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Tracking Nonradial Outflows in Extreme Ultraviolet and White Light Solar Images

Understanding the solar corona requires knowledge of its dynamics through its various layers and subsequent connectivity to the heliosphere. This requires understanding the nature of the outflows and the physical transitions through the middle corona (∼1.5–6.0 Rs). While this region is still inaccessible to in situ measurements, remote sensing observations are available, but their interpretation can be controversial due to line-of-sight effects and the nonradial motion of outflowing structures close to the Sun (≤3.0 Rs). In this work, we describe a method to mitigate these challenges by generating nonradial height–time profiles of outflows by using advanced image processing techniques. The northern and southern boundaries of a large equatorial streamer during the 2008 solar minimum were identified in STEREO/SECCHI solar images, using two different methodologies based on thresholds of brightness and piecewise polynomial function fitting. To address line-of-sight issues, we used tomographic reconstruction of the 3D distribution of the coronal electron density based on SECCHI/COR2 images. Spectral analysis of the time series of the position angle of the streamer boundary revealed its oscillatory nature at some heights at 36–48 and 10.5–14.6 hr. Dividing the distance between the northern and southern streamer boundaries in equal parts at each height, we obtained nonradial height–time paths from which we generated nonradial profiles of corona/solar wind plasma outflow. We tracked outflows as they moved uninterruptedly from the Sun in EUVI, through COR1 and into COR2. Finally, we discuss the preliminary results of nonradial plane-of-sky velocities for a coronal mass ejection and two small-scale features.

Nathalia Alzate↗

Remotely Sensing Larval Population Dynamics of Rice Field Anophelines

The primary objective of both studies was to determine if RS and GIS techniques could be used to distinguish between high and low larval-producing rice fields in California. Results of the first study suggested that early-season green-up and proximity to livestock pastures were positively correlated with high larval abundance. Based on the early-season spectral differences between high and low larval-producing fields, it appeared that canopy development and tillering influenced mosquito habitat quality. At that time, rice fields consisted of a mixture of plants and water, a combination that allowed An. freeborni females to lay eggs in partial sunlight, protected from both predators and wind. This established a population earlier in the season than in other, 'less-green' fields where tillering and plant emergence was too minimal for ovipositioning. The study also indicated the importance of the distance that a mosquito would have to fly in order to take a bloodmeal prior to ovipositing. These associations were fully explored in an expanded study two years later. The second study confirmed the positive relationship between early season canopy development and larval abundance, and also demonstrated the relationship between abundance and distance-to-pasture. The association between greenness (as measured using NDVI), distance-to-pasture, and abundance is illustrated. The second study also indicated the siginificance of the landscape context of rice fields for larval production. Fields that included opportunities for feeding and resting within the flight range of the mosquito had higher abundances than did fields that were in a homogeneous rice area.

Beck, Louisa R.↗

Lidar Remote Sensing for Characterizing Forest Vegetation - Special Issue. Foreword

The Silvilaser 2009 conference held in College Station, Texas, USA, was the ninth conference in the Silvilaser series, which started in 2002 with the international workshop on using lidar (Light Detection and Ranging) for analyzing forest structure, held in Victoria, British Columbia, Canada. Following the Canadian workshop, subsequent forestry-lidar conferences took place in Australia, Sweden, Germany, USA, Japan, Finland, and the United Kingdom (UK). By the time this Silvilaser 2009 special issue of PE&RS is published, the 10th international conference will have been held in Freiburg, Germany, and planning will be ongoing for the 11th meeting to take place in Tasmania, Australia, in October 2011. Papers presented at the 2005 conference held in Blacksburg, Virginia, USA, were assembled in a special issue of PE&RS published in December 2006. Other special issues resulting from previous conferences were published in journals such as the Canadian Journal of Remote Sensing (2003), the Scandinavian Journal of Forest Research (2004), and Japan s Journal of Forest Planning (2008). Given the conference history and the much longer record of publications on lidar applications for estimating forest biophysical parameters, which dates back to the early 1980s, we may consider lidar an established remote sensing technology for characterizing forest canopy structure and estimating forest biophysical parameters. Randy Wynne, a professor at Virginia Tech and the final keynote speaker at Silvilaser 2009, made the case that it was time to push 30 years of research into operations, along the lines of what has already been done to good effect in the Scandinavian countries. In Randy s words, it s time to "Just do it!" This special issue includes a selection of papers presented during the 2009 Silvilaser conference, which consisted of eight sections as follows: (1) biomass and carbon stock estimates, (2) tree species and forest type classification, (3) data fusion and integration, (4, 5, and 6) forest inventory, (7) silvicultural and ecological applications, and (8) terrestrial lidar applications. Within the constraint limiting the number of papers that could be fitted into the special issue we attempted to select those papers that best represented these conference topics and sections, giving special consideration to studies using forestry lidar data collected from each of the three platforms -- terrestrial, airborne, and spaceborne. Reflecting the international participation and reach of the conference, the studies presented here took place in the USA, Canada, Taiwan, the UK, and China.

Popescu, Sorin C.↗

Solar Probe Plus

The NASA Solar Probe Plus mission is planned to be launched in 2018 to study the upper solar corona with both.in-situ and remote sensing instrumentation. The mission will utilize 6 Venus gravity assist maneuver to gradually lower its perihelion to 9.5 Rs below the expected Alfven pOint to study the sub-alfvenic solar wind that is still at least partially co-rotates with the Sun. The detailed science objectives of this mission will be discussed. SPP will have a strong synergy with The ESA/NASA Solar orbiter mission to be launched a year ahead. Both missions will focus on the inner heliosphere and will have complimentary instrumentations. Strategies to exploit this synergy will be also presented.

Szabo, Adam↗

Long-Term Changes in Light Scattering in Chesapeake Bay Inferred from Secchi Depth, Light Attenuation, and Remote Sensing Measurements

The relationship between the Secchi depth (Z(sub SD)) and the diffuse attenuation coefficient for photosynthetically active radiation (K(sub d)(PAR)), and in particular the product of the two, Z(sub SD) X K(sub d)(PAR), is governed primarily by the ratio of light scattering to absorption. We analyzed measurements of Z(sub SD) and K(sub d)(PAR) at main stem stations in Chesapeake Bay and found that the Z(sub SD) X K(sub d)(PAR) product has declined at rates varying from 0.020 to 0.033 /yr over the 17 to 25 years of measurement, implying that there has been a long -term increase in the scattering-to-absorption ratio. Remote sensing reflectance at the green wavelength most relevant to Z(sub SD) and K(sub d)(PAR) in these waters, R(sub rs)(555), did not exhibit an increasing trend over the 10 years of available measurements. To reconcile the observations we constructed a bio-optical model to calculate Z(sub SD), K(sub d)(PAR), Z(sub SD) X K(sub d)(PAR), and R(sub rs)(555) as a function of light attenuating substances and their mass-specific absorption and scattering coefficients. When simulations were based exclusively on changes in concentrations of light attenuating substances, a declining trend in Z(sub SD) E K(sub d) entailed an increasing trend in R(sub rs)(555), contrary to observations. To simulate both decreasing Z(sub SD) X K(sub d)(PAR) and stationary R(sub rs)(555), it was necessary to allow for a declining trend in the ratio of backscattering to total scattering. Within our simulations, this was accomplished by increasing the relative proportion of organic detritus with high mass-specific scattering and low backscattering ratio. An alternative explanation not explicitly modeled is an increasing tendency for the particulate matter to occur in large aggregates. Data to discriminate between these alternatives are not available.

Gallegos, Charles L.↗

Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA

This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF), and Multi-Layer Perceptron (MLP) algorithms for LAI and BAPH modeling. The results showed that RF outperformed MLP by reducing the normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. The final variables selected for modeling of both LAI and BAPH indicated the superiority of Sentinel-2 variables over the Sentinel-1 SAR with minor contributions of site variables (mainly elevation). The red-edge spectral vegetation indices played a significant role in both LAI and BAPH estimation. We attained the lowest nRMSEs of 0.12, and 0.16 for the final LAI model of RS, and BF, respectively using Sentinel-2 and site variables. The lowest nRMSE for both RS and BF BAPH models was 0.12. As RS and BF are the primary host species for a cyclically occurring and most destructive pest of the region, eastern spruce budworm (Choristoneura fumiferana; SBW), these estimations will be useful to evaluate SBW dynamics in the region.

Forest inventory↗

Efficiencies of Rotational Raman, and Rayleigh Techniques for Laser Remote Sensing of the Atmospheric Temperature

Various lidar methods have been developed for measuring the atmospheric temperature, making use of the temperature dependant characteristics of rotational Raman scattering (RRS) from nitrogen and oxygen, and Rayleigh or Rayleigh-Brillowin scattering (RS or RBS). These methods have various advantages and disadvantages as compared to each other but their potential accuracies are principal characteristics of their efficiency. No systematic attempt has been undertaken so far to compare the efficiences, in the above meaning, of different temperature lidar methods. Two RRS techniques have been compared. Here, we do such a comparison using two methods based on the detection and analysis of RS (RBS) spectra. Four methods are considered here for measuring the atmospheric temperature. One of them (Schwiesow and Lading, 1981) is based on an analysis of the RS linewidth with two Michelson interferometers (MI) in parallel. The second method (Shimisu et al., 1986) employs a high-resolution analysis of the RBS line shape. The third method (Cooney, 1972) employs the temperature dependance of the RRS spectrum envelope. The fourth method (Armstrong, 1974) makes use of a scanning Fabry-Perot interferometer (FPI) as a comb filter for processing the periodic RRS spectrum of the nitrogen. Let us denote the corresponding errors in measuring the temperature by sigma(sub MI), sigma(sub HR), sigma(sub ENV), and sigma(sub FPI). Let us also define the ratios chi(sub 1) = sigma(sub MI)/sigma(sub ENV), chi(sub 2) = sigma(sub HR)/sigma(sub ENV), and chi(sub 3) = sigma(sub FPI)/sigma(sub ENV) interpreted as relative errors with respect to sigma(sub ENV).

Ivanova, I. D.↗

Tonlé Sap Food Security & Agriculture III: Evaluating Changes in Ecosystem Vitality and Freshwater Health in the Tonlé Sap Basin using Remotely Sensed Data

Tonlé Sap Lake, located in Cambodia, Southeast Asia, is one of the most productive inland fisheries in the world. With the unique reverse flow hydrology of the Tonlé Sap River, this freshwater system contains rich biodiversity and provides critical freshwater resources for the local community. Overfishing, stronger seasonality, drought, dam construction, forest fires, and untreated industrial domestic sewage threaten the ecosystem vitality and economic success of Tonlé Sap. In collaboration with Conservation International and the Cambodian Ministry of Water Resources and Meteorology’s Tonlé Sap Authority, we calibrated and finalized remotely-sensed proxies for sub-indicators of the Freshwater Health Index (FHI). We used NASA Earth observation data from Moderate Resolution Imaging Spectroradiometer (MODIS), and the Gravity Recovery and Climate Experiment (GRACE). These datasets were used in RS proxies and a Soil and Water Assessment Tool (SWAT) model that previous teams developed for sub-indicators of FHI. They included landcover, bank modification, and water quality metrics. It was determined that the ground water storage in Tonlé Sap Lake exhibited a slight declining trend over from April 1st, 2002 to February 3rd, 2017. To calculate chlorophyll-a concentration in the Tonlé Sap Lake, we used Copernicus Global Land Service mission data which provided a Trophic State Index of the lake. Between January 1st, 2000 and December 31st, 2020, the lake and surrounding region within the study area boundary displayed a slight decrease in vegetation density and consistently high chlorophyll-a concentrations. The SWAT model calculated nitrogen and phosphorus content measured in outlet points of the lake. We demonstrated that remotely sensed data is valuable for providing additional information for the FHI, but is not fully capable of replacing its in situ counterpart. We also concluded the water quantity is on a slowly declining trend within the basin.

Joey Lindsay↗

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Sullivan, Dana↗

Correcting airborne scanning infrared radiometer measurements for atmospheric effects

Two techniques were developed for determining atmospheric corrections from observations made by a scanning radiometer. Both techniques depend on knowing the radiometer's limb function. The limb function for an RS-18 scanning radiometer is derived from calculations made with a radiation model and used to demonstrate the techniques. One technique requires observations made over an isothermal water surface within the area being remotely sensed. The other technique does not depend on an isothermal water surface but requires a boat measurement of radiometric sea surface temperature within the area being remotely sensed. The radiation model used to derive the limb function does not account for the effects of atmospheric particulates on the correction. It is hypothesized that the effect of particulates on the limb function derived in this study is negligible, and therefore the technique essentially obtains the total correction. The techniques developed can be used over land provided that a section of isothermal land exists within the experiment area or that a radiometric measurement of surface temperature is made at the surface.

Boudreau, R. D.↗

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.

Sullivan, Dana G.↗

Hydrologic interpretations based on infrared imagery of Long Island, New York

Six remote-sensing flights over Long Island's north and south shores were made during the period July 13, 1967, to February 25, 1970. Infrared imagery in the 8- to 14-micrometer range was obtained; results varied from poor to excellent in quality. The ability of the RS 7 and Reconofax 4 imagers to discern thermal contrasts of as little as 1 to 2 C (Celsius) permitted identification of areas of heavy ground-water discharge. These areas were concentrated primarily along the eroded headlands of the north shore and in the lower reaches of watercourses draining into Great South Bay. Only a few highly localized examples of direct ground-water discharge into the embayments along Long Island's south shore were detected in the imagery. Thermal loading emanating from a powerplant near Oceanside is shown to be quickly dissipated in Middle Bay. Optimal time for the collection of infrared imagery for hydrologic studies on Long Island is in summer and in winter, when surface-water thermal differences are relatively large.

Pluhowski, E. J.↗

Updated MISR Over-Water Research Aerosol Retrieval Algorithm - Part 2: A Multi-Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll alpha concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the "atmospheric correction" needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (R (sub rs)) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral R (sub rs), that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nanometer-interpolated AOD less than 1.0, the root-mean square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD greater than 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Satellite Remote Sensing↗

Spectral Correlation in MODIS Water-Leaving Reflectance Retrieval Uncertainty

Spectral remote sensing reflectance, Rrs(λ) (sr−1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band)error covariance in R rs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in R rs some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Ocean color↗

Cloud Absorption Radiometer Autonomous Navigation System - CANS

CAR (cloud absorption radiometer) acquires spatial reference data from host aircraft navigation systems. This poses various problems during CAR data reduction, including navigation data format, accuracy of position data, accuracy of airframe inertial data, and navigation data rate. Incorporating its own navigation system, which included GPS (Global Positioning System), roll axis inertia and rates, and three axis acceleration, CANS expedites data reduction and increases the accuracy of the CAR end data product. CANS provides a self-contained navigation system for the CAR, using inertial reference and GPS positional information. The intent of the software application was to correct the sensor with respect to aircraft roll in real time based upon inputs from a precision navigation sensor. In addition, the navigation information (including GPS position), attitude data, and sensor position details are all streamed to a remote system for recording and later analysis. CANS comprises a commercially available inertial navigation system with integral GPS capability (Attitude Heading Reference System AHRS) integrated into the CAR support structure and data system. The unit is attached to the bottom of the tripod support structure. The related GPS antenna is located on the P-3 radome immediately above the CAR. The AHRS unit provides a RS-232 data stream containing global position and inertial attitude and velocity data to the CAR, which is recorded concurrently with the CAR data. This independence from aircraft navigation input provides for position and inertial state data that accounts for very small changes in aircraft attitude and position, sensed at the CAR location as opposed to aircraft state sensors typically installed close to the aircraft center of gravity. More accurate positional data enables quicker CAR data reduction with better resolution. The CANS software operates in two modes: initialization/calibration and operational. In the initialization/calibration mode, the software aligns the precision navigation sensors and initializes the communications interfaces with the sensor and the remote computing system. It also monitors the navigation data state for quality and ensures that the system maintains the required fidelity for attitude and positional information. In the operational mode, the software runs at 12.5 Hz and gathers the required navigation/attitude data, computes the required sensor correction values, and then commands the sensor to the required roll correction. In this manner, the sensor will stay very near to vertical at all times, greatly improving the resulting collected data and imagery. CANS greatly improves quality of resulting imagery and data collected. In addition, the software component of the system outputs a concisely formatted, high-speed data stream that can be used for further science data processing. This precision, time-stamped data also can benefit other instruments on the same aircraft platform by providing extra information from the mission flight.

Kahle, Duncan↗

BOREAS RS-12 Automated Ground Sunphotometer Measurements in the SSA

The BOREAS RSS-12 team collected both ground and airborne sunphotometer measurements for use in characterizing the aerosol optical properties of the atmosphere during the BOREAS data collection activities. These measurements are to be used to: 1) measure the magnitude and variability of the aerosol optical depth in both time and space; 2) determine the optical properties of the boreal aerosols; and 3) atmospherically correct some remotely sensed data acquired during BOREAS. These data cover selected days and times from May to September 1994 and were taken from one of two ground sites near Candle Lake in the SSA. The data described in this document are from the field sunphotometer data. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗

Application of selected methods of remote sensing for detecting carbonaceous water pollution

A reach of the Houston Ship Channel was investigated during three separate overflights correlated with ground truth sampling on the Channel. Samples were analyzed for such conventional parameters as biochemical oxygen demand, chemical oxygen demand, total organic carbon, total inorganic carbon, turbidity, chlorophyll, pH, temperature, dissolved oxygen, and light penetration. Infrared analyses conducted on each sample included reflectance ATR analysis, carbon tetrachloride extraction of organics and subsequent scanning, and KBr evaporate analysis of CCl4 extract concentrate. Imagery which was correlated with field and laboratory data developed from ground truth sampling included that obtained from aerial KA62 hardware, RC-8 metric camera systems, and the RS-14 infrared scanner. The images were subjected to analysis by three film density gradient interpretation units. Data were then analyzed for correlations between imagery interpretation as derived from the three instruments and laboratory infrared signatures and other pertinent field and laboratory analyses.

Davis, E. M.↗

CPF-VIIRS Intercalibration Methodology

NASA’s Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission will carry a high-accuracy reflected solar (RS) spectrometer to measure the Earth-reflected solar radiation from International Space Station with an SI-traceable radiometric uncertainty of 0.3% (1-sigma). The CPF measurements will serve as an on-orbit reference for intercalibrating other spaceflight RS instruments. The CPF intercalibration team has developed a state-of-the-art approach to calibrate the reflective solar bands (RSB) of the NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) sensor against the CPF benchmark measurements with an aimed intercalibration methodology uncertainty of 0.3%. This presentation will highlight the CPF mission overview and CPF-VIIRS intercalibration approach, as well as other potential outcomes of the CPF-VIIRS intercalibration study that may benefit the broader remote sensing community.

Raj Bhatt↗