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Results for “remote sensing surface reflectance”

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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Cutting out the Middleman: Calibrating and Validating a Dynamic Vegetation Model (ED2-PROSPECT5) Using Remotely Sensed Surface Reflectance

Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.

Alexey N Shiklomanov↗
NASA NTRS2000

Towards GPS Surface Reflection Remote Sensing of Sea Ice Conditions

This paper describes the research to extend the application of Global Positioning System (GPS) signal reflections, received by airborne instruments, to cryospheric remote sensing. The characteristics of the GPS signals and equipment afford the possibility of new measurements not possible with existing radar and passive microwave systems. In particular, the GPS receiving systems are small and light-weight, and as such are particularly well suited to be deployed on small aircraft or satellite platforms with minimal impact. Our preliminary models and experimental results indicate that reflected GPS signals have potential to provide information on the presence and condition of sea and fresh-water ice as well as the freeze/thaw state of frozen ground. In this paper we show results from aircraft experiments over the ice pack near Barrow, Alaska suggesting correlation between forward scattered GPS returns and RADARSAT backscattered signals.

Komjathy, A.↗
NASA NTRS1984

Atmospheric effects on remote sensing of surface reflectance

This paper reviews the atmospheric effects on remote sensing of surface reflectance. The scattering and absorption of sunlight by atmospheric molecules and aerosols affects the quality of images of the surface remotely sensed from satellites and aircrafts. The concentration and characteristics of the atmospheric aerosols vary from place to place and vary with time. The effect of atmospheric aerosols on the upward radiance depends on their optical thickness, scattering phase function and absorption. These parameters result from the aerosol concentration, composition, and the relative humidity. For high resolution images the aerosol scale height is also of importance. The radiative transfer theory that predicts the atmospheric radiances for a given surface and atmosphere is a well established theory for the case of uniform surfaces (or low resolution data). Some radiative transfer models exist for nonuniform surfaces and others are being developed. Recent field experiment and laboratory simulation data confirm the need for these models and can be used for their testing. It is shown that the atmospheric effect reduces the apparent resolution of satellite imagery and causes errors in the classification of surface fields. Suggestions for correction procedures are given. Such corrections can be based on ground observations, on satellite radiances above dark areas, or on climatologic information, depending on the accuracy of the corrections needed. The chosen correction algorithm depends also on the image resolution and the specific remote sensing application.

Kaufman, Y. J.↗
NASA NTRS1986

Non-Lambertian effects on remote sensing of surface reflectance and vegetation index

This paper discusses the effects of non-Lambertian reflection from a homogeneous surface on remote sensing of the surface reflectance and vegetation index from a satellite. Remote measurement of the surface characteristics is perturbed by atmospheric scattering of sun light. This scattering tends to smooth the angular dependence of non-Lambertian surface reflectances, an effect that is not present in the case of Lambertian surfaces. This effect is calculated to test the validity of a Lambertian assumption used in remote sensing. For the three types of vegetations considered in this study, the assumption of Lambertian surface can be used satisfactorily in the derivation of surface reflectance from remotely measured radiance for a view angle outside the backscattering region. Within the backscattering region, however, the use of the assumption can result in a considerable error in the derived surface reflectance. Accuracy also deteriorates with increasing solar zenith angle. The angular distribution of the surface reflectance derived from remote measurements is smoother than that at the surface. The effect of surface non-Lambertianity on remote sensing of vegetation index is very weak. Since the effect is similiar in the visible and near infrared part of the solar spectrum for the vegetations treated in this study, it is canceled in deriving the vegetation index. The effect of the diffuse skylight on surface reflectance measurements at ground level is also discussed.

Lee, T. Y.↗
NASA NTRS2000

Sea Ice Remote Sensing Using Surface Reflected GPS Signals

This paper describes a new research effort to extend the application of Global Positioning System (GPS) signal reflections, received by airborne instruments, to cryospheric remote sensing. Our experimental results indicate that reflected GPS signals have potential to provide information on the presence and condition of sea and freshwater ice as well as the freeze/thaw state of frozen ground. In this paper we show results from aircraft experiments over the ice pack near Barrow, Alaska indicating correlation between forward-scattered GPS returns and RADARSAT backscattered measurements.

Komjathy, Attila↗
NASA NTRS1982

Solution of the equation of radiative transfer for remote sensing over nonuniform surface reflectivity

An understanding of radiative transfer in the earth's atmosphere is a necessity for the remote sensing of surface reflectivity from satellites and aircraft. The range of the adjacency effect, which represents the effect of bright areas on the radiance above dark areas, is the main parameter that distinguishes atmospheric radiative transfer over a nonuniform surface from that over a uniform one. A radiative transfer model which expresses this range correctly is, therefore, critical for developing remote sensing methods for the case of an atmosphere over a nonuniform surface. The present investigation is concerned with the development of a new approximate solution of the radiative transfer (RT) equation. The solution is not limited to nonabsorbing atmospheres, but it will still be limited to nadir observations. The results compare favorably with Monte Carlo simulations.

Kaufman, Y. J.↗
NASA NTRS1974

Effects of leaf age within growth stages of pepper and sorghum plants on leaf thickness, water, chlorophyll, and light reflectance

Pepper and sorghum plants (characterized by porous and compact leaf mesophylls, respectively) were used to study the influence of leaf age on light reflectance. Measurements were limited to the upper five nodal positions within each growth stage, since upper leaves make up most of the reflectance surfaces remotely sensed. The increase in leaf thickness and water content with increasing leaf age was taken into consideration, since each of these factors affects the reflectance as well as the selection of spectral wavelength intervals for optimum discrimination of vegetation.

Gausman, H. W.↗
NASA NTRS1992

Remote sensing of surface hemispherical reflectance (albedo) using pointable multispectral imaging spectroradiometers

Remote techniques for determining albedo are examined in terms of the range of view angles required in the use of string techniques with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the High Resolution Imaging Spectroradiometer (HIRIS). Ground data are used to compute full and half strings out to 15, 30, 45, and 60 degrees for various sun angles and ground cover types. A knowledge-based system is employed to evaluate both the visible and near-IR bands, and the results indicate errors of up to 7 percent for the MODIS data, HIRIS data, and the full-string +/- 60 degrees. In the cases of large extrapolations greater ranges of error are noted indicating that 60-deg systems are most effective. The error is increased in the case of sensor systems that only view in the fore or aft direction, and the MODIS full string for +/- 45 deg is also considered a good system.

Kimes, D. S.↗
NASA NTRS2022

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the “long” near infrared band (NIRL) used for atmospheric correction. For this band, the prelaunch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua RRS time series is quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP) RRS to NIRL calibration and compared our results to previous MODIS/Aqua and Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS)/OrbView2 analysis. In doing so, we note that g(NIRL) sensitivities of mission-averaged RRS time series are lower for VIIRS and Sea-WiFS, relative to MODIS. At the scale of monthly climatologies (MCs), however, all sensors show prominent g(NIRL) sensitivity with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-tonoise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations [e.g., using simultaneous same view (SSV) measurements] toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS. Index Terms—Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua, ocean color, ocean gyres, Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS), system vicarious calibration (SVC), Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP).

MODIS↗
NASA NTRS2022

Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the ‘long’ near infrared band (NIRL) used for atmospheric correction. For this band, the pre-launch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that MODIS/Aqua RRS time series are quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of VIIRS/SNPP RRS to NIRL calibration, and compared our results to previous MODIS/Aqua and SeaWiFS/OrbView2 analysis. In doing so, we note g(NIRL) sensitivities of mission-averaged RRS timeseries are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies, however, all sensors show prominent g(NIRL) sensitivity, with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on g(NIRL) sensitivity. As such, g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations (e.g., using simultaneous same view measurements) toward minimizing NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in RRS.

Brian B Barnes↗
NASA NTRS2019

Surface Polarized Reflectance Analysis For Aerosol Remote Sensing

We study the Earth surface polarized reflectance using data collected by a space-based lidar. Accurate modelling of the surface reflectance supports retrieval algorithm development for the current and future Earth Science missions. Strong polarization of the laser light from Cloud-Aerosol Transport System (CATS) instrument, operated in 2015-2017, and nighttime measurements yield higher signal-to-noise ratio for polarization compared to the previous analysis of reflected, initially unpolarized, solar light.

Earth surface polarized reflectance↗
NASA NTRS2019

Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the more than 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4 percent Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36 percent higher reflectance values in the visible bands compared to aquatic methods (ACOLITE (Atmospheric Correction for OLI (Operational Land Imager) 'lite') and SeaDAS (Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System)) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 per steraradian) compared to Landsat-8 (0.001 per steraradian). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59 percent and 35 percent respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 5–31 percent depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance.

Landsat-8↗
NASA NTRS1999

Angular and Seasonal Variation of Spectral Surface Reflectance Ratios: Implications for the Remote Sensing of Aerosol over Land

We obtain valuable information on the angular and seasonal variability of surface reflectance using a hand-held spectrometer from a light aircraft. The data is used to test a procedure that allows us to estimate visible surface reflectance from the longer wavelength 2.1 micrometer channel (mid-IR). Estimating or avoiding surface reflectance in the visible is a vital first step in most algorithms that retrieve aerosol optical thickness over land targets. The data indicate that specular reflection found when viewing targets from the forward direction can severely corrupt the relationships between the visible and 2.1 micrometer reflectance that were derived from nadir data. There is a month by month variation in the ratios between the visible and the mid-IR, weakly correlated to the Normalized Difference Vegetation Index (NDVI). If specular reflection is not avoided, the errors resulting from estimating surface reflectance from the mid-IR exceed the acceptable limit of DELTA-rho approximately 0.01 in roughly 40% of the cases, using the current algorithm. This is reduced to 25% of the cases if specular reflection is avoided. An alternative method that uses path radiance rather than explicitly estimating visible surface reflectance results in similar errors. The two methods have different strengths and weaknesses that require further study.

Remer, L. A.↗
NASA NTRS1984

Characterization of Surface Reflectance Variation Effects on Remote Sensing

The use of Monte Carlo radiative transfer codes to simulate the effects on remote sensing in visible and infrared wavelengths of variables which affect classification is examined. These variables include detector viewing angle, atmospheric aerosol size distribution, aerosol vertical and horizontal distribution (e.g., finite clouds), the form of the bidirectional ground reflectance function, and horizontal variability of reflectance type and reflectivity (albedo). These simulations are used to characterize the sensitivity of observables (intensity and polarization) to variations in the underlying physical parameters both to improve algorithms for the removal of atmospheric effects and to identify techniques which can improve classification accuracy. It was necessary to revise and validate the simulation codes (CTRANS, ARTRAN, and the Mie scattering code) to improve efficiency and accommodate a new operational environment, and to build the basic software tools for acquisition and off-line manipulation of simulation results. Initial calculations compare cases in which increasing amounts of aerosol are shifted into the stratosphere, maintaining a constant optical depth. In the case of moderate aerosol optical depth, the effect on the spread function is to scale it linearly as would be expected from a single scattering model. Varying the viewing angle appears to provide the same qualitative effect as modifying the vertical optical depth (for Lambertian ground reflectance).

Pearce, W. A.↗