Optical remote sensing of marine constituents in coastal waters: a feasibility study

Optical remote sensing of ocean color is a well-established technique for inferring ocean properties. However, most retrieval algorithms are based on the assumption that the radiance received by satellite instruments is affected only by the phytoplankton pigment concentration and correlated substances. This assumption works well for open ocean water but becomes questionable for coastal waters. To reduce uncertainties associated with this assumption, we developed a new algorithm for the retrieval of marine constituents in a coastal environment. We assumed that ocean color can be adequately described by a three-component model made up of chlorophyll a, suspended matter, and yellow substance. The simultaneous retrieval of these three marine constituents and of the atmospheric aerosol content was accomplished through an inverse-modeling scheme in which the difference between simulated radiances exiting the atmosphere and radiances measured with a satellite sensor was minimized. Simulated radiances were generated with a comprehensive radiative transfer model that is applicable to the coupled atmosphere-ocean system. The method of simulated annealing was used to minimize the difference between measured and simulated radiances. To evaluate the retrieval algorithm, we used simulated (instead of measured) satellite-received radiances that were generated for specified concentrations of aerosols and marine constituents, and we tested the ability of the algorithm to retrieve assumed concentrations. Our results require experimental validation but show that the retrieval of marine constituents in coastal waters is possible.     

Importance and estimation of aerosol vertical structure in satellite ocean-color remote sensing

The vertical distribution of absorbing aerosols affects the reflectance of the ocean-atmosphere system. The effect, due to the coupling between molecular scattering and aerosol absorption, is important in the visible, especially in the blue, where molecular scattering is effective, and becomes negligible in the near infrared. It increases with increasing Sun and view zenith angles and aerosol optical thickness and with decreasing scattering albedo but is practically independent of wind speed. Relative differences between the top of the atmosphere reflectance simulated with distinct vertical distributions may reach approximately 10% or even 20%, depending on aerosol absorption. In atmospheric correction algorithms, the differences are directly translated into errors on the retrieved water reflectance. These errors may reach values well above the 5x10(-4) requirement in the blue, even for small aerosol optical thickness, preventing accurate retrieval of chlorophyll-a [Chl-a] concentration. Estimating aerosol scale height or altitude from measurements in the oxygen A band, possible with the polarization and directionality of the Earth's reflectance instrument and medium resolution imaging spectrometer, is expected to improve significantly the accuracy of the water reflectance retrievals and yield acceptable [Chl-a] concentration estimates in the presence of absorbing aerosols.     

Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties

Two-band algorithms that use the ratio of reflectances at 672 and 704 nm have already proved successful for chlorophyll a retrieval in a range of coastal and inland waters. An analysis of the effect of reflectance measurement errors on such algorithms is made. It provides important indications of the range of validity of these algorithms and motivates the development of an entirely new type of adaptive two-band algorithm for hyperspectral data, whereby the higher wavelength is chosen for each input spectrum individually. When one selects the wavelength at which reflectance is equal to the reflectance at the red chlorophyll a absorption peak, chlorophyll a retrieval becomes entirely insensitive to spectrally flat reflectance errors, which are typical of imperfect atmospheric correction, and is totally uncoupled from the retrieval or an estimation of backscatter. This new algorithm has been tested for Dutch inland and Belgian coastal waters.     

基于神经网络方法的高光谱遥感浅海水深反演

利用人工神经网络方法进行了高光谱遥感反演浅海水深的初步研究.在产生模拟数据时,为保证模拟数据的合理性,引入了根据水体和海底特性来划分光学浅水和光学深水的方法,并初步研究了利用光谱徽分技术进行光学浅水和光学深水区分的有效性.在人工神经网络建模过程中,采用主成分分析的方法对网络的输入数据进行预处理,显著提高了网络的学习速度.建立的人工神经网络模型和基于非线性最优化方法的反演算法与实测数据的反演结果相比较,人工神经网络模型的反演精度明显高于非线性最优化反演算法.     

Optical remote sensing of surface roughness through the turbulent atmosphere

Using a CO(2), cw, coherent lidar, we have measured the surface roughness of diffuse targets through 1000 m of turbulent atmosphere. The technique measures the phase fluctuations of the speckle field at the receiver and relates the measured phase variance to the surface roughness. Measurements were made with aluminum targets that had been sandblasted with 8-, 16-, and 30-grit material and also with a flame-sprayed aluminum target. It was found that a linear relationship exists between the standard deviation (SD) of the unwrapped phase fluctuations and the SD of the target surface-height fluctuations. Good results were obtained with modest transmitter power and small receiver optics in just a few seconds of averaging time.     

Hyperspectral remote sensing for shallow waters. I. A semianalytical model

For analytical or semianalytical retrieval of shallow-water bathymetry and/or optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance (K(d)), one for the upwelling radiance of the water column (K(u)(C)), and one for the upwelling radiance from bottom reflection (K(u)(B)). Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiative-transfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above (R(rs)) and below (r(rs)) the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical (SA) model for r(rs) of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption (a) and backscattering (b(b)). For remote-sensing inversion, parameters connecting R(rs) and r(rs) are also derived. It is found that r(rs) values determined by the SA model agree well with the exact values computed by Hydrolight (~3% error), even for Hydrolight r(rs) values calculated with different particle phase functions. The Hydrolight calculations included b(b)/a values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions.     

Volcano remote sensing with ground-based spectroscopy

The chemical compositions and emission rates of volcanic gases carry important information about underground magmatic and hydrothermal conditions, with application in eruption forecasting. Volcanic plumes are also studied because of their impacts upon the atmosphere, climate and human health. Remote sensing techniques are being increasingly used in this field because they provide real-time data and can be applied at safe distances from the target, even throughout violent eruptive episodes. However, notwithstanding the many scientific insights into volcanic behaviour already achieved with these approaches, technological limitations have placed firm restrictions upon the utility of the acquired data. For instance, volcanic SO(2) emission rate measurements are typically inaccurate (errors can be greater than 100%) and have poor time resolution (ca once per week). Volcanic gas geochemistry is currently being revolutionized by the recent implementation of a new generation of remote sensing tools, which are overcoming the above limitations and are providing degassing data of unprecedented quality. In this article, I review this field at this exciting point of transition, covering the techniques used and the insights thereby obtained, and I speculate upon the breakthroughs that are now tantalizingly close.     

Optical remote sensing inside an inhomogeneous axisymmetric medium: the absorption field measurement

It is shown that the absorption field inside an inhomogeneous, rotationally symmetric medium with a spatially variable refractive index can be reconstructed by means of a tomographic technique. The classic Abel transform is extended to non-Euclidean optical media. The optical behavior of such a medium is described and, provided that the product of the refractive index with the radial distance is a monotonic function, an exact inverse formula is found. Both a numerical and an analytical test on a phantom function is carried out to prove the exactness of this formula. In contrast, when the assumption of a monotonic function is not true, it is shown that the reconstruction problem becomes subdeterminate because of the presence of annular regions, known as blind areas, inside of which no curved path reaches an extremum. The spatial localization and the size of these regions are related to the extrema of the index of refraction times the radial distance.     

Effect of suspended particulate and dissolved organic matter on remote sensing of coastal and riverine waters

We use remote sensing reflectance (RSR) together with the inherent optical properties of suspended particulates to determine the backscattering ratio b(b)/b for coastal waters. We examine the wavelength dependence of b(b)(lambda) and f(lambda)/Q(lambda) and establish the conditions when C(lambda) in RSR(lambda) approximately or = C(lambda)b(b)(lambda)/a(lambda) can be treated as a constant. We found that for case 2 waters, RSR was insensitive to the natural fluctuations in particle-size distributions. The cross-sectional area of the suspended particulate per unit volume, x(g), showed an excellent correlation with the volume scattering coefficient.     

Introduction to remote sensing

Remote sensing consists of gathering information about objects and features without placing instruments in contact with them. The sensors are placed on aircraft or spacecraft platforms and the earth’s surface surveyed for its natural resources. Electromagnetic radiation (emr) in the visible, infrared and microwave bands are employed, mostly solar radiation or natural emissions. The interactions ofemr with the objects are impressed as “signatures” on the reflected, scattered, transmitted or emittedemr. The sensors employed are (i) cameras with normal or special films sensitive to infrared, (ii) electro-optical systems in the scanning mode using solid state detectors, (iii) imaging tubes and devices and (iv) microwave systems which can gather data even when clouds intervene. The data gathered with the sophisticated systems are converted into imagery or directly processed on electronic computers. The processed data are then interpreted in terms of known ground truths or ‘emr signatures’ of the objects. Remote sensing has wide applications in agriculture, forestry, geology, hydrology cartography and oceanography.     

Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization

In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R(2) = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.     

Optical infrared remote sensors

Various kinds of remote sensors, active and passive, covering a significant part of the electromagnetic spectrum from ultraviolet to microwave regions have been developed for observation of earth for the purpose of resource survey. Several of the widely used remote sensors (in the visible and infrared region) beginning from photographic cameras to the modern-day linear imaging self-scanning sensors have been described with reference to the state-of-the-art, critical parameters, performance limitations etc. User requirements with regard to various system parameters of the remote sensors have been analysed. Some future trends in the development of remote sensors for spaceborne applications have been touched upon.     

International scene in remote sensing

A brief survey of the status of remote sensing or earth observations from space as applied to earth-bound resources management is made. Glimpses of space systems under execution and under planning are given. Utilisation of the remote sensing technology by developed and developing countries is reviewed, pointing out current problems and future potentials.     

Data processing in remote sensing

A brief overview of pattern recognition and image processing with special emphasis on the first topic and its application to remote sensing is presented. Some of the recent areas of work in pattern recognition are also highlighted.     

International issues in remote sensing

Starting with the initial aim of reconnaissance technical developments in remote sensing have progressed sufficiently for the large-scale realisation of practical benefits. During the eighties a number of countries will have remote sensing satellite systems in operation. There are however a few technical, legal, political and economic issues that still remain unresolved. The resolution of these issues would facilitate practical applications especially in developing countries. Apart from the purely technical and economic issues such as the ability to compare data from two different satellites, the cost of the data etc one of the major hurdles in the application of this technology is the establishment of an international regime governing the activities of states in remote sensing. This is particularly important in view of the link between surveillance and remote sensing. Even though discussions have been going on for quite some time at the United Nations, the prospects of reaching agreement remain bleak. The main problems precluding agreement are national security, commercial and sovereignty concerns of the developed and developing countries. The key issues relate to the right of countries to conduct remote sensing over other countries, the right of countries collecting remote sensing data (over other countries) to distribute this data freely and the modalities of how the “sensitivity” aspects of remote sensing for surveillance and economic espionage can be reconciled with a legal regime that emphasises international cooperation. A critical analysis of existing international space law seems to indicate that there are two kinds of remote sensing—passive and active. In passive remote sensing the satellite sensor detects the sun-reflected or self-emitted radiation from objects on the ground. In active remote sensing a pulse of electromagnetic radiation is transmited from the satellite and its reflectance or scattering by objects on the earth’s surface is measured. A strict reading of existing legal principles on space seem to imply that passive sensing is legal while active sensing could be interpreted as violating the sovereignty of the sensed state. Agreement on remote sensing can be reached if a resolution or a range of resolutions can be defined to discriminate between “sensitive” and “non-sensitive” data. The only international agreement in this area between the USSR and a group of nine socialist countries uses a resolution limit of 50m. Available information on the subject seems to indicate that the range is from 25–50 m. One other aspect dealt with relates to the use of satellite data for verification of arms control measures, for crisis monitoring and the prospects of setting up an International Satellite Monitoring Agency (ISMA). It appears that the huge expense that this would entail would be justified only if theISMA can monitor the superpowers and the arms race between them.     

Correlated imaging with partially coherent light for remote sensing

Based on the extended Huygens–Fresnel integral and the theory of coherence of light field, we have investigated the correlated imaging by using the transverse normalized second-order intensity fluctuation correlation function with partially coherent light radiation. The imaging for a reflected object with relative long distance is determined by the feature of speckle-to-speckle correlation. By using the correlation function, we study the effects of imaging distance, the sizes of object lens and reference lens, the source’s transverse coherent width and its transverse size on the quality of correlated imaging. Numerical results show that the parameters of imaging system and the properties of partially coherent light source have significant influences on the imaging resolution and visibility. For an object lens with large enough diameter, the resolution is determined by the transverse coherent width of light source. On the contrary, it depends on the aperture of object lens. The magnification of the system depends only on the propagation distance. This speckle-to-speckle correlated imaging with unbalanced arms have potential applications in remote sensing due to its unique features.     

Channel selection of atmospheric remote sensing

We introduce a channel selection method for atmospheric remote-sensing problems described by a Fredholm integral equation of the first kind. Whether one set of channels (CH) is more suitable than another (CH') can be judged by whether (1) the degree of predominance (DP) value of CH is larger than that of CH', i.e., if the number of channels is the same and (2) the number of channels of CH is more than that of CH', if the DP values of both are acceptable. One can calculate the DP of the unknown function f (y) for a set of remote-sensing channels by DP = [1 + (Rf?(a)(2) - 1) R(d)(2)](-1/2), Rf?(a)(2) = R(c)(2)[ R(b)(2) + R(a)(2)(1 + R(b)(2))], where R(a), R(b), R(c), and (1 - R(d)(2))(1/2) of this channel set represent the influences on the ability to recover the unknown function caused by various measurement errors, the noise parameter, the relativity of the kernel functions, and the blindness of remote sensing means, respectively. Our channel selection method can be simplified to a conventional method when there are no differences in the relative measurement errors, no blind components of the unknown function and no noise parameters in the kernel function.