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At least 217 records · Page 12

Radiometric calibration of a polarization-sensitive sensor

The radiometric accuracy of a sensor is adversely affected by scene polarization if its optical system is sensitive to polarization. Tests performed on the reflective bands of the NS001 Thematic Mapper simulator, an aircraft multispectral scanner, show that it is very sensitive to the polarization state of the incoming radiations. For 100 percent linearly polarized light, errors in the measured intensity vary from -40 to +40 percent, depending on the scan angle and spectral band. To estimate polarization-induced errors in the intensity measured at aircraft level, the intensity and polarization of the atmospheric radiances were simulated using a realistic earth-atmosphere radiative transfer model. For the polarization of atmospheric radiances in the solar meridian plane over a vegetated target, intensity errors may range from -10 to + 10 percent. The polarization-induced errors are highest in the shortest NS001 spectral band (0.450-0.525 microns) because of large atmospheric polarizations contributed by Rayleigh particles and small diluting effects caused by the small contributions of weakly polarized radiations coming from aerosols and the surface. Depending on the illumination and view angles, the errors in derived surface reflectance due to the radiance errors can be very large. In particular, for large off-nadir view angles in the forward scattered direction when the sun is low, the relative errors in the derived surface reflectance can be as large as 4 to 5 times the relative error in the radiances. Polarization sensitivity errors cannot be neglected for the shorter wavelengths when the surface reflectance contribution to atmospheric radiances is very small.

Ahmad, Suraiya P.↗

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

54 ENVIRONMENTAL SCIENCES↗

Assessment of MODIS and VIIRS Calibration Consistency for Reflective Solar Bands Using Vicarious Approaches

The Moderate-Resolution Imaging Spectroradiometer (MODIS) is the key instrument of the NASA’s Earth Observing System (EOS) Terra and Aqua missions, launched in December 1999 and May 2002, respectively. The Visible Infrared Imaging Radiometer Suite (VIIRS) expands the MODIS legacy, launched onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in October 2011 and NOAA20 satellite in November 2017, respectively. The MODIS and VIIRS sensors have a similar design with spectrally matched reflective solar spectral bands (RSB). Their on-board calibration components include a solar diffuser and a solar diffuser stability monitor for RSB, a V-grooved blackbody for the thermal emissive bands (TEB), and a space view as a background reference. This study evaluates the calibration consistency of the Terra and Aqua MODIS RSB based on the current Collection 6.1 L1B data products generated by the NASA MODAPS, which have used consistent calibration coefficient look up tables (LUT) for the entire data-record. In the case of SNPP and NOAA20 VIIRS, the latest L1B data produced by NASA Land SIPS are used. Several independent vicarious approaches are used to examine the stability and consistency of the at-sensor reflectance among MODIS and VIIRS instruments. Vicarious approaches include observations from simultaneous nadir overpasses (SNO), the Libya-4 desert and Dome C snow sites, as well as deep convective clouds (DCC). Impact of existing band spectral difference on the reflectance is corrected using hyperspectral observations provided by Europe Space Agency’s SCIAMACHY sensor. Results of this study provide comprehensive assessments of calibration performance, radiometric agreement and associated uncertainties.

MODIS↗

Progress Towards a Common CERES Cloud Mask Algorithm for MODIS and VIIRS: Evaluation using CALIOP Data

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has endeavored to produce a long-term global climate data record for detecting changes in the Earth’s radiation budget and to improve understanding of how clouds contribute to those changes. CERES incorporates cloud information derived from passive narrowband satellite imaging radiometers, and over the course of the project different instruments have contributed to this effort, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS).Cloud properties have been derived from MODIS on the Aqua satellite for CERES since 2002,and VIIRS on the NOAA-20 satellite launched in 2017 will continue the cloud record once Aqua-MODIS reaches the end of its operational lifetime. However, MODIS and VIIRS have different spectral capabilities and characteristics which complicates our ability to seamlessly transition the record from MODIS to VIIRS. This study evaluates some of the recent algorithm modifications incorporated by the CERES Cloud Working Group towards developing a unified cloud mask algorithm for MODIS and VIIRS that utilizes a set of spectral bands common to both instruments. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products are used to evaluate MODIS and VIIRS regional cloud fraction estimates and identify areas where improvements can be made. The cloud mask algorithm relies on computed estimates of cloud-free top-of-atmosphere radiances to differentiate cloudy and cloud-free imager pixels. The unified cloud mask algorithm currently under development uses a reduced number of spectral bands common to both MODIS and VIIRS and so relies on the accuracy of these simulated cloud-free radiances more heavily than predecessor algorithms, i.e., the CERES Edition 4 cloud mask, which use as much spectral information as available on each imager with less consideration for cross-platform consistency. Our validation strategy using CALIOP data is also described. The CALIOP observations are critical for assessing cloud detection accuracies and for independently confirming cloud-free conditions which enables a more robust evaluation of the simulated cloud-free radiances for biases due to factors such as view angle and the spectral dependence of water vapor absorption. These effects are known to differ for the two satellite instruments. Comparisons of MODIS and VIIRS cloud fractions are presented in context with estimates from CALIOP and from the CERES Edition 4 cloud mask algorithm to gauge current progress in developing accurate and consistent MODIS and VIIRS cloud properties for CERES.

CERES↗

The Multispectral Atmospheric Mapping Sensor (MAMS): Instrument description, calibration and data quality

A new instrument has been developed to produce high resolution imagery in eight visible and three infared spectral bands from an aircraft platform. An analysis of the data and calibration procedures has shown that useful data can be obtained at up to 50 m resolution with a 2.5 milliradian aperture. Single sample standard errors for the measurements are 0.5, 0.2, and 0.9 K for the 6.5, 11.1, and 12.3 micron spectral bands, respectively. These errors are halved when a 5.0 milliradian aperture is used to obtain 100 m resolution data. Intercomparisons with VAS and AVHRR measurements show good relative calibration. MAMS development is part of a larger program to develop multispectral Earth imaging capabilities from space platforms during the 1990s.

Jedlovec, G. J.↗

SeaWiFS technical report series. Volume 9: The simulated SeaWiFS data set, version 1

Data system development activities for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) must begin well before the scheduled 1994 launch. To assist in these activities, it is essential to develop a simulated SeaWiFS data set as soon as possible. Realism is of paramount importance in this data set, including SeaWiFS spectral bands, orbital and scanning characteristics, and known data structures. Development of the simulated data set can assist in identification of problem areas that can be addressed and solved before the actual data are received. This paper describes the creation of the first version of the simulated SeaWiFS data set. The data set includes the spectral band, orbital, and scanning characteristics of the SeaWiFS sensor and SeaStar spacecraft. The information is output in the data structure as it is stored onboard. Thus, it is a level-0 data set which can be taken from start to finish through a prototype data system. The data set is complete and correct at the time of printing, although the values in the telemetry fields are left blank. The structure of the telemetry fields, however, is incorporated. Also, no account for clouds has been included. However, this version facilitates early prototyping activities by the SeaWiFS data system, providing a realistic data set to assess performance.

Gregg, Watson W.↗

Information content in Iris spectra

Spectra from the satellite instrument Iris (infrared interferometer spectrometer) were examined to find the number of independent variables needed to describe the broad-band high-resolution spectral data. The radiated power in the atmospheric window from 771 to 981 per cm was the first parameter chosen for fitting observed spectra. At succeeding levels of analysis, the residual variability (observed spectrum minus best-fit spectrum) in an ensemble of observations was partitioned into spectral eigenvectors. The eigenvector describing the largest fraction of this variability was examined for a strong spectral signature; the power in the corresponding spectral band was then used as the next fitting parameter. The measured power in nine spectral intervals, when it was inserted in the spectral-fitting functions, was adequate to describe most spectra to within the noise level of Iris. Considerations of relative signal strength and scales of atmospheric variability suggest a combination sounder (multichannel, broad field of view) scanner (window channel, small field of view) as an efficient observing instrument.

Price, J. C.↗

Remote sensing of benthic microalgal biomass with a tower-mounted multispectral scanner

A remote sensing instrument was mounted on a 50-ft tower overlooking North Inlet Estuary, South Carolina in order to conduct a remote sensing study of benthic microalgae. The instrument was programmed to take multispectral imagery data along a 90 deg horizontal frame in six spectral bands ranging from 400-1050 nm and had a ground resolution of about 3 cm. Imagery measurements were encoded in digital form on magnetic tape and were stored, decoded, and manipulated by computer. Correlation coefficients were calculated on imagery data and chlorophyll a concentrations derived from ground truth data. The most significant correlation occurred in the blue spectral band with numerical values ranging from -0.81 to -0.88 for three separate sampling periods. Mean values of chlorophyll a for a larger section of mudflat were estimated using regression equations. The scanner has provided encouraging results and promises to be a useful tool in sampling the biomass of intertidal benthic microalgae.

Jobson, D. J.↗

MISR radiometric camera-by-camera Cloud Mask (MIRCCM_V1)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The RCCM is derived from the radiance values, and is calculated independently for each camera. Therefore, the amount of apparent cloudiness will vary with view angle, with the oblique view angles generally being more cloudy than the near-nadir ones. Since the RCCM is calculated primarily from the radiance values, it does not work well over snow and ice and will usually confuse clear snow/ice with cloud. It works the best over clear-sky ocean, but other surface types are also of quite good quality. The RCCM product also contains a glint mask for each camera, and this mask is set to true whenever the scattering angles indicate that glint could be possible. This glint mask is not masked out over land; users must do this step themselves. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

SUN GLINT↗

MISR Geometric Parameters (MIB2GEOP_V1)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The MISR Level 1B2 Geometric Parameters data product is part of the georectified radiance product. It contains the geometric parameters which measure the sun and view angles at the reference ellipsoid. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=17.6 km; Longitude_Resolution=17.6 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day].

SOLAR AZIMUTH ANGLE↗

MISR Geometric Parameters (MIB2GEOP_V2)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The MISR Level 1B2 Geometric Parameters data product is part of the georectified radiance product. It contains the geometric parameters which measure the sun and view angles at the reference ellipsoid. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=17.6 km; Longitude_Resolution=17.6 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly].

AM-1↗

MISR radiometric camera-by-camera Cloud Mask (MIRCCM_V3)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The RCCM is derived from the radiance values, and is calculated independently for each camera. Therefore, the amount of apparent cloudiness will vary with view angle, with the oblique view angles generally being more cloudy than the near-nadir ones. Since the RCCM is calculated primarily from the radiance values, it does not work well over snow and ice and will usually confuse clear snow/ice with cloud. It works the best over clear-sky ocean, but other surface types are also of quite good quality. The RCCM product also contains a glint mask for each camera, and this mask is set to true whenever the scattering angles indicate that glint could be possible. This glint mask is not masked out over land; users must do this step themselves. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

CLOUD MASK↗

MISR radiometric camera-by-camera Cloud Mask (MIRCCM_V4)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The RCCM is derived from the radiance values, and is calculated independently for each camera. Therefore, the amount of apparent cloudiness will vary with view angle, with the oblique view angles generally being more cloudy than the near-nadir ones. Since the RCCM is calculated primarily from the radiance values, it does not work well over snow and ice and will usually confuse clear snow/ice with cloud. It works the best over clear-sky ocean, but other surface types are also of quite good quality. The RCCM product also contains a glint mask for each camera, and this mask is set to true whenever the scattering angles indicate that glint could be possible. This glint mask is not masked out over land; users must do this step themselves. [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

SUN GLINT↗

An empirical study of scanner system parameters

Parametric values (instantaneous field of view, number and location of spectral bands, signal-to-noise ratio, etc.) of a multispectral scanner (thematic mapper) are assigned empirically. An empirical simulation approach was used with candidate parameter values selected by heuristic means. Results obtained using a conventional maximum likelihood pixel classifier suggest that although the classification accuracy declines slightly as the instantaneous field of view (IFOV) is decreased this is more than made up by an improved mensuration accuracy. Use of a classifier involving both spatial and spectral features helps resist degradation as the signal-to-noise ratio is decreased. The importance of having at least one spectral band in each of the major available portions of the optical spectrum is demonstrated.

Landgrebe, D. A.↗

Use of ERTS Data for Mapping Snow Cover in the Western United States

The author has identified the following significant results. Results of the analysis of the initial sample of ERTS-1 data indicate that the MSS-5 spectral band is the most useful for detecting and mapping mountain snow cover. At the ERTS-1 resolution, snow cover can be readily detected in the MSS-5 band and can be distinguished from clouds. Snow line elevations have been mapped for five mountain areas. In one case for the Salt-Verde watershed in Arizona good agreement is observed between the location of the snow line as mapped from the ERTS-1 data and as depicted on an aerial snow survey chart compiled a week earlier. Examination of data from the Arctic has revealed that multispectral data can provide information on glacial conditions that cannot be ascertained from observations in a single spectral band.

Barnes, J. C.↗

Multispectral and thermal surface imagery and surface elevation mosaics (camspec-air)

This dataset contains high resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems, which have been processed for value added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures 6 spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation3. Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the 6 spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values. 1https://www.arm.gov/publications/tech_reports/handbooks/doe-sc-arm-tr-281.pdf 2https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Setup.html 3https://pubs.usgs.gov/of/2021/1039/ofr20211039.pdf

54 ENVIRONMENTAL SCIENCES↗

Multispectral and thermal surface imagery and surface elevation mosaics - SGP July 2022

This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance via custom code. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values. 1 https://www.arm.gov/publications/tech_reports/handbooks/doe-sc-arm-tr-281.pdf 2 https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Setup.html 3 https://pubs.usgs.gov/of/2021/1039/ofr20211039.pdf

54 ENVIRONMENTAL SCIENCES↗

A Change in the Solar He II EUV Global Network Structure as an Indicator of the Geo-Effectiveness of Solar Minima

Solar activity during 2007 - 2009 was very low, causing anomalously low thermospheric density. A comparison of solar extreme ultraviolet (EUV) irradiance in the He II spectral band (26 to 34 nm) from the Solar Extreme ultraviolet Monitor (SEM), one of instruments on the Charge Element and Isotope Analysis System (CELIAS) on board the Solar and Heliospheric Observatory (SOHO) for the two latest solar minima showed a decrease of the absolute irradiance of about 15 +/- 6 % during the solar minimum between Cycles 23 and 24 compared with the Cycle 22/23 minimum when a yearly running-mean filter was used. We found that some local, shorter-term minima including those with the same absolute EUV flux in the SEM spectral band show a higher concentration of spatial power in the global network structure from the 30.4 nm SOHO/Extreme ultraviolet Imaging Telescope (EIT) images for the local minimum of 1996 compared with the minima of 2008 - 2011.We interpret this higher concentration of spatial power in the transition region's global network structure as a larger number of larger-area features on the solar disk. These changes in the global network structure during solar minima may characterize, in part, the geo-effectiveness of the solar He II EUV irradiance in addition to the estimations based on its absolute levels.

Solar extreme ultraviolet irradiance↗