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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Airborne laser ranging system for monitoring regional crustal deformation

Alternate approaches for making the atmospheric correction without benefit of a ground-based meteorological network are discussed. These include (1) a two-color channel that determines the atmospheric correction by measuring the time delay induced by dispersion between pulses at two optical frequencies; (2) single-color range measurements supported by an onboard temperature sounder, pressure altimeter readings, and surface measurements by a few existing meteorological facilities; and (3) inclusion of the quadratic polynomial coefficients as variables to be solved for along with target coordinates in the reduction of the single-color range data. It is anticipated that the initial Airborne Laser Ranging System (ALRS) experiments will be carried out in Southern California in a region bounded by Santa Barbara on the norht and the Mexican border on the south. The target area will be bounded by the Pacific Ocean to the west and will extend eastward for approximately 400 km. The unique ability of the ALRS to provide a geodetic 'snapshot' of such a large area will make it a valuable geophysical tool.

Degnan, J. J.↗
NASA NTRS2011

Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.

Hu, Chuanmin↗
NASA NTRS2016

The Sensitivity of SeaWiFS Ocean Color Retrievals to Aerosol Amount and Type

As atmospheric reflectance dominates top-of-the-atmosphere radiance over ocean, atmospheric correction is a critical component of ocean color retrievals. This paper explores the operational Sea-viewing Wide Field-of-View Sensor (SeaWiFS) algorithm atmospheric correction with approximately 13 000 coincident surface-based aerosol measurements. Aerosol optical depth at 440 nm (AOD(sub 440)) is overestimated for AOD below approximately 0.1-0.15 and is increasingly underestimated at higher AOD; also, single-scattering albedo (SSA) appears overestimated when the actual value less than approximately 0.96.AOD(sub 440) and its spectral slope tend to be overestimated preferentially for coarse-mode particles. Sensitivity analysis shows that changes in these factors lead to systematic differences in derived ocean water-leaving reflectance (Rrs) at 440 nm. The standard SeaWiFS algorithm compensates for AOD anomalies in the presence of nonabsorbing, medium-size-dominated aerosols. However, at low AOD and with absorbing aerosols, in situ observations and previous case studies demonstrate that retrieved Rrs is sensitive to spectral AOD and possibly also SSA anomalies. Stratifying the dataset by aerosol-type proxies shows the dependence of the AOD anomaly and resulting Rrs patterns on aerosol type, though the correlation with the SSA anomaly is too subtle to be quantified with these data. Retrieved chlorophyll-a concentrations (Chl) are affected in a complex way by Rrs differences, and these effects occur preferentially at high and low Chl values. Absorbing aerosol effects are likely to be most important over biologically productive waters near coasts and along major aerosol transport pathways. These results suggest that future ocean color spacecraft missions aiming to cover the range of naturally occurring and anthropogenic aerosols, especially at wavelengths shorter than 440 nm, will require better aerosol amount and type constraints.

single scattering albedo↗
NASA NTRS2001

SIMBIOS Sun Photometer Program

Aerosol optical thickness (AOT) values determined from the satellite ocean color data provide useful information on the spatial and temporal distributions of aerosols and are by-products of the atmospheric corrections required to estimation of water-leaving radiances. The Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) project is using in situ atmospheric data, primarily from sun photometers, for several purposes including: (1) validation the SeaWiFS and other ocean color mission aerosol optical products, e.g., AOT and Angstrom exponent; (2) evaluation of the aerosol models currently used for atmospheric corrections; and (3) development of vicarious sensor calibration methodologies, especially for the near-infrared bands where in situ water-leaving radiance data in the visible from sites like the Marine Optical Buoy cannot be used. The principal source of in situ aerosol observations was the Aerosol Robotic Network (AERONET).

McClain, Charles↗
NASA NTRS2019

Vicarious Calibration of GOCI for the SeaDAS Ocean Color Retrieval

A key on-orbit calibration step for satellite remote sensing of ocean color is the vicarious calibration. This establishes the final gains for each spectral band on the sensor that minimize bias in the retrieved ocean color signal. The vicarious calibration is specific to the instrument and the atmospheric correction algorithm. The vicarious calibration gains for the Geostationary Ocean Color Imager (GOCI) are presented here, which were derived to optimize the performance ofNASA's standard atmospheric correction algorithm as implemented in the l2gen code and distributed through the SeaDAS open-source software package. Following NASA's protocols, the near-infrared(NIR) bands were calibrated first, and the visible bands were then calibrated relative to this fixed NIR calibration. The gain for the 745-nm NIR band was derived using a fixed aerosol model, which waschosen based on the Angstrom Coefficients derived from MODIS onAqua (MODISA). For the vicarious gains of the visible bands, twosources for the target water-leaving radiances were tested: matchupsfrom MODISA and climatological data from SeaWiFS. A validation analysis using AERONET-OC data shows an improvement in sensor performance when compared with results using the current vicarious gains and results using no vicarious calibration. Good agreement was found in vicarious gains derived using both concurrent MODISA and climatological SeaWiFS as vicarious calibration data sources. These results support the use of a concurrent sensor for the vicarious calibration when in situ data are not available and demonstrate that using climatology from a well-calibrated sensor like SeaWiFSfor the vicarious calibration is a valid alternative when it is not possible to use a concurrent sensor or in situ data. We recommend using the gains derived from concurrent GOCI matchups with MODISA for GOCI processing in SeaDAS/l2gen.

Concha, Javier↗
NASA NTRS2001

Sun and Sky Radiance Measurements and Data Analysis Protocols

This chapter is concerned with two types of radiometric measurements essential to verify atmospheric correction algorithms and to calibrate vicariously satellite ocean color sensors. The first type is a photometric measurement of the direct solar beam to determine the optical thickness of the atmosphere. The intensity of the solar beam can be measured directly, or obtained indirectly from measurements of diffuse global upper hemispheric irradiance. The second type is a measurement of the solar aureole and sky radiance distribution using a CCD camera, or a scanning radiometer viewing in and perpendicular to the solar principal plane. From the two types of measurements, the optical properties of aerosols, highly variable in space and time, can be derived. Because of the high variability, the aerosol properties should be known at the time of satellite overpass. Atmospheric optics measurements, however, are not easy to perform at sea, from a ship or any platform. This complicates the measurement protocols and data analysis. Some instrumentation cannot be deployed at sea, and is limited to island and coastal sites. In the following, measurement protocols are described for radiometers commonly used to measure direct atmospheric transmittance and sky radiance, namely standard sun photometers, fast-rotating shadow-band radiometers, automated sky scanning systems, and CCD cameras. Methods and procedures to analyze and quality control the data are discussed, as well as proper measurement strategies for evaluation of atmospheric correction algorithms and satellite-derived ocean color.

Frouin, Robert↗
NASA NTRS2003

Atmospheric Pressure Corrections in Geodesy and Oceanography: a Strategy for Handling Air Tides

Global pressure data are often needed for processing or interpreting modern geodetic and oceanographic measurements. The most common source of these data is the analysis or reanalysis products of various meteorological centers. Tidal signals in these products can be problematic for several reasons, including potentially aliased sampling of the semidiurnal solar tide as well as the presence of various modeling or timing errors. Building on the work of Van den Dool and colleagues, we lay out a strategy for handling atmospheric tides in (re)analysis data. The procedure also offers a method to account for ocean loading corrections in satellite altimeter data that are consistent with standard ocean-tide corrections. The proposed strategy has immediate application to the on-going Jason-1 and GRACE satellite missions.

Ponte, Rui M.↗
NASA NTRS1994

High spectral resolution in the solar spectrum

A session dedicated to high spectral resolution in the solar spectrum, covering topics of calibration, atmospheric correction, geology/pedology, inland water, and vegetation, is reported. The session showed a high degree of diversity in the topics and the approaches used. It was highlighted that high spectral resolution data could provide atmospherically corrected ground level calibrated reflectance values. Important advances were shown in the use of radiative transfer models applied either on water bodies or vegetation. Several studies highlighted the high degree of redundancy contained in high spectral resolution data.

Baret, F.↗
NASA NTRS1994

Retrieval of Surface Directional Reflectance Properties Using Ground Level Multiangle Measurements

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversion. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directional-hemispherical reftectances (albedos)from multiangle radiance measurements taken at ground level. The retrieval schemes are tested on simulated data incorporating realistic surface BRFs and atmospheric models containing aerosols. Sensitivity of the atmospherically corrected BRFs and associated directional-hemispherical reflectances to various aerosol properties and the sun-view geometry is illustrated. A measurement strategy for obtaining highly accurate surface reflectance properties also is examined in the context of instrument radiametric calibration, knowledge of the atmospheric properties, and sun-view angular coverage.

Martonchik, John V.↗
NASA NTRS2017

Hyperspectral Radiative Transfer Modeling to Explore the Combined Retrieval of Biophysical Parameters and Canopy Fluorescence from FLEX - Sentinel-3 Tandem Mission Multi-Sensor Data

The FLuorescence EXplorer (FLEX) satellite mission, selected as ESA's 8th Earth Explorer, has been designed forthe measurement of sun-induced fluorescence (F) spectra emitted by plants. This will be accomplished through amulti-sensor approach by placing it in a common orbit in tandem with the Sentinel-3 (S3) mission, which willhave two optical sensors on board, OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land SurfaceTemperature Radiometer) to complement FLEX. These S3 instruments will be used in combination with theimaging spectrometers on board FLEX to provide data useful for atmospheric correction of FLEX data. However,a fully synergetic approach, i.e. by exploiting the spectral and directional information from all tandem missioninstruments together, is an attractive alternative which is explored in this paper. By employing all combined topof-atmosphere (TOA) spectral radiance data, one can (i) characterize the relevant optical properties of the atmosphere,(ii) retrieve biophysical canopy properties including the associated reflectance anisotropy, and (iii)retrieve a more accurate and consistent canopy F.Regarding retrieval methods, Fraunhofer Line Depth (FLD) and Spectral Fitting (SF) are well-known techniquesapplied to hyperspectral data. Both methods depend on a high spectral resolution and assume aLambertian (isotropic) canopy reflectance. However, most vegetation canopies are non-Lambertian. This impliesthat, in particular when ignoring the anisotropic surface reflection, substantial retrieval errors can occur due tothe interaction between atmospheric absorption bands and surface reflectance anisotropy. In this paper, a novelmethod based on spectral radiative transfer (RT) modeling is proposed, in which coupled RT models are used tosimulate TOA radiance spectra. These are then matched with ‘measured' spectra in order to retrieve surfacefluorescence, along with a suite of biophysical parameters, by model inversion through optimization. By applyingcoupled RT models of the soil-leaf-canopy and the surface-atmosphere systems, TOA radiance spectra canbe simulated for all optical sensors of this tandem mission. In this way, complex effects due to surface reflectanceanisotropy and the spectral sampling by the various instruments, which are difficult to compensate for in the endproducts, are properly taken into account by their incorporation in the forward modeling. Next, by model inversionof TOA radiance data via optimization, the most accurate F retrievals can be achieved in a consistentmanner, along with important canopy level biophysical parameters that may help interpret the F spectrum, suchas chlorophyll content and leaf area index (LAI). The potential of this approach has been explored in a numericalexperiment, and the results are presented in this paper. We find that, with the assumed well-characterized andplausible FLEX/S3 instrument performances, the simultaneous retrieval of biophysical canopy parameters and Fspectra would be possible with a remarkable accuracy, provided the correct atmospheric characterization isavailable.

Verhoef, Wouter↗
NASA NTRS2021

Seasonal Bias in Global Ocean Color Observations

In this study we identify a seasonal bias in the ocean color satellite derived remote sensing reflectances (Rrs(λ); sr-1) at the ocean color validation site, MOBY (Marine Optical BuoY). The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including VIIRS (Visible Infrared Imaging Radiometer Suite), SeaWiFS (Sea-viewing Wide Field-of-view Sensor), and MODIS (Moderate Resolution Imaging Spectrometer). The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder) satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs (λ) and we recommend more simulations to discern these influence Rrs (λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are or will be affected until a solution is implemented.

satellite ocean color↗
NASA NTRSDate not supplied

Quantitative Assessment of the HLS Surface Reflectance Consistency

The Harmonized Landsat and Sentinel-2 (HLS) project produces compatible surface reflectance (SR) from observations acquired by Landsat-8/9 OLI and Sentinel-2A/2B MSI. The HLS harmonization procedures include atmosphere correction, cloud masking, view angle normalization, and bandpass adjustment. The objective of this study is to quantitatively assess the reflectance consistency between Landsat and Sentinel-2 within the Version 2 HLS data. We collected 545 pairs of same-day Landsat/Sentinel-2 images across the globe to represent a wide range of vegetation types and climate regimes. The mean absolute difference (MAD) in reflectance between Landat and Sentinel-2 was calculated as a consistency indicator for each harmonization step. The MAD generally increased after the atmosphere correction, and then greatly decreased after the BRDF and bandpass adjustments, to smaller than the top-of-atmosphere MAD values. The MAD ranged from 0.0048 to 0.0093 for the six common spectral bands (blue, green, red, NIR, SWIR1, and SWIR2) in the final products, only slightly greater than the difference between Landsat and Sentinel-2 calibrations.. An evaluation on a few commonly used vegetation indices also showed good agreement between Landsat and Sentinel-2 reflectance. All these evaluations demonstrate that the HLS project produces a consistent SR dataset from Landsat-OLI and Sentinel-MSI, which will be a valuable resource for a wide range of remote sensing applications.

Qiang Zhou↗
NASA NTRS2013

Spatially Resolving Ocean Color and Sediment Dispersion in River Plumes, Coastal Systems, and Continental Shelf Waters

Satellite remote sensing of ocean color in dynamic coastal, inland, and nearshorewaters is impeded by high variability in optical constituents, demands specialized atmospheric correction, and is limited by instrument sensitivity. To accurately detect dispersion of bio-optical properties, remote sensors require ample signal-to-noise ratio (SNR) to sense small variations in ocean color without saturating over bright pixels, an atmospheric correction that can accommodate significantwater-leaving radiance in the near infrared (NIR), and spatial and temporal resolution that coincides with the scales of variability in the environment. Several current and historic space-borne sensors have met these requirements with success in the open ocean, but are not optimized for highly red-reflective and heterogeneous waters such as those found near river outflows or in the presence of sediment resuspension. Here we apply analytical approaches for determining optimal spatial resolution, dominant spatial scales of variability ("patches"), and proportions of patch variability that can be resolved from four river plumes around the world between 2008 and 2011. An offshore region in the Sargasso Sea is analyzed for comparison. A method is presented for processing Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra imagery including cloud detection, stray lightmasking, faulty detector avoidance, and dynamic aerosol correction using short-wave- and near-infrared wavebands in extremely turbid regions which pose distinct optical and technical challenges. Results showthat a pixel size of approx. 520 mor smaller is generally required to resolve spatial heterogeneity in ocean color and total suspended materials in river plumes. Optimal pixel size increases with distance from shore to approx. 630 m in nearshore regions, approx 750 m on the continental shelf, and approx. 1350 m in the open ocean. Greater than 90% of the optical variability within plume regions is resolvable with 500 m resolution, and small, but significant, differences were found between peak and nadir river flow periods in terms of optimal resolution and resolvable proportion of variability.

MODIS↗
NASA NTRS1976

Coastal zone classification from satellite imagery

The author has identified the following significant results. Studies of cover distribution along Delaware's coast, especially in tidal wetlands, were made, utilizing semi-automated analysis of LANDSAT-1 MSS digital data. Cover maps with eleven vegetation and other cover categories were produced with accuracy of identification above 80% in all categories. Recent studies have tested a new technique for training automated analysis which uses ground measured reflectance and atmospheric correction techniques to derive signatures for specific categories in preference to the relative radiance signatures derived from training sets within LANDSAT data itself. Initial tests using a four category scheme indicate that training data based on absolute measured reflectance and atmospheric correction of LANDSAT data can produce comparable accuracy of categorization to that achieved using more conventional relative radiance training. The analysis of the same four categories produced average categorization accuracies of 82.1% by conventional reflative radiance training and 81.9% by use of absolute reflectance signatures.

Klemas, V.↗
NASA NTRSDate not supplied

Fusing Surface BRDF from Geostationary and Polar-Orbiting Satellite Sensors

The bi-directional reflectance distribution function (BRDF) describes the fundamental optical property of a surface and therefore has been retrieved from both geostationary (GEO) and polar-orbiting (or Low-Earth Orbit, LEO) satellite observations. In theory, although GEO and LEO observations feature different illumination-view geometries, they reflect the same physical property and the retrieved BRDF should be mutually consistent. This fact also suggests that we may derive a better BRDF product by synergistically fusing the GEO and LEO datasets. Here we demonstrate the idea by fusing Terra/Aqua MODIS and GOES16/17 ABI surface BRDF with the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Our processing is implemented in two steps. First, we compile and project both MODIS and ABI data on the same GeoNEX common grid, and then use the MODIS MAIAC BRDF (MCD19A3) as the prior information to perform atmospheric correction of ABI data and retrieve surface BRDF with the ABI MAIAC code. In the second step, we run the MAIAC code in the forward mode to simulate MODIS and ABI TOA radiances from the jointly retrieved surface BRDF and the corresponding illumination-view geometries. By comparing the simulated TOA radiances with corresponding observations (e.g., MOD02 and ABI05), we can quantitatively evaluate whether the jointly retrieved ABI surface BRDF improves over the separately retrieved MODIS and ABI BRDF products. We expect that the jointly retrieved BRDF data are more robust than the standard products and can help us reduce uncertainties in higher-level earth observation satellite products.

Geostationary satellite↗
NASA NTRS1975

Coastal Zone Classification from Satellite Imagery

The author has identified the following significant results. Studies of cover distribution along Delaware's coast, especially in tidal wetlands, were made utilizing semi-automated analysis of LANDSAT-1 MSS digital data. Cover maps with eleven vegetation and other cover categories were produced with accuracy of identification above 80% in all categories. Recent studies have tested a new technique for training automated analysis which uses ground measured reflectance and atmospheric correction techniques to derive signatures for specific categories in preference to the relative radiance signatures derived from training sets within the LANDSAT data itself. Initial tests using a four category scheme indicate that training data based on absolute measured reflectance and atmospheric correction of LANDSAT data can produce comparable accuracy of categorization to that achieved using more conventional relative radiance training.

Klemas, V.↗
NASA NTRS2012

Applications of MODIS Fluorescent Line Height Measurements to Monitor Water Quality Trends and Algal Bloom Activity

Recent advances in satellite and airborne remote sensing, such as improvements in sensor and algorithm calibrations, processing techniques and atmospheric correction procedures have provided for increased coverage of remote-sensing, ocean-color products for coastal regions. In particular, for the Moderate Resolution Imaging Spectrometer (MODIS) sensor calibration updates, improved aerosol retrievals and new aerosol models has led to improved atmospheric correction algorithms for turbid waters and have improved the retrieval of ocean color in coastal waters. This has opened the way for studying ocean phenomena and processes at finer spatial scales, such as the interactions at the land-sea interface, trends in coastal water quality and algal blooms. Human population growth and changes in coastal management practices have brought about significant changes in the concentrations of organic and inorganic, particulate and dissolved substances entering the coastal ocean. There is increasing concern that these inputs have led to declines in water quality and have increase local concentrations of phytoplankton, which cause harmful algal blooms. In two case studies we present MODIS observations of fluorescence line height (FLH) to 1) assess trends in water quality for Tampa Bay, Florida and 2) illustrate seasonal and annual variability of algal bloom activity in Monterey Bay, California as well as document estuarine/riverine plume induced red tide events. In a comprehensive analysis of long term (2003-2011) in situ monitoring data and satellite imagery from Tampa Bay we assess the validity of the MODIS FLH product against chlorophyll-a and a suite of water quality parameters taken in a variety of conditions throughout a large optically complex estuarine system. A systematic analysis of sampling sites throughout the bay is undertaken to understand how the relationship between FLH and in situ chlorophyll-a responds to varying conditions and to develop a near decadal trend in water quality changes. In situ monitoring locations that correlated well with satellite imagery were in depths greater than seven meters and were located over five kilometers from shore. Water quality parameter of total nitrogen, phosphorous, turbidity and biological oxygen demand had high correlations with these sites, as well. Satellite FLH estimates show improving water quality from 2003-2007 with a slight decline up through 2011. Dinoflagellate blooms in Monterey Bay, California (USA) have recently increased in frequency and intensity. Nine years of MODIS FLH observations are used to describe the annual and seasonal variability of bloom activity within the Bay. Three classes of MODIS algorithms were correlated against in situ chlorophyll measurements. The FLH algorithm provided the most robust estimate of bloom activity. Elevated concentrations of phytoplankton were evident during the months of August-November, a period during which increased occurrences of dinoflagellate blooms have been observed in situ. Seasonal patterns of FLH show the on- and offshore movement of areas of high phytoplankton biomass between oceanographic seasons. Higher concentrations of phytoplankton are also evident in the vicinity of the land-based nutrient sources and outflows, and the cyclonic bay-wide circulation can transport these nutrients to the northern Bay bloom incubation region. Both of these case studies illustrate the utility MODIS FLH observations in supporting management decisions in coastal and estuarine waters.

Fischer, Andrew↗
NASA NTRS2017

Global Clear-Sky Surface Skin Temperature from Multiple Satellites Using a Single-Channel Algorithm with Angular Anisotropy Corrections

Surface skin temperature (T(sub s)) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve T(sub s) over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of T(sub s) over the diurnal cycle in non-polar regions, while polar T(sub s) retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed T(sub s), along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly T(sub s) observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived T(sub s) data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, T(sub s) validation with established references is essential, as is proper evaluation of T(sub s) sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based T(sub s) product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

Scarino, Benjamin R.↗