Global Imager (GLI) is the visible to infrared imager aboard ADEOS-II satellite with 30 and 6 channels for 1 km and 250m resolutions, respectively. The sensor was successfully captured the first image on January 25, 2003. Sea surface temperature (SST) will be retrieved in combination with simultaneous SST observation by low-resolution microwave sensor, AMSR-E. Distribution of chlorophyll and other constituents will be obtained from ocean color channels. Frequent observations with 250 m visible channels will be also available, and combination with 1 km ocean color and SST will be useful for coastal applications. Early scientific results of GLI ocean group will be presented in this presentation.
The paper presents initial results of atmospherically corrected ocean color data from the Global Imager (GLI), a moderate resolution spectrometer launched in December 2002 aboard ADEOS-II satellite. The standard GLI atmospheric correction algorithm, which includes an iterative procedure based on in-water optical modeling is first described, followed by brief description of standard in-water algorithms for output geophysical parameters. Ship/buoy-observed and satellite-derived marine reflectances, or normalized water-leaving radiance, are then compared, under vicarious calibration correction factors based on global GLI-SeaWiFS data comparison. The results, over 15 water-leaving radiance match-up data collected mostly off California and off Baja California, show standard errors in GLI estimate of 0.1 to 0.36 μW/cm2/nm/sr for 412, 443, 490, and 565 nm bands, with improved standard errors of 0.09 to 0.14 μW/cm2/nm/sr if in situ data set is limited to those obtained by in-water radiance measurement. Under provisional de-striping procedure, satellite-derived chlorophyll a estimates compares well with 35 ship-measured data collected off California within one day difference from the satellite observation, showing standard error factor of 1.73 (+73% or -43% error).
Two types of algorithm selected for ADEOS-II/GLI atmospheric correction for ocean applications were summarized. The standard algorithm, OTSK1a, is an extension from the OCTS algorithm (and also similar to current SeaWiFS/MODIS algorithm). The research algorithm, OTSK1b, is selected to validate OTSK1a. OTSK1b is a new approach for atmospheric correction by using multi-layered perceptrons (i.e. neural network) to model the transfer function between top-of- atmosphere GLI reflectances and above-surface marine reflectances. The performance of these two algorithms was tested with GLI synthetic dataset and MODIS data.
KEYWORDS: Raman spectroscopy, LIDAR, Polymers, Pulsed laser operation, Water, Backscatter, Raman scattering, Data acquisition, Remote sensing, Signal to noise ratio
A Nd:YAG Raman lidar system is here used to derive the temperature profile of sea water with varying depth by extracting it from the Raman spectrum. The study was conducted both in the laboratory and in situ; the temperature accuracy thus achieved is 0.4 C rms, and the depth-resolution is +/- 1.5 m. Attention is given to the theory of subsurface water temperature detection and the configuration of the detector hardware employed
Temperature is a very important parameter in physic oceanography research and ocean forecast. The ocean scientific and technical community has been laying extreme stress on fast measuring sea temperature distribution over large areas. Recently, remote sensing of sea surface temperature measurement with an infrared channel of NOAA satellite became an advanced technical means. In China, a group at Ocean University of Qingdao has obtained excellent successful results in inversing sea surface temperature from satellite data. But, it is difficult to obtain sea temperature vertical distribution with fast measurement over large area from satellite remote sensing. The data of sea temperature vertical distribution is very important for the research of sea-air interaction and ocean frontier behavior. Recently, the method of measuring sea temperature profile with lidar techniques has made some progress. Laser Raman spectrum is a good method. Therefore, this paper mainly reports the results of finely measuring laser Raman spectrum of different temperature sea water in lab, and the data processing method of obtaining temperature parameter from Raman spectrum. The experiment proves that the temperature precision of the data processing method reaches +/- 0.30 degree(s)C.
In this paper, a Nd:YAG Lidar system (YLS) is described. The backscattering problem of sea water is surveyed in the China Sea. A concurrent Raman scattering signal is used as a calibrating signal to reduce the influence of the variable random sea surface. The calibrated results show that this method is feasible and this experimental system can be used in a survey of scattering characteristics in the China Sea.
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