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Retrieving surface properties from airborne hyperspectral imagery requires the use of an atmospheric correction model to compensate for atmospheric scattering and absorption. In this study, a solar spectral irradiance monitor (SSIM) from the University of Colorado Boulder was flown on a Twin Otter aircraft with the National Ecological Observatory Network’s (NEON) imaging spectrometer. Upwelling and downwelling irradiance observations from the SSIM were used as boundary conditions for the radiative transfer model used to atmospherically correct NEON imaging spectrometer data. Using simultaneous irradiance observations as boundary conditions removed the need to model the entire atmospheric column so that atmospheric correction required modeling only the atmosphere below the aircraft. For overcast conditions, incorporating SSIM observations into the atmospheric correction process reduced root-mean-square (rms) error in retrieved surface reflectance by up to 57% compared with a standard approach. In addition, upwelling irradiance measurements were used to produce an observation-based estimate of the adjacency effect. Under cloud-free conditions, this correction reduced the rms error of surface reflectance retrievals by up to 27% compared with retrievals that ignored adjacency effects.

This chapter gives an overview of airborne radiometry, specifically solar spectral and broadband radiation (irradiance, radiance, actinic flux) measurements and their use for quantifying the radiative effects of atmospheric constituents (clouds, aerosols, gases), photochemistry, and surface properties. Crucially, airborne radiometers establish a link between the properties of atmospheric constituents as retrieved from remote sensing and their radiative impact on weather and climate. The chapter addresses important advances in instrumentation, motivated by challenging problems in radiation science and examines remaining challenges and expected developments.

Optimal Estimation (OE) methods can simultaneously estimate surface and atmospheric properties from remote Visible/Shortwave imaging spectroscopy. Simultaneous solutions can improve retrieval accuracy with principled uncertainty quantification for hypothesis testing. While OE has been validated under benign atmospheric conditions, future global missions will observe environments with high aerosol and water vapor loadings. This work addresses the gap with diverse scenes from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) India campaign. We refine atmospheric models to represent variable aerosol optical depths and properties. We quantify retrieval accuracy and information content for both reflectance and aerosols over different surface types, comparing results to in situ and remote references. Additionally, we assess uncertainty of maximum a posteriori solutions using linearized estimates as well as sampling-based inversions that more completely characterize posterior uncertainties. Principled uncertainty quantification can combine multiple spacecraft data products while preventing local environmental biases in future global investigations.

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth's climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate spectral decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7% and 99.9% of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

We treat Earth-reflected radiation as mixtures of spectra unique to sources of scattering and absorption in Earth’s atmosphere and surface. We identify the signals and quantify their mixtures using source separation in a Bayesian framework.

Montane coniferous forests and woodlands in the Front Range of the Colorado Rocky Mountains have been subject to increased wildfire in recent years. The area and intensity of these fires is strongly dependent upon the spatial variability and type of fuels as they are arrayed across the landscape. Considering the size of the patches and the mosaic of fuel materials, high spectral and spatial resolution estimates of vegetation components and fuel types are needed to improve fire risk assessment, especially around the wildland/urban interface. Here we used highly resolved remotely sensed imagery, in combination with several spectral techniques to map major forest components and fuel types in montane coniferous forests in the Colorado Front Range by discriminating the fractional covers of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil at a sub-pixel level. An accuracy assessment based on a dataset including 34 field transects indicated that we could explain fractional cover of 73.5%, 40.3%, and 77.6% for PV, NPV, and soil respectively through the use of hyperspectral indicators. Based on the fractional cover of these components, we were able to assess the spatial patterns of vegetation and fuel characteristics at a landscape scale. Throughout the study areas, PV fractions were dominant (48.7%), followed by NPV (28.8%) and soil (22.5%). However, due to microclimate and disturbances such as fire, insect infestations and forest management practices, the spatial distribution of fractions was highly heterogeneous. There was a high fraction of PV in mature forest and on north-facing slopes, and a high fraction of NPV and bare soil in areas with recent disturbance such as fire or insect infestation. In severely burned areas, bare soil was dominant. Fuel treatments reduced the fraction of PV by 11.7%, and increased fractions of NPV by 7.4% and bare soil by 4.5%. These results suggest that hyperspectral remote sensing can be an excellent indicator of not only fuel fractional cover, but of fuel condition after fire, thereby greatly improving regional fire risk assessment.

A synthetic data spectral cube that represents at-sensor radiance data of AVIRIS was used to examine the accuracy of several methods to recover absolute surface reflectance data of terrestrial targets. Soil and vegetation targets, selected to represent the images of ground variation and their spectra, were retrieved using HATCH, Empirical Line (EL) and their hybrids methods. After a synthetic radiance data cube was generated, reflectance recovery was carried out and compared with the true (input) reflectance information. It was found that even under controlled and ideal conditions, the spectral recovery using HATCH code provided differences of up to 40%. The EL methods, using the two end-members that represent the scene reduced this difference to about 4%, and in some cases, even to 0.1% It was found that selecting the calibration targets over low water vapor content improved the results. Applying EL on radiance data provided a severe difference of more than 200% in areas located outside the calibration target water vapor zone. Only over similar water vapor zones were the EL methods found to reasonably recover the surface reflectance. Examining the spectral variability in the calibration targets showed that using of spectral features targets with relative spectral similarity is almost as effective as using spectrally featureless targets for the EL process. Applying EL, using external spectral information of possible known targets, revealed a relatively high difference, as compared to the true reflectance data. However, thematic analysis using a SAM classifier proved that even under non-ideal conditions, the EL correction can yield a reasonable spatial mapping capability relative to those obtained under real reflectance domains. It was concluded that EL must be run on reflectance data (generated from absolute based method) over low water vapor zones to provide the most precise reflectance information. Also, it was found that it is not mandatory to select calibration targets that are totally featureless or characterized by low or high albedo response.

The High-accuracy Atmospheric Correction for Hyperspectral Data (HATCH) model was developed for deriving high-quality surface reflectance spectra from remotely sensed hyperspectral imaging data. This paper presents the novel techniques applied in HATCH. An innovative technique, a "smoothness test" for water vapor amount retrieval and for automatic spectral calibration, is developed for HATCH. HATCH also includes an original fast radiative transfer equation solver and a correlated-k gaseous absorption model based on HITRAN 2000 database. Spectral regions with overlapping absorptions by different gases are handled by precomputing a correlated-k lookup table for various gas mixing ratios. The interaction between multiple scattering and absorption is explicitly handled through the use of the correlated-k method for gaseous absorption. Finally, some results are presented for HATCH applied to Airborne Visible Infrared Imaging Spectoradiometer data and together with comparison of the results between HATCH and the Atmosphere Removal program. The limitations in HATCH include that the HATCH assumes a Lambertian surface, and adjacent effect is not considered. HATCH assumes aerosols to be spatially homogeneous in a scene.

The atmospheric correction program High-accuracy Atmospheric Correction for Hyperspectral data (HATCH) has been developed specifically to convert radiance from imaging spectrometer sensors to reflectance on a pixel-by-pixel basis. HATCH was developed to update the previously available model, the Atmosphere Removal model (ATREM). In this paper, we test the HATCH model against model data using MODTRAN 4 as well as with Airborne Visible/Infrared Imaging Spectroradiomter and Hyperion data for which simultaneous ground reflectances were acquired. We also compare HATCH to the commercially available ACORN model. Results show that HATCH produces smoother-looking spectra than its predecessor ATREM and is less influenced by liquid water in vegetation. Comparisons with MODTRAN were made by calculating above-atmosphere radiances from a hypothetical target with a 0.5 reflectance at all wavelengths between 400 and 2500 nm and retrieving them with HATCH. The results show a maximum deviation of 10% at several wavelengths, highlighting the differences between the models. Hyperion images contain striping artifacts. We show that optimum removal is obtained by normalizing the means and standard deviations of each column after converting radiance to reflectance with an atmospheric model. HATCH produces water vapor corrections virtually unaffected by vegetation liquid water if the water vapor band at 940 nm is used in the calculation. Retrievals using the 1140-nm band are subject to errors associated with liquid water in vegetation. Retrievals of reflectance from Hyperion radiances require use of the 1140-nm band because the 940-nm band falls in the detector crossover region.

[1] We explore the potential for directly measured hyperspectral Earth-reflected solar radiances to provide sufficient information to study changes in Earth's climate based on the quantified variability of the data using principal component analysis (PCA) and singular spectrum analysis. To do this we used these two multivariate analysis techniques on Earth-reflected radiances between 300 and 1750 nm measured from space by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument. The spatial and temporal variability of hyperspectral reflected radiances over global, hemispherical, and regional scales was quantified. As few as six components were needed to explain over 99.5% of the variance in all cases studied, with the exception of an Arctic Ocean case in which only four components were needed. Both of these values represent large reductions in dimensionality of the input radiances from 291 spectral bands. PCA facilitated attribution of the dominant spectral patterns extracted to atmospheric and surface variables, including water vapor, clouds, surface albedo, and sea ice. The second most dominant spectral variable, that is, the second principal component, in the Arctic closely resembled sea ice reflectance and followed the temporal behavior of sea ice extent determined from AMSR-E observations. The extraction of the spectral, spatial, and temporal variability in reflected shortwave hyperspectral radiance using multivariate analysis provides an alternate and complementary approach to inverse methods for applying space-based observations to climate studies.

[1] The measurement of cloud absorption from aircraft has been a controversial subject largely because broadband measurements provide little insight into the physical mechanisms underlying the absorption. To partition and quantify the various mechanisms of cloud absorption, spectrally resolved measurements are required. Measurements of cloud solar spectral (400–2150 nm) absorption from airborne spectroradiometers are presented from two cases of extensive tropical marine stratus cloud fields. Radiative transfer modeling was used to retrieve the cloud optical thickness and cloud droplet effective radius from a best fit with the measured cloud spectral albedo. These values were used to estimate the cloud spectral absorptance. For the higher optical thickness case, the measurement-model agreement in absorptance across the spectrum is better than 0.05 and substantially better (within 0.01) at visible wavelengths unaffected by cloud absorption. For an optically thinner and more heterogeneous cloud field, the differences were higher, up to 0.07 in the near infrared. The standard deviations of cloud absorptance spectra show that the integrated absorbed irradiances, usually measured by broadband radiometers, are strongly affected by variations in the water vapor amount. Radiative transfer modeling is used to illustrate the spectral dependence of the absorption from radiatively important gases (e.g., water vapor), cloud liquid water, and absorbing aerosol particles. A novel sampling strategy, based on single aircraft measurements, is demonstrated, as is the value of spectrally resolved measurements in partitioning the various mechanisms of cloud absorption including the possible effects of absorbing aerosols embedded in clouds.

Multivariate analysis techniques were used to quantify and compare the spectral and temporal variability of observed and simulated shortwave hyperspectral Earth reflectance. The observed reflectances were measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument between 2002 and 2010. The simulated reflectances were calculated using climate Observing System Simulation Experiments (OSSEs), which used two Intergovernmental Panel on Climate Change AR4 scenarios (constant CO2 and A2 emission) to drive Moderate Resolution Atmospheric Transmission simulations. Principal component (PC) spectral shapes and time series exhibited evidence of physical variables including cloud reflectance, vegetation and desert albedo, and water vapor absorption. Comparing the temporal variability of the OSSE-simulated and SCIAMACHY-measured hyperspectral reflectance showed that their Intertropical Convergence Zone-like Southern Hemisphere (SH) tropical PC1 ocean time series had a 90° phase difference. The observed and simulated PC intersection quantified their similarity and directly compared their temporal variability. The intersection showed that despite the similar spectral variability, the temporal variability of the dominant PCs differed as in, for example, the 90° phase difference between the SH tropical intersection PC1s. Principal component analysis of OSSE reflectance demonstrated that the spectral and centennial variability of the two cases differed. The A2 PC time series, unlike the constant CO2 time series, exhibited centennial secular trends. Singular spectrum analysis isolated the A2 secular trends. The A2 OSSE PC1 and PC4 secular trends matched those in aerosol optical depth and total column precipitable water, respectively. This illustrates that time series of hyperspectral reflectance may be used to identify and attribute secular climate trends with a sufficiently long measurement record and high instrument accuracy.

[1] Coordinated flight legs of two aircraft above and below extended ice clouds played an important role in the Tropical Composition, Cloud and Climate Coupling Experiment (Costa Rica, 2007). The Solar Spectral Flux Radiometer measured up- and downward irradiance on the high-altitude (ER-2) and the low-altitude (DC-8) aircraft, which allowed deriving apparent absorption on a point-by-point basis along the flight track. Apparent absorption is the vertical divergence of irradiance, calculated from the difference of net flux at the top and bottom of a cloud. While this is the only practical method of deriving absorption from aircraft radiation measurements, it differs from true absorption when horizontal flux divergence is nonzero. Differences between true and apparent absorption are inevitable in any inhomogeneous atmosphere, especially clouds. We show, for the first time, the spectral shape of measured apparent absorption and compare with results from a three-dimensional radiative transfer model. The model cloud field is created from optical thickness and effective radius retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator and from reflectivity profiles from the Cloud Radar System, both on board the ER-2. Although the spectral shape is reproduced by the model calculations, the measured apparent absorption in the visible spectral range is higher than the model results along extended parts of the flight leg. This is possibly due to a net loss of photons into neighboring cirrus-free areas that are not contained within the model domain.

We present current results of a study that will aid in defining the requirements of an Earth-viewing spectrometer over the solar spectral domain for climate benchmarking, a driving imperative for CLARREO.

Testing MODTRATM5 (MOD5) capabilities against NASA satellite state-of-the-art radiance and irradiance measurements has recently been undertaken. New solar data have been acquired from the SORCE satellite team, providing measurements of variability over solar rotation cycles, plus an ultra-narrow calculation for a new solar source irradiance, extending over the full MOD5 spectral range. Additionally, a MOD5-AIRS analysis has been undertaken with appropriate channel response functions. Thus, MOD5 can serve as a surrogate for a variety of perturbation studies, including two different modes for including variations in the solar source function, Io: (1) ultra-high spectral resolution and (2) with and without solar rotation. The comparison of AIRS-related MOD5 calculations, against a suite of 'surrogate' data generated by other radiative transfer algorithms, all based upon simulations supplied by the AIRS community, provide validation in the Long Wave Infrared (LWIR). All ~2400 AIRS instrument spectral response functions (ISRFs) are expected to be supplied with MODTRANTM5. These validation studies show MOD5 replicates line-by-line (LBL) brightness temperatures (BT) for 30 sets of atmospheric profiles to approximately -0.02°K average offset and

The High-accuracy Atmosphere Correction for Hyperspectral Data (HATCH) algorithm retrieves surface reflectance from imaging spectrometer data in the 350-2500nm wavelength region with resolutions of 5nm or greater. A key feature of HATCH is that it derives calibration for the sensor spectral response functions (band centers and FWHMs) from the data themselves. This is approached by utilizing known atmospheric absorption features (e.g. water vapor and oxygen bands) and the application of a new technique dubbed the© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Over the last decade a series of techniques has been developed to correct hyperspectral imaging sensed at to apparent surface reflectance. The techniques range from the empirical line method that makes use of ground target measurements to model-based methods such as the atmospheric removal program and MODTRAN that derive parameters from the data themselves to convert radiance to reflectance. Hybrid methods have been developed to augment the model calculations to provide better quality reflectance data. The model methods are computing intensive and,therefore, there is interest in developing more rapid methods to correct the data to reflectance. A parametric technique described here is in the early stages of development and could provide a breakthrough in speed of correction.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The hyperspectral data and information collection experiment obtained data over the Cuprite Mining District to determine the ability of the system to identify sub-pixel targets. Positive results were obtained for targets of crushed dolomite placed at the base of Kaolinite Hill on top of visibly similar soil. Equally positive results were obtained for Mylar targets placed on Stonewall Playa, with a 5 percent target being detected. However when a dolomite target on the playa was being sought, the Mylar target was also detected. The reasons for this spurious detection are discussed.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A radiometrically stable, commercially available spectroradiometer was used in conjunction with a simple, custom-designed telescope to make spectrally continuous measurements of solar spectral transmittance and directly transmitted solar spectral irradiance. The wavelength range of the instrument is 350-2500 nm and the resolution is 3-11.7 nm. Laboratory radiometric calibrations show the instrument to be stable to better than 1.0% over a nine-month period. The instrument and telescope are highly portable, can be set up in a matter of minutes, and can be operated by one person. A method of absolute radiometric calibration that can be tied to published top-of-the-atmosphere (TOA) solar spectra in valid Langley channels as well as regions of strong molecular absorption is also presented. High-altitude Langley plot calibration experiments indicate that this technique is limited ultimately by the current uncertainties in the TOA solar spectra, approximately 2-3%. Example comparisons of measured and modtran-modeled direct solar irradiance show that the model can be parameterized to agree with measurements over the large majority of the wavelength range to the 3% level for the two example cases shown. Side-by-side comparisons with a filter-based solar radiometer are in excellent agreement, with a mean absolute difference of tau = 0.0036 for eight overlapping wavelengths over three experiment days.