Mantis: an all-sky visible-to-near-infrared hyper-angular spectropolarimeter.

In this paper, we introduce and present first results from Mantis, a pushbroom type spectropolarimeter recently acquired by the Naval Research Laboratory and built by Polaris Sensor Technologies, Inc. The instrument is designed for high spatial and spectral resolution polarimetric imaging of downwelling skylight. Linear Stokes vectors are acquired over the spectral range of 382-1017 nm, with ≈0.64nm channel spacing, and each line scan consists of 2226 pixels over a 72° field of view (0.75 mrad instantaneous). Measurement of the full sky dome is achieved through the use of a high-precision motorized pan-tilt unit and systematic scanning. An automated Sun shade allows for data collection in the main solar plane without saturation of the focal plane. The uncertainty in the degree of linear polarization varies between 0.07% and 0.5%, depending on incidence angle and wavelength. The total radiometric uncertainty is 2.07% to 2.5%, of which 2% is absolute calibration error. Preliminary data analysis reveals the instrument has a large potential for remote sensing applications.

A Systematic Sensitivity Study on Surface Pixel Shifts in High Spatial Resolution Satellite Images Resulting from Atmospheric Refraction in the Sensor to Surface Ray Path.

When viewing Earth's surfaces from a low Earth orbiting (LEO) satellite platform with an optical sensor, the upward light propagation path from the ground to the satellite is affected by atmospheric refraction. For imaging sensors with a spatial resolution of about one km on the ground, atmospheric refraction is typically neglected during geo-registration of the satellite images. However, for high spatial resolution imaging systems with surface pixel sizes of approximately one meter or finer, the neglect of atmospheric refraction effects can typically introduce errors of a few meters in the spatially registered images. The atmospheric refraction effects need to be properly taken into consideration during the spatial registration of high spatial resolution satellite images. We have found that, with minor modifications, the ray tracing models implemented inside the LOWTRAN series of atmospheric radiative transfer codes developed in the 1970s and 1980s, in particular LOWTRAN7 in late 1980s, can be used for modeling the pixel displacement resulting from atmospheric refraction for satellite observations. The LOWTRAN series models were originally designed for calculating atmospheric transmittances and radiances for radiation going through long paths of the Earth's atmosphere. In the ray tracing portions of the codes, a spherical model atmosphere from the ground to 100 km is finely divided into about 30 thin atmospheric layers. The refraction angles for ray paths between consecutive layer boundaries are accurately calculated. We make a new use of the refraction angles calculated by the LOWTRAN7 code to study the surface pixel shift resulting from atmospheric refraction for satellite observations. In this letter, we report the modeling results on surface pixel displacements for different satellite altitudes and downward view zenith angles, several atmospheric temperature and pressure profiles, a few surface elevations, and wavelength dependencies from blue (450 nm) to near-IR (865 nm). These results can have reference values for researchers to estimate refraction-induced pixel displacements in their high spatial resolution satellite images. The results may also potentially help in designing spacecraft algorithms for accurate instrument pointing and mission tasking to automatically capture short-lived science events.

Propagation of sensor noise in oceanic hyperspectral remote sensing.

In previous works, the authors have shown via numerical simulation that sensor noise, even assuming otherwise perfect knowledge of the environment, can cause large scale variations in the retrieval of concentrations of biophysical parameters in a water body, and also investigated methods for using statistical measures (such as the Mahalanobis distance) to help mitigate these issues. In this work, we derive explicit formulas that can be used to estimate how uncertainty in the sensor radiance is propagated to uncertainty in the remote sensing reflectanceRrs(λ), without the need for simulations. In particular, the formulas show that the variation in Rrs(λ)is affected by not only the noise characteristics of the sensor, but also by the conditions (atmospheric parameters, viewing angles, altitude) under which the data is collected. We include validation results for the formulas over a wide range of atmospheric conditions, and show by example how the collection conditions can affect the uncertainty in Rrs(λ).

Airborne system for multispectral, multiangle polarimetric imaging.

In this paper, we describe the design, fabrication, calibration, and deployment of an airborne multispectral polarimetric imager. The motivation for the development of this instrument was to explore its ability to provide information about water constituents, such as particle size and type. The instrument is based on four 16 MP cameras and uses wire grid polarizers (aligned at 0°, 45°, 90°, and 135°) to provide the separation of the polarization states. A five-position filter wheel provides for four narrow-band spectral filters (435, 550, 625, and 750 nm) and one blocked position for dark-level measurements. When flown, the instrument is mounted on a programmable stage that provides control of the view angles. View angles that range to ±65° from the nadir have been used. Data processing provides a measure of the polarimetric signature as a function of both the view zenith and view azimuth angles. As a validation of our initial results, we compare our measurements, over water, with the output of a Monte Carlo code, both of which show neutral points off the principle plane. The locations of the calculated and measured neutral points are compared. The random error level in the measured degree of linear polarization (8% at 435) is shown to be better than 0.25%.

Expected improvements in the quantitative remote sensing of optically complex waters with the use of an optically fast hyperspectral spectrometer-a modeling study.

Using simulated data, we investigated the effect of noise in a spaceborne hyperspectral sensor on the accuracy of the atmospheric correction of at-sensor radiances and the consequent uncertainties in retrieved water quality parameters. Specifically, we investigated the improvement expected as the F-number of the sensor is changed from 3.5, which is the smallest among existing operational spaceborne hyperspectral sensors, to 1.0, which is foreseeable in the near future. With the change in F-number, the uncertainties in the atmospherically corrected reflectance decreased by more than 90% across the visible-near-infrared spectrum, the number of pixels with negative reflectance (caused by over-correction) decreased to almost one-third, and the uncertainties in the retrieved water quality parameters decreased by more than 50% and up to 92%. The analysis was based on the sensor model of the Hyperspectral Imager for the Coastal Ocean (HICO) but using a 30-m spatial resolution instead of HICO's 96 m. Atmospheric correction was performed using Tafkaa. Water quality parameters were retrieved using a numerical method and a semi-analytical algorithm. The results emphasize the effect of sensor noise on water quality parameter retrieval and the need for sensors with high Signal-to-Noise Ratio for quantitative remote sensing of optically complex waters.

Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling.

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise in the data. In this study, we have compared the Euclidean distance and the Mahalanobis distance as metrics for spectral comparison. In addition to providing justification for the preference of the Mahalanobis Distance over the Euclidean Distance, we have also included a statistical description of noisy hyperspectral data.

Impact of signal-to-noise ratio in a hyperspectral sensor on the accuracy of biophysical parameter estimation in case II waters.

Errors in the estimated constituent concentrations in optically complex waters due solely to sensor noise in a spaceborne hyperspectral sensor can be as high as 80%. The goal of this work is to elucidate the effect of signal-to-noise ratio (SNR) on the accuracy of retrieved constituent concentrations. Large variations in the magnitude and spectral shape of the reflectances from coastal waters complicate the impact of SNR on the accuracy of estimation. Due to the low reflectance of water, the actual SNR encountered for a water target is usually quite lower than the prescribed SNR. The low SNR can be a significant source of error in the estimated constituent concentrations. Simulated and measured at-surface reflectances were used in this study. A radiative transfer code, Tafkaa, was used to propagate the at-surface reflectances up and down through the atmosphere. A sensor noise model based on that of the spaceborne hyperspectral sensor HICO was applied to the at-sensor radiances. Concentrations of chlorophyll-a, colored dissolved organic matter, and total suspended solids were estimated using an optimized error minimization approach and a few semi-analytical algorithms. Improving the SNR by reasonably modifying the sensor design can reduce estimation uncertainties by 10% or more.

Vicarious calibrations of HICO data acquired from the International Space Station.

The Hyperspectral Imager for the Coastal Ocean (HICO) presently onboard the International Space Station (ISS) is an imaging spectrometer designed for remote sensing of coastal waters. The instrument is not equipped with any onboard spectral and radiometric calibration devices. Here we describe vicarious calibration techniques that have been used in converting the HICO raw digital numbers to calibrated radiances. The spectral calibration is based on matching atmospheric water vapor and oxygen absorption bands and extraterrestrial solar lines. The radiometric calibration is based on comparisons between HICO and the EOS/MODIS data measured over homogeneous desert areas and on spectral reflectance properties of coral reefs and water clouds. Improvements to the present vicarious calibration techniques are possible as we gain more in-depth understanding of the HICO laboratory calibration data and the ISS HICO data in the future.

Interpretation of absorption bands in airborne hyperspectral radiance data.

It is demonstrated that hyperspectral imagery can be used, without atmospheric correction, to determine the presence of accessory phytoplankton pigments in coastal waters using derivative techniques. However, care must be taken not to confuse other absorptions for those caused by the presence of pigments. Atmospheric correction, usually the first step to making products from hyperspectral data, may not completely remove Fraunhofer lines and atmospheric absorption bands and these absorptions may interfere with identification of phytoplankton accessory pigments. Furthermore, the ability to resolve absorption bands depends on the spectral resolution of the spectrometer, which for a fixed spectral range also determines the number of observed bands. Based on this information, a study was undertaken to determine under what circumstances a hyperspectral sensor may determine the presence of pigments. As part of the study a hyperspectral imager was used to take high spectral resolution data over two different water masses. In order to avoid the problems associated with atmospheric correction this data was analyzed as radiance data without atmospheric correction. Here, the purpose was to identify spectral regions that might be diagnostic for photosynthetic pigments. Two well proven techniques were used to aid in absorption band recognition, the continuum removal of the spectra and the fourth derivative. The findings in this study suggest that interpretation of absorption bands in remote sensing data, whether atmospherically corrected or not, have to be carefully reviewed when they are interpreted in terms of photosynthetic pigments.

Ocean PHILLS hyperspectral imager: design, characterization, and calibration.

The Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy (Ocean PHILLS) is a hyperspectral imager specifically designed for imaging the coastal ocean. It uses a thinned, backsideilluminated CCD for high sensitivity and an all-reflective spectrograph with a convex grating in an Offner configuration to produce a nearly distortionfree image. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several oceanographic experiments in 1999-2001. Here we describe the instrument design and present the results of laboratory characterization and calibration. We also present examples of remote-sensing reflectance data obtained from the LEO-15 site in New Jersey that agrees well with ground-truth measurements.

Identification of bacteria from two-dimensional resonant-Raman spectra.

We present the first measurements of two-dimensional resonant-Raman spectra and demonstrate the applicability of the method to the identification of bacteria, including differentiation of genetically similar species. A new device that sequentially illuminates bacteria with different ultraviolet wavelengths and measures a spectrum at each was developed for this purpose. We anticipate that information within such two-dimensional spectra will allow identification of bacteria and chemicals in environments containing multiple organisms and chemicals, leading, for example, to instruments that rapidly identify bacteria in hospital and food plant settings, for screening large populations, and for biochemical-threat warning systems.

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables.

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.