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At least 163 records · Page 9
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NASA NTRS2018

Comparisons of Bispectral and Polarimetric Retrievals of Marine Boundary Layer Cloud Microphysics: Case Studies Using a LES-Satellite Retrieval Simulator

Many passive remote-sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological data sets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g., POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration and atmospheric correction. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote-sensing retrieval simulator. Created by coupling a large-eddy simulation (LES) cloud model with a 1-D radiative transfer model, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison) but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator, we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are highly correlated with one another within expected observational uncertainties. The relatively small systematic biases at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval but generally maintains a constant mean value.

Miller, Daniel J.↗
NASA NTRS2018

Comparisons of Bispectral and Polarimetric Retrievals of Marine Boundary Layer Cloud Microphysics: Case Studies Using a LES- Satellite Retrieval Simulator

Many passive remote-sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological data sets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g., POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration and atmospheric correction. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote-sensing retrieval simulator. Created by coupling a large-eddy simulation (LES) cloud model with a 1-D radiative transfer model, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison) but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator, we are able to verify that at high spatial resolution (50m) the bispectral and polarimetric retrievals are highly correlated with one another within expected observational uncertainties. The relatively small systematic biases at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval but generally maintains a constant mean value.

Miller, Daniel J.↗
NASA NTRS1972

Ocean color determination through a scattering atmosphere

Measurements made of the surface level albedo for ocean water containing various concentrations of phytoplankton indicate a strong correlation between wavelength dependent albedo ratios and phytoplankton chlorophyll concentration. To sense surface level albedo ratios from space platforms it is necessary to correct for the scattering and absorption properties of the atmosphere for the wavelengths in question. Atmospheric scattering models were constructed to calculate corrections at two wavelengths, 0.46 and 0.54 millimicrons. Assuming a natural background uncertainty in the aerosol optical depth of 0.1, it is found that the chlorophyll concentration may be determined to within one standard deviation of from 0.5 to 2.5 milligrams per cubic meter. By remotely sensing the aerosol optical depth to a greater accuracy it appears feasible to detect chlorophyll concentrations to uncertainty approaching 0.1 milligram per cubic meter.

Curran, R. J.↗
NASA NTRS2021

Bias-Correcting Carbon Fluxes Derived From Land-Surface Satellite Data for Retrospective and Near Real-Time Assimilation Systems

The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide(CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital in-put datasets, e.g., inventories, in situ measurements, and re-processed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth’s surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases inCO2which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal fore-casts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA’s Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

CO2↗
NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗
NASA NTRS2012

Low Noise Amplifiers and Receivers for Remote Sensing

The study of atmospheric dynamics and climatology depend on accurate and frequent measurements of temperature and humidity profiles of the atmosphere. These measurements furthermore enable highly accurate measurements of ocean topography by providing total column water vapour data for radar path delay correction. The atmospheric temperature profile is characterized at the oxygen molecule absorption frequencies (60 and 118 GHz) and the humidity profile at the water molecule absorption frequencies (23 and 183 GHz). Total column measurements can be achieved by comparing measured radiometric temperatures at atmospheric window channels, such as 90, 130, and 166 GHz. The standard receiver technology for these frequencies was diode mixers with MMIC LNAs being applied at the lower frequencies. The sensitivity of millimeter wave receivers improved significantly with the introduction of the low noise 35 nm gate length InP MMIC amplifiers. We currently achieve 3 dB noise figure at 180 GHz and 2 dB noise figure at 90 GHz with our MMIC low noise amplifiers (LNAs) in room temperature. These amplifiers and the receivers we have built using them made it possible to conduct highly accurate airborne measurements campaigns from the Global Hawk unmanned aerial vehicle, develop millimeter wave internally calibrated radiometers for altimeter radar path delay correction, and build prototypes of large arrays of millimeter receivers for a geostationary interferometric sounder. We use the developed millimeter wave receivers to measure temperature and humidity profiles in the atmosphere and in hurricanes as well as to characterize the path delay error in ocean topography alitmetery.

low noise temperature↗
NASA NTRS2011

Low Noise Amplifiers and Receivers for Remote Sensing

The study of atmospheric dynamics and climatology depend on accurate and frequent measurements of temperature and humidity profiles of the atmosphere. These measurements furthermore enable highly accurate measurements of ocean topography by providing total column water vapour data for radar path delay correction. The atmospheric temperature profile is characterised at the oxygen molecule absorption frequencies (60 and 118 GHz) and the humidity profile at the water molecule absorption frequencies (23 and 183 GHz). Total column measurements can be achieved by comparing measured radiometric temperatures at atmospheric window channels, such as 90, 130 and 166 GHz. The standard receiver technology for these frequencies was diode mixers with MMIC LNAs being applied at the lower frequencies. The sensitivity of millimetre wave receivers improved significantly with the introduction of the low noise 35 nm gate length InP MMIC amplifiers. We currently achieve 3 dB noise figure at 180 GHz and 2 dB noise figure at 90 GHz with our MMIC low noise amplifiers (LNAs) in room temperature. These amplifiers and the receivers we have built using them made it possible to conduct highly accurate airborne measurement campaigns from the Global Hawk unmanned aerial vehicle, develop millimeter wave internally calibrated radiometers for altimeter radar path delay correction, and build prototypes of large arrays of millimeter receivers for a geostationary interferometric sounder. We use the developed millimeter wave receivers to measure temperature and humidity profiles in the atmosphere and in hurricanes as well as to characterize the path delay error in ocean topography altimetry.

low noise amplifier↗
NASA NTRS2012

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

Malakar, Nabin K.↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRS1984

Characterizing the scientific potential of satellite sensors

Analytical and programming support is to be provided to characterize the potential of the LANDSAT thematic mapper (TM) digital imagery for scientific investigations in the Earth sciences and in terrestrial physics. In addition, technical support to define lower atmospheric and terrestrial surface experiments for the space station and technical support to the Research Optical Sensor (ROS) study scientist for advanced studies in remote sensing are to be provided. Eleven radiometric calibration and correction programs are described. Coherent noise and bright target saturation correction are discussed along with image processing on the LAS/VAX and Hp-300/IDIMS. An image of San Francisco, California from TM band 2 is presented.

Source record↗
NASA NTRS1991

Satellite remote sensing of surface energy and mass balance - Results from FIFE

Results obtained from the FIFE experiments conducted in 1987 and 1989 are summarized. Data analyses indicate that the hypotheses linking energy balance components to surface biology and remote sensing are reasonable at a point level, and that satellite remote sensing can potentially provide useful estimates of the surface energy budget. An investigation of atmospheric scattering and absorption effects on satellite remote sensing of surface radiance shows that the magnitude of atmospheric opacity variations within the FIFE site and with season can have a large effect on satellite measured values of surface radiances. Comparisons of atmospherically corrected TM radiances with surface measured radiances agreed to within about two percent at the visible and near-infrared wavelengths and to 6 percent in the midinfrared.

Hall, F. G.↗
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 NTRS1994

(abstract) Tropospheric Calibration for the Mars Observer Gravity Wave Experiment

In spring 1993, microwave radiometer-based tropospheric calibration was provided for the Mars Observer gravitational wave search. The Doppler shifted X-band radio signals propagating between Earth and the Mars Observer satellite were precisely measured to determine path length variations that might signal passage of gravitational waves. Experimental sensitivity was restricted by competing sources of variability in signal transit time. Principally, fluctuations in the solar wind and ionospheric plasma density combined with fluctions in tropospheric refractivity determined the detection limit. Troposphere-induced path delay fluctions are dominated by refractive changes caused by water vapor inhomogeneities blowing through the signal path. Since passive microwave remote sensing techniques are able to determine atmospheric propagation delays, radiometer-based tropospheric calibration was provided at the Deep Space Network Uranus tracking site (DSS-15). Two microwave water vapor radiometers (WVRs), a microwave temperature profiler (MTP), and a ground based meterological station were deployed to determine line-of-sight vapor content and vertical temperature profile concurrently with Mars Observer tracking measurements. This calibration system provided the capability to correct Mars Observer Doppler data for troposphere-induced path variations. We present preliminary analysis of the Doppler and WVR data sets illustrating the utility of WVRs to calibrate Doppler data. This takes an important step toward realizing the ambitious system required to support future Ka-band Cassini satellite gravity wave tropospheric calibration system.

atmospheric water vapor microwave remote sensing↗
NASA NTRS1984

Spatial variation of sea surface temperature and flux-related parameters measured from aircraft in the JASIN experiment

Spatial variation of some parameters measured on two aircraft flying 100-km box and 200-km triangular patterns at low levels in the atmospheric boundary layer during the Joint Air Sea Interaction Experiment in the North Atlantic was studied. The variation should be representative of summer conditions in mid-latitude oceans. The variance density of remotely sensed sea surface temperature, corrected for sky reflection, is found to depend on the one-dimensional wave number raised to the power of approximately -5/3. Nonuniform clouds add low-frequency variance to observations of a downward looking radiometer and result in steeper slope of the spectra of uncorrected sea surface temperature. Turbulent fluxes of momentum, sensible heat, and moisture were determined with the bulk formulae from the parameters (wind speed, temperature, specific humidity, and sea surface temperature) measured from the aircraft. The averages of these fluxes over each flight leg were compared with the fluxes determined from the parameters averaged over the same leg. The difference is negligible, showing that spatially averaged observations, such as those from spaceborne sensors, can be used in the bulk formulae to evaluate the fluxes.

Liu, W. T.↗
NASA NTRS2019

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency's (ESA's) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from theArgo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS.We also identify the impact of the corrections for atmospheric effectsrecently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

SSS↗
NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗
NASA NTRS1992

Surface reflectance retrieval from satellite and aircraft sensors - Results of sensors and algorithm comparisons during FIFE

Visible to shortwave infrared radiometric data collected by a number of remote sensing instruments on aircraft and satellite platforms were compared over common areas in the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site on August 4, 1989, to assess their radiometric consistency and the adequacy of atmospheric correction algorithms. The instruments in the study included the Landsat 5 Thematic Mapper (TM), the SPOT 1 high-resolution visible (HRV) 1 sensor, the NS001 Thematic Mapper simulator, and the modular multispectral radiometers (MMRs). Atmospheric correction routines analyzed were an algorithm developed for FIFE, LOWTRAN 7, and 5S. A comparison between corresponding bands of the SPOT 1 HRV 1 and the Landsat 5 TM sensors indicated that the two instruments were radiometrically consistent to within about 5 percent. Retrieved surface reflectance factors using the FIFE algorithm over one site under clear atmospheric conditions indicated a capability to determine near-nadir surface reflectance factors to within about 0.01 at a reflectance of 0.06 in the visible (0.4-0.7 microns) and about 0.30 in the near infrared (0.7-1.2 microns) for all but the NS001 sensor. All three atmospheric correction procedures produced absolute reflectances to within 0.005 in the visible and near infrared. In the shortwave infrared (1.2-2.5 microns) region the three algorithms differed in the retrieved surface reflectances primarily owing to differences in predicted gaseous absorption. Although uncertainties in the measured surface reflectance in the shortwave infrared precluded definitive results, the 5S code appeared to predict gaseous transmission marginally more accurately than LOWTRAN 7.

Markham, B. L.↗
NASA NTRS1992

A field measure of the shade fraction

'Shade' has a technical definition peculiar to linear spectral mixture analysis of imaging spectrometer data: it is the reduction in radiance from a surface due to lighting conditions and geometry, and includes topographic shading described by photometric functions as well as shadowing at all scales. 'Shade' is an important constituent of nearly all remotely sensed images, and is one endmember resolved in spectral mixture analysis, where it is represented as a fraction of the measured radiance and a characteristic spectrum. This spectrum is typically the null vector, provided the data have been corrected for atmospheric and instrument effects: i.e., 'shade' is the radiance from an ideal black surface. In topographic shading, irradiance is reduced - typically in proportion to cos(i), where i (incidence angle) is the angle between the sun and the local surface normal vectors. Therefore, the radiance is lowered by a multiplicative factor. Shadowing occurs when i is greater than 90 deg, or when sunlight is blocked by adjacent high terrain; the only irradiance is down-welling skylight and bounce light from adjacent terrain. In spectral mixture analysis, 'shade' is regarded as an additive term. In this regard, it is an accurate description of the proportion of a scene that consists of ideal shadows ('checkerboard mixing'); however, 'shade' represents the multiplicative cos(i) factor as well, as here it should be interpreted as the proportion of shadow that would darken the scene an equivalent amount. In either case, the 'shade' fraction is lessened by adjacency effects, because the scene has a non-zero reflectivity instead of the ideal black surface generally assumed.

Gillespie, Alan R.↗