The study of aquatic ecosystems is an important research area addressing diverse problems such as carbon sequestration in coastal margins and wetlands, kelp and seagrass studies, coral reefs, harmful algal blooms and hypoxia, and carbon cycling in this dynamic environment. The application of an imaging spectrometer to aquatic ecosystem study is particularly challenging due to low water-leaving radiance levels adjacent to the shore region with its higher values. The Committee on Earth Observation Satellites (CEOS) has established more stringent performance standards for the visible/near infrared wavelengths than are typically available in imaging spectrometer designs. We have recently developed a compact form imaging spectrometer, the Chrisp Compact VNIR/SWIR Imaging Spectrometer (CCVIS), that facilitates their modular usage with a wide field telescope without sacrificing performance. The CCVIS design and the operational concept have predicted performance that approaches the CEOS standards. The envisioned satellite implementation requires a pitchback maneuver where the imaging of the slit projected onto the surface is slowly scanned while recording focal plane array readouts at a higher rate thereby avoiding saturation over the land surface while obtaining a high signal-to-noise ratio over the water. The effective frame rate is determined by the time it takes to scan the projected slit one ground sample distance (GSD). This approach has the added benefit of measuring a range of angles during a single GSD acquisition, providing insight into the bidirectional reflectance distribution function (BRDF).
KEYWORDS: Prisms, Spectroscopy, Calibration, Radiometry, Short wave infrared radiation, Signal to noise ratio, Sensors, Electronics, Data modeling, Polarization
We report the characteristics of the Portable Remote Imaging Spectrometer, an airborne sensor specifically designed for the challenges of coastal ocean research. PRISM has high signal to noise ratio and uniformity, as well as low
polarization sensitivity. Acquisition of high quality data has been demonstrated with the first engineering flight.
Hyperion is a hyperspectral sensor on board NASA's EO-1 satellite with a spatial resolution of approximately 30 m and a swath width of about 7 km. It was originally designed for land applications, but its unique spectral configuration (430 nm - 2400 nm with a ~10 nm spectral resolution) and high spatial resolution make it attractive for studying complex coastal ecosystems, which require such a sensor for accurate retrieval of environmental properties. In this paper, Hyperion data over an area of the Florida Keys is used to develop and test algorithms for atmospheric correction and for retrieval of subsurface properties. Remote-sensing reflectance derived from Hyperion data is compared with those from in situ measurements. Furthermore, water's absorption coefficients and bathymetry derived from Hyperion imagery are compared with sample measurements and LIDAR survey, respectively. For a depth range of ~ 1 - 25 m, the Hyperion bathymetry match LIDAR data very well (~11% average error); while the absorption coefficients differ by ~16.5% (in a range of 0.04 - 0.7 m-1 for wavelengths of 410, 440, 490, 510, and 530 nm) on average. More importantly, in this top-to-bottom processing of Hyperion imagery, there is no use of any a priori or ground truth information. The results demonstrate the usefulness of such space-borne hyperspectral data and the techniques developed for effective and repetitive observation of complex coastal regions.
The 3.75-micron and 11-micron channels on the polar orbiting NOAA Advanced Very High Resolution Radiometer (AVHRR) sensors have saturation temperatures of approximately 325 K. They allowed limited successes in estimating the sub-pixel fire temperature and fractional area coverage. The saturation problem associated with the 3.75-micron AVHRR channel greatly limited the ability for such estimates. In order to overcome this problem, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Terra and Aqua spacecrafts have both been equipped with a special fire channel centered at 3.95 microns with a specified saturation temperature of 500 K and a spatial resolution of 1 km. We have analyzed more than 40 sets of Terra and Aqua MODIS fire data acquired over different geographical regions, and found that very few fire pixels have the 3.95-micron fire channel brightness temperatures greater than 450 K. We suggest that the saturation temperature of fire channels near 4 microns for future satellite instruments with pixel sizes of about 1 km should be specified at about 450 K or even slightly lower in order to make the channels more useful for quantitative remote sensing of fires. A dual gain approach should also be considered for future satellite fire channels.
The Moderate Resolution Imaging Spectroradiometer (MODIS instruments onboard the NASA Terra and Aqua Spacecrafts have provided unprecedented capabilities in global observations of land, ocean, and atmosphere. In this paper, we report that, under certain atmospheric and surface conditions, several MODIS channels in the visible and near-IR spectral regions can saturate. The radiance dynamic ranges of similar channels for future satellite earth observing instruments should be increased. Instrument designers and MODIS data users should be aware of the saturation problems associated with these MODIS channels.
Cirrus clouds have been identified as one of the most uncertain components in climate research. They are located at high altitudes (near the tropopause), are frequently optically thin in nature, and are composed of non-spherical ice crystals. In this paper, we detail a method for inferring tropical cirrus cloud optical thickness from MODIS level-3 derived cirrus reflectance and solar/satellite view geometry data. We then demonstrate the applicability of this method using an independent MODIS level-3 data file from NASA s Aqua satellite to obtain the average daily tropical cirrus optical thickness. A preliminary study has also been conducted to ascertain the general characteristics of tropical cirrus cover as a whole using two consecutive years of Aqua MODIS global daily data. This study includes the frequency of occurrence (percentage of days with cirrus cover) and the spatial distribution of optical thickness fields. The retrieval method described here is complimentary to the standard operational MODIS cloud products (included in level-3 data) for the case of tropical cirrus clouds.
We previously developed an algorithm named Tafkaa for remote sensing of ocean color from aircraft and satellite platforms. The algorithm allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser's vector radiative transfer code. During the past few years we have extended the capabilities of the code. Current modifications include the ability to account for varying solar geometry (important for very long scenes) and view geometries (important for wide fields of view). Additionally, versions of Tafkaa have been made for a variety of multi-spectral sensors, including SeaWiFS and MODIS. Here we present sample results of atmospheric corrections of data from several platforms.
The NASA Moderate Resolution Imaging Spectrometer (MODIS) on the Terra Spacecraft has been collecting scientific data since February of 2000. MODIS is a major facility instrument for remote sensing of the atmosphere, land surfaces, and ocean color. On the MODIS instrument, there are five channels located within and around the 0.94-micron water vapor band absorption region for remote sensing of atmospheric water vapor. There is also a channel located at 1.375 micron for detecting thin cirrus clouds. In this paper, we describe the basic principles for using these near-IR channels for remote sensing of water vapor and high clouds. Based on the analysis of two years’ measurements with these channels on the Terra MODIS, we have found that reliable observations of water vapor and high clouds on regional and global scales can be made. We present results on seasonal variations of water vapor and high clouds.
We previously developed an algorithm for remote sensing of ocean color from space that allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser's vector radiative transfer code. During the past year we extended our radiative transfer calculations, allowing us to generate tables for several airborne altitudes. We also modified our lookup-table software to interpolate to sensor altitudes between those specified in the new tables. Here, we present results of atmospheric corrections using the new tables and software on hyperspectral imagery collected with NRL's recent PHILLS instrument and past AVIRIS flights.
The Moderate Resolution Imaging Spectroradiometer (MODIS), a major facility instrument on board the Terra Spacecraft, was successfully launched into space in December of 1999. MODIS has several near-IR channels within and around the 0.94- micrometer water vapor bands for remote sensing of integrated atmospheric water vapor over land and above clouds. MODIS also has a special near-IR channel centered at 1.375-micron with a width of 30 nm for remote sensing of cirrus clouds. In this paper, we describe briefly the physical principles on remote sensing of water vapor and cirrus clouds using these channels. We also present sample water vapor images and cirrus cloud images obtained from MODIS data.
Existing atmospheric correction algorithms for multi-channel remote sensing of ocean color from space were designed for retrieving water leaving radiances in the visible over clear deep ocean areas. The information about atmospheric aerosols is derived from channels between 0.66 and 0.87 micrometer, where the water leaving radiances are close to zero. The derived aerosol information is extrapolated back to the visible when retrieving water leaving radiances from remotely sensed data. For the turbid coastal environment, the water leaving radiances from the 0.66-micrometer channel may not be close to zero because of back scattering by suspended materials in the water. This channel may not be useful for deriving information on atmospheric aerosols. As a result, the algorithms developed for applications to clear ocean waters cannot be easily modified to retrieve water leaving radiances from remotely sensed data measured over the coastal environments. We have developed an atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the near-future Coastal Ocean Imaging Spectrometer (COIS). The algorithm uses lookup tables generated with a vector radiative transfer code. Aerosol parameters are determined by a spectrum-matching technique utilizing channels located at wavelengths longer than 0.86 micrometer. The aerosol information is extracted back to the visible based on aerosol models during our retrieval of water leaving radiances. Quite reasonable results have been obtained when applying our algorithm to process hyperspectral imaging data acquired with an airborne imaging spectrometer.
We present an overview of the Naval EarthMap Observer (NEMO) spacecraft and then focus on the processing of NEMO data both on-board the spacecraft and on the ground. The NEMO spacecraft provides for Joint Naval needs and demonstrates the use of hyperspectral imagery for the characterization of the littoral environment and for littoral ocean model development. NEMO is being funded jointly by the U.S. government and commercial partners. The Coastal Ocean Imaging Spectrometer (COIS) is the primary instrument on the NEMO and covers the spectral range from 400 to 2500 nm at 10-nm resolution with either 30 or 60 m work GSD. The hyperspectral data is processed on-board the NEMO using NRL's Optical Real-time Automated Spectral Identification System (ORASIS) algorithm that provides for real time analysis, feature extraction and greater than 10:1 data compression. The high compression factor allows for ground coverage of greater than 106 km2/day. Calibration of the sensor is done with a combination of moon imaging, using an onboard light source and vicarious calibration using a number of earth sites being monitored for that purpose. The data will be atmospherically corrected using ATREM. Algorithms will also be available to determine water clarity, bathymetry and bottom type.
Multi-channel remote sensing of ocean color from space has a rich history -- from the past CZCS, to the present SeaWiFS, and to the near-future MODIS. The atmospheric correction algorithms for processing remotely sensed data from these sensors were mainly developed by Howard Gordon at University of Miami. The algorithms were primarily designed for retrieving water leaving radiances in the visible spectral region over clear deep ocean areas. The information about atmospheric aerosols is derived from channels between 0.66 and 0.87 micrometer, where the water leaving radiances are close to zero. The derived aerosol information is extrapolated back to the visible when retrieving water leaving radiances from remotely sensed data. For the turbid coastal environment, the water leaving radiances for channels between 0.66 and 0.87 micrometer may not be close to zero because of back scattering by suspended materials in the water. Under these conditions, the channels are no longer useful for deriving information on atmospheric aerosols. As a result, the algorithms developed for applications to clear ocean waters cannot be easily modified to retrieve water leaving radiances from remote sensing data acquired over the costal environments. We have recently developed a fast and fully functional atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the Coastal Ocean Imaging Spectrometer (COIS). Our algorithm uses lookup tables generated with a vector radiative transfer code developed by Ahmad and Fraser (1982) and a spectral matching technique for the retrieval of water leaving radiances. The information on atmospheric aerosols is estimated using dark channels beyond 0.86 micron. Quite reasonable results were obtained when applying the algorithm to process spectral imaging data acquired over Chesapeake Bay with the NASA JPL Airborne Visible Infrared Imaging Spectrometer (AVIRIS).
KEYWORDS: Data modeling, Calibration, Algorithm development, Atmospheric corrections, Coastal modeling, Space operations, Commercial off the shelf technology, Reflectivity, Atmospheric modeling, Imaging systems
A wide variety of applications of imaging spectrometry have been demonstrated using data from aircraft systems. Based on this experience the Navy is pursuing the Hyperspectral Remote Sensing Technology (HRST) Program to use hyperspectral imagery to characterize the littoral environment, for scientific and environmental studies and to meet Naval needs. To obtain the required space based hyperspectral imagery the Navy has joined in a partnership with industry to build and fly the Naval EarthMap Observer (NEMO). The NEMO spacecraft has the Coastal Ocean Imaging Spectrometer (COIS) a hyperspectral imager with adequate spectral and spatial resolution and a high signal-to- noise ratio to provide long term monitoring and real-time characterization of the coastal environment. It includes on- board processing for rapid data analysis and data compression, a large volume recorder, and high speed downlink to handle the required large volumes of data. This paper describes the algorithms for processing the COIS data to provide at-launch ocean data products and the research and modeling that are planned to use COIS data to advance our understanding of the dynamics of the coastal ocean.
The concept of imaging spectrometry was originated from geological communities in the early 1980s. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) built at NASA Jet Propulsion Laboratory has been collecting spectral imaging data in the 0.4 - 2.5 micron region from an ER-2 aircraft at 20 km for a decade. Newer generations of airborne and spaceborne imaging spectrometers have also been built. In this paper, techniques, such as spectral matching and channel ratioing for extracting information on the Earth's atmosphere and surfaces are illustrated. Examples of using the technique of imaging spectrometry for remote sensing of atmospheric water vapor, cirrus clouds, aerosols, fire, land surface properties, and ocean color are presented. The applications of imaging spectrometry to the selection and implementation of new channels on the Moderate Resolution Imaging Spectroradiometer (MODIS) for global remote sensing of atmospheric water vapor and cirrus clouds from space are also described.
In recent years, spectral imaging data have been acquired with a number of airborne imaging spectrometers. Similar data will soon be collected with the NASA HyperSpectral Imager instrument from the Lewis spacecraft. The majority of users of imaging spectrometer data are interested in studying surface properties. The atmospheric absorption and scattering effects must be removed from imaging spectrometer data, so that surface reflectance spectra can be derived. Previously, we developed and updated an operational atmosphere removal algorithm, which used the Malkmus narrow band model for modeling atmospheric gaseous transmittance. The narrow band model does a reasonably good job in modeling spectra at a resolution of 10 nm or coarser. Imaging spectrometers with spectral resolutions finer than 10 nm are now available. The narrow band model is not quite suitable for modeling data collected with these spectrometers. In this paper, we describe the development of a line-by-line- based algorithm for removing atmospheric effects from imaging spectrometer data. We also discus issues related to sampling and spectral resolution.
Through analysis of spectral imaging data acquired with the airborne visible infrared imaging spectrometer (AVIRIS) from an ER-2 aircraft at 20 km altitude, it was found that narrow channels near the center of the strong 1.38 micrometer water vapor band are very sensitive in detecting thin cirrus clouds over different geographical regions. Based on this observation from AVIRIS data a channel centered at 1.375 micrometer with a width of 30 nm was selected for the moderate resolution imaging spectrometer (MODIS) for remote sensing of cirrus clouds from space. The sensitivity of this channel to detect thin cirrus clouds during the day time is expected to be one or two orders of magnitude better than the current infrared emission techniques. As a result, much larger fraction of the satellite data is expected to be identified as being covered by cirrus clouds. In order to make better studies of surface reflectance properties, thin cirrus effects must be removed from satellite images. We have developed an empirical algorithm for removing/correcting thin cirrus effects in the 0.4 - 1.0 micrometer region using channels near 1.375 micrometer. This algorithm will be incorporated into the present MODIS atmospheric correction algorithms for ocean color and land applications.
Spectral imaging data have been acquired with the Navy HYDICE (the hyperspectral digital imagery collection experiment) instrument from an aircraft. Similar data will soon be collected with the NASA HSI instrument from the Lewis spacecraft. The majority of users of imaging spectrometer data are interested in studying surface properties. Therefore, atmospheric absorption and scattering effects must be removed from imaging spectrometer data, so that surface reflectance spectra can be derived. Previously, an operational atmosphere removal algorithm, which used the 5S code for modeling the atmospheric scattering effects and the Malkmus narrow band model for modeling atmospheric gaseous transmittances, was specifically designed for deriving surface reflectances from spectral imaging data collected by the NASA JPL airborne visible/infrared imaging spectrometer (AVIRIS). We have recently updated this algorithm by replacing the 5S code, which requires that the sensor be at the top of the atmosphere, with the 6S code, which accommodates sensors at any altitudes. The updated algorithm allows processing of imaging spectrometer data acquired from low- and high-altitude aircraft platforms, and from satellite platforms. We are currently developing another algorithm that uses a line-by line code to calculate atmospheric gaseous transmittances for processing imaging spectrometer data with spectral resolution between approximately 0.5 and 10 nm.
Using spectral imaging data acquired with the Airborne Visible Infrared Imaging Spectrometer
(AVIRIS) from an ER-2 aircraft at 20 km altitude during various field programs, it was found that narrow
channels near the center of the strong 1 .38-jim water vapor band arevery effective in detecting thin cirrus
clouds. Based on this observation from AVIRIS data, a channel centered at 1 .375im with a width of 30
nm was selected for the Moderate Resolution Imaging Spectrometer (MODIS) for remote sensing of
cirrus clouds from space. The sensitivity of the 1 .375-jim MODIS channel to detect thin cirrus clouds
during the day time is expected to be one to two orders of magnitude better than the current infrared
emission techniques. As a result, much larger fraction of the satellite data is expected to be identified as
being covered by cirrus clouds. In order to make better studies of surface reflectance properties, thin
cirrus effects must be removed from satellite images. We have developed an empirical approach for
removing/correcting thin cirrus effects in the 0.4 - 1.0 j.tm region using channels near 1 .375 tim. This
represents a step beyond the detection of cirrus clouds using water vapor absorption channels.
The normalized difference vegetation index (NDVI), which is equal to (NIR- RED)/(NIR+RED), has been widely used for remote sensing of vegetation for many years. One weakness of this index is that the reflectance of RED channel has no sensitivity to changes in lead area index changes when the leaf area index is equal to 1 or greater due to strong chlorophyll absorption near 0.67 micron. In this paper, another index, namely the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is equal to [R(0.86 micrometers ) - R(1.24 micrometers )]/[R(0.86 micrometers ) + R(1.24 micrometers )], where R represents the apparent reflectance. At 0.86 micrometers and 1.24 micrometers , vegetation canopies have similar scattering properties, but slightly different liquid water absorption. The scattering by vegetation canopies enhances the weak liquid water absorption at 1.24 micrometers . As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge, California and Holland, Maine are used to demostrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given. Because aerosol scattering effects in the 0.86-1.24 micrometers region are weak, NDWI is less sensitive to atmospheric effects that NDVI.
Remote sensing of water status and biochemical components of vegetation can have important applications in the fields of agriculture and forestry. Reflectance of fresh, green vegetation in the 1.0 - 2.5 micrometers region is dominated by liquid water absorption and also weakly affected by absorption due to biochemical components, such as lignin and cellulose. We have developed both the nonlinear and linear least squares spectrum- matching techniques for deriving equivalent water thickness (EWT) of vegetation from AVIRIS data in the 1.0 and 1.6 micrometers regions. Seasonal variations of EWTs over an agricultural area in Greeley, Colorado are determined. EWTs from 1.0 micrometers region are generally greater than those from 1.6 micrometers region because of the deeper light penetration into the canopy. After fitting the AVIRIS data with water spectrum alone, a weak lignin-cellulose absorption feature centered at 1.72 micrometers is seen in the residual spectra. We map the depth of the 1.72-micrometers feature, which can be considered as an index of component abundance in the canopy.
Thin cirrus clouds are difficult to detect in visible and thermal infrared images, particularly over land. Using spectral imaging data measured with the airborne visible/infrared imaging spectrometer (AVIRIS) from an ER-2 aircraft, it has been found that narrow channels close to the center of the strong 1.38 micrometers water vapor band are very effective in separating thin cirrus clouds from clear surface areas. Due to the total absorption of solar radiation by atmospheric water vapor, pixels that do not contain cirrus clouds or stratospheric aerosols are black in images around 1.38 micrometers . Pixels containing cirrus clouds appear white in these images because of the scattering of solar radiation by cirrus clouds. We have selected a near- IR channel centered at 1.375 micrometers with a width of 30 nm for the moderate resolution imaging spectrometer (MODIS) for remote sensing of cirrus clouds from space. This channel may also be useful for remote sensing of stratospheric aerosols when cirrus clouds are absent.
Column atmospheric water vapor amounts at high spatial resolution were derived from spectral data collected by the airborne visible-infrared imaging spectrometer (AVIRIS). The quantitative derivation is made by curve fitting observed spectra with calculated spectra in the 0. 94 jim and 1. 14 jim water vapor band absorption regions using an atmospheric model a narrow band spectral model and a nonlinear least squares fitting technique. The derivation is also made using a band ratioing technique. These techniques are applicable for retrieving water vapor values from AVIRIS data measured on clear days with visibilities 20 km or greater. The precision of the retrieved column water vapor amounts is 5 or better. It now appears feasible to derive high spatial resolution column water vapor amounts over land areas from satellite altitude with the proposed high resolution imaging spectrometer (HIRIS). Curve fitting of spectra near 1 jim from areas covered with vegetation using an atmospheric model and a simplified vegetation reflectance model indicates that both the amount of atmospheric water vapor and the moisture content of vegetation can be retrieved simultaneously because the band centers of liquid water in vegetation and the atmospheric water vapor are offset by approxinuitely 0. 05 jim. 1.
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