low backscattering bloom detection technique, the Red
Band Difference (RBD), to detect various dinoflagellates blooms using both MODIS (Moderate Resolution Imaging
Spectroradiometer) and MERIS (Medium Resolution Imaging Spectrometer) data and present the results which confirm
the potentials of the RBD technique. Here we present examples of bloom detection in waters off Gulf of Mexico,
Monterey Bay, South Africa, and East China Sea.
With the increasing recognition of the need for using the NIR bands for chlorophyll retrieval in coastal waters it is necessary to account not only for the spectral modulation of the total elastic backscatter by the chlorophyll absorption spectra, as it is normally done, but to also take into account the spectral signature of the backscatter itself, whether from mineral or organic particulates, including algae, and to assess how these factors effect retrieval algorithms. Based on our recent field measurements in coastal waters, we have undertaken a study to examine the spectral behavior of the backscatter to total scattering ratio as a function of suspended solids and chlorophyll loadings. The total scattering spectra is obtained using the WET Labs AC-S instrument which provides hyperspectral measurements of absorption and attenuation, in conjunction with the bb9 instrument which provides direct measurement of backscatter, as well fluorescence measurement of chlorophyll concentration [Chl]. The relevant WET Labs absorption and attenuation data were then used as input into Hydrolight radiative transfer simulations to obtain the backscattering ratio spectral distributions. Preliminary NIR algorithms, which were evolved for high [Chl] coastal waters and which focus on the contribution of spectral changes due to chlorophyll backscattering in the NIR, are presented. It is expected that these algorithms will ultimately prove to be less dependent on regional tuning.
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