Aerosols play a critical role in radiative transfer within the atmosphere and in climate change. As part of the validation of atmospheric correction of remote sensing data affected by the atmosphere, it is critical to utilize appropriate aerosol models as aerosols are a main source of error. Here, we define the aerosol model by recalculating the aerosol microphysical properties based on the optical thickness at 440 nm and the Ångström coefficient obtained from numerous AERONET sites. The associated uncertainties are up to 23%, except for the imaginary part of the refractive index (about 38%). Uncertainties of the retrieved aerosol microphysical properties were incorporated in the framework for validating surface reflectance derived from space-borne Earth observation sensors. It yields an overall uncertainty of approximately of 1 to 3% of the retrieved surface reflectance in the MODIS red spectral band, well below the specification used for atmospheric correction.

To investigate near UV aerosol absorbers, such as iron in mineral dust and brown carbon from biomass burning, AERONET spectral imaginary refractive index and size distributions retrievals were performed with the input of additional wavelength data. This includes adding both measured spectral AOD and sky radiances at 380 nm and 500 nm to the four channels (440, 675, 870 and 1020 nm) that are used in the standard retrieval data product. The vector radiative transfer code in the AERONET Version 3 retrievals enabled more accurate computations in the ultraviolet region (380 nm). Calibration of the 380 nm channel radiances was done by the vicarious method since the integrating sphere radiance source that is used for all other wavelengths does not produce sufficient energy at 380 nm. Spectral single scattering albedo, imaginary refractive indices and aerosol size distributions from these six wavelength retrievals are compared to the standard four wavelength retrieval values.

Various forms of global compositional forecasting are now commonplace across the world's operational centers. Biomass burning smoke is often forecast just like other aspects of our weather to support numerous applications such as air quality, transportation, and climate. Recent developments in the field have been bolstered by a new generation of advanced satellite sensors and algorithms on an international constellation of geostationary and polar orbiting satellites. The academic community frequently solicits operational developers for input on development needs and what should be operationalized. Yet, the volume of new data sources is currently outpacing Moore's Law and the forecasting community's ability to process and utilize new data sources data. Targeted to the academic community and using the 2020 western biomass-burning season as an example, this presentation will provide a brief review of how developers view next generation products for use in coupled observational and data assimilation systems that may be required to meet challenges posed by global extreme smoke event forecasting.

The NASA Micro Pulse Lidar Network (MPLNET) is a federated network of micro pulse lidar sites, mostly colocated with the NASA Aerosol Robotic Network (AERONET), providing information on the vertical properties of both aerosols and clouds. MPLNET began in 2000, and has grown to include ~80 sites worldwide. MPLNET data is collected continuously, offering the ability to examine diurnal changes in aerosol and cloud properties. To date, twelve sites have over 10 years of data and more will hit this milestone soon. Here we present early results from development of Level 3 monthly diurnal climatologies from MPLNET.

Seasonal snow cover plays a key role in freshwater resources, water security, natural hazards, and weather and climate. However, accurate estimation of snow-water equivalent (SWE) with remote sensing observations remains a significant challenge. NASA Terrestrial Hydrology Program launched its multi-year SnowEx mission whose primary goal is to develop and test techniques for estimating how much water is stored in some complex Earth's terrestrial snow-covered regions (e.g. forested areas). The first year of the 5-year campaign took place in Colorado during the winter 2016–2017, during which in situ measurements and ground-based remote sensing observations were collected by the scientific community. Throughout February 2017, about 100 people were deployed and over 30 remote sensing instruments were used. This required an exceptional coordination effort, which resulted in collocated in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements) with ground-based remote sensing observations (microwave radiometers, radar, scatterometers, lidars, etc.). The public release of all these datasets has started (nsidc.org/data/snowex).

NASA SnowEx's goal is estimating how much water is stored in Earth's terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

The land surface reflectance is a fundamental climate data record at the basis of the derivation of other climate data records (Albedo, LAI/Fpar, Vegetation indices) and has been recognized as a key parameter in the understanding of the land-surface-climate processes. In this presentation, we present the validation of the Land surface reflectance used for MODIS, VIIRS, Landsat 8 and Sentinel 2 data. This methodology uses the 6SV Code and data from the AERONET network. The overall accuracy clearly reaches the satellite specifications. To understand how to improve the validation, we developed an exhaustive error budget. Results show an impact of the absorption of aerosol and of the fine mode volume concentration.

The land surface reflectance is a fundamental climate data record at the basis of the derivation of other climate data records (Albedo, LAI/Fpar, Vegetation indices) and has been recognized as a key parameter in the understanding of the land-surface-climate processes. In this presentation, we present the validation of the Land surface reflectance used for MODIS and VIIRS data. This methodology uses the 6SV Code and data from the AERONET network. The overall accuracy clearly reaches the MODIS and VIIRS specifications. To understand how to improve the validation, we developed an exhaustive error budget. Results show an impact of the absorption of aerosol and of the fine mode volume concentration. At the end, we discuss about the interest of the indirect and direct method for validation.

We report our first photometric measurements of aerosol optical depth from AERONET's mini-DRAGON sites at Singapore performed over the months of August and September 2012. Multi-spectral measurements of aerosol optical depth provide essential spectral information to obtain and/or retrieve the so-called Angstrom exponent number which is an essential parameter for inferring aerosol particle size regime. Based on the range of variability of Angstrom exponent number and aerosol optical depth, various aerosol types present in the local environment, can be identified. Special emphasis is placed on detecting the possible presence of external sources of aerosols such as from trans-boundary smoke originating from regional biomass burning episodes which is prevalent during this time of the year.

The aerosol products derived from the ocean-color missions Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate-Resolution Spectroradiometer (MODIS) Aqua and Terra are compared with field measurements from globally distributed Aerosol Robotic Network (AERONET) sites. Validation statistics are found consistent for the three missions. The median absolute relative difference between SeaWiFS and AERONET aerosol optical thickness τais approximately 20% at all bands while it is slightly higher for both MODIS products (between 20% and 28%). This is associated with a larger relative bias (median of relative differences between satellite and AERONET τa) on the order of +15% for these missions. With respect to previous processing versions, a noticeable improvement is seen in the representation of the spectral dependence of τa. The bias found for the Ångström exponent varies from -0.08 to +0.13 for the three missions.

Carbonaceous aerosol plays an important role not only in climate but also in aerosol study. It is, however, difficult to make models of biomass burning aerosols because their properties are changable due to strongly dependce on the biomaterial itself, and on the stage of burning, and/or transportation process. This work intends to estimate the optical properties of biomass burning aerosols based on the combined use of the satellite data by CAI (Cloud Aerosol Imager) on GOSAT and by POLDER (POLarization and Directionality of Earth's Reflectances) on PARASOL. As a result, aerosol optical thickness (AOT), Angstrom exponent, and single scattering albedo (SSA) are retrieved. The retrieved values of AOT and Angstrom exponent are partially validated with the ground-based measurements of AERONET.

This paper describes an Aerosol Robotic Network (AERONET)-based Surface Reflectance Validation Network (ASRVN) and its data set of spectral surface bidirectional reflectance and albedo based on Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50 times 50 km2subsets of MODIS level 1B (L1B) data from MODIS adaptive processing system and AERONET aerosol and water-vapor information. Then, it performs an atmospheric correction (AC) for about 100 AERONET sites based on accurate radiative-transfer theory with complex quality control of the input data. The ASRVN processing software consists of an L1B data gridding algorithm, a new cloud-mask (CM) algorithm based on a time-series analysis, and an AC algorithm using ancillary AERONET aerosol and water-vapor data. The AC is achieved by fitting the MODIS top-of-atmosphere measurements, accumulated for a 16-day interval, with theoretical reflectance parameterized in terms of the coefficients of the Li Sparse-Ross Thick (LSRT) model of the bidirectional reflectance factor (BRF). The ASRVN takes several steps to ensure high quality of results: 1) the filtering of opaque clouds by a CM algorithm; 2) the development of an aerosol filter to filter residual semitransparent and subpixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing the requirement of the consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of a seasonal backup spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixel. The ASRVN products include three parameters of the LSRT model (kL, kG, and kV), surface albedo, normalized BRF (computed for a standard viewing geometry, VZA = 0deg, SZA = 45deg), and instantaneous BRF (or one-angle BRF value derived from the last day of MODIS measurement for specific viewing geometry) for the MODIS 500-m bands 1-7. The results are produced daily at a resolution of 1 km in gridded format. We also provide a cloud mask, a quality flag, and a browse bitmap image. The ASRVN data set, including 6 years of MODIS TERRA and 1.5 years of MODIS AQUA data, is available now as a standard MODIS product (MODASRVN) which can be accessed through the Level 1 and Atmosphere Archive and Distribution System website ((http://ladsweb.nascom.nasa.gov/data/search.html).). It can be used for a wide range of applications including validation analysis and science research.

We have developed an atmospheric correction algorithm to retrieve the surface bidirectional reflectance factor (BRF) and albedo from Multiangle Imaging SpectroRadiometer (MISR) measurements for small areas around Aerosol Robotic Network (AERONET) sunphotometer sites, using AERONET aerosol and column water vapor information. Our goal is to develop an indirect validation method for MISR surface reflectance products over heterogeneous land. Our algorithm makes independent retrievals with both the Li Sparse-Ross Thick kernel BRF model and the modified Rahman-Pinty-Verstraete BRF model used in the Moderate Resolution Imaging Spectroradiometer and MISR land algorithms, respectively. In this study, we report the first results of processing MISR Collection 4 data for 2003-2004 for two sites, Mongu, Zambia, and Greenbelt, MD. We found that MISR generally provides accurate retrievals of BRF and albedo in both clear and hazy atmospheric conditions, correctly reproducing the parameter time series and spatial distribution. We found that the MISR BRF, on average, is less anisotropic in the visible bands. The difference is greatest in the blue band, but decreases with increasing wavelength such that it is negligible in the near-IR band. This discrepancy originates in part in the MISR aerosol retrieval algorithm over heterogeneous land, which tends to select an aerosol model that favors spectrally invariant shapes of surface BRF. The other part of the discrepancy comes from the surface hemispherical-directional reflectance factor retrieval algorithm where the iteration loop that removes the diffuse atmospheric transmittance is currently turned off. Our initial results suggest that the MISR surface albedo is on average lower than our retrievals by about 0.005 in the green and red bands. In the near-IR, it agreed with our retrievals with the modified Rahman-Pinty-Verstraete model for the Mongu site, but was systematically lower over the Greenbelt site by about 0.016. When significant aerosol absorption is present (Mongu), the albedo discrepancy is additionally biased by the difference between the MISR and AERONET retrievals of aerosol absorption

Observations of the aerosol optical thickness (AOT) by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard Terra and Aqua satellites are being used extensively for applications to climate and air quality studies. Data quality is essential for these studies. Here we investigate the effects of unresolved clouds on the MODIS measurements of the AOT. The main cloud effect is from residual cirrus that increases the AOT by 0.015/spl plusmn/0.003 at 0.55 /spl mu/m. In addition, lower level clouds can add contamination. We examine the effect of lower clouds using the difference between simultaneously measured MODIS and AERONET AOT. The difference is positively correlated with the cloud fraction. However, interpretation of this difference is sensitive to the definition of cloud contamination versus aerosol growth. If we consider this consistent difference between MODIS and AERONET to be entirely due to cloud contamination we get a total cloud contamination of 0.025/spl plusmn/0.005, though a more likely estimate is closer to 0.020 after accounting for aerosol growth. This reduces the difference between MODIS-observed global aerosol optical thickness over the oceans and model simulations by half, from 0.04 to 0.02. However it is insignificant for studies of aerosol cloud interaction. We also examined how representative are the MODIS data of the diurnal average aerosol. Comparison to monthly averaged sunphotometer data confirms that either the Terra or Aqua estimate of global AOT is a valid representation of the daily average. Though in the vicinity of aerosol sources such as fires, we do not expect this to be true.

MPLNET is a network of lidar systems that provide long term observations of aerosol and cloud properties at multiple sites around the globe. Each site in the network uses an elastic-scattering lidar co-located with a sunphotometer to provide data products of aerosol optical and physical properties. Expansion of the network is based on partnering with research groups interested in joining MPLNET. Results have contributed to a variety of studies including aerosol transport processes and satellite calibration and validation efforts

An operational system for autonomous above-water radiance measurements, called the SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM), was deployed at the Acqua Alta Oceanographic Tower in the northern Adriatic Sea and used for the validation of remote sensing radiometric products in coastal waters. The SeaPRISM data were compared with simultaneous data collected from an independent in-water system for a wide variety of sun elevations along with different atmospheric, seawater, and sea state conditions. The average absolute differences between the above- and in-water determinations of water-leaving radiances (computed linearly) were less than 4.5% in the 412-555-nm spectral interval. A similar comparison for normalized water-leaving radiances showed average absolute differences less than 5.1%. The comparison between normalized water-leaving radiances computed from remote sensing and SeaPRISM matchup data, showed absolute spectral average (linear) differences of 17.0%, 22.1%, and 20.8% for SeaWiFS, MODIS, and MERIS, respectively. The results, in keeping with those produced by independent in-water systems, suggest the feasibility of operational coastal networks of autonomous above-water radiometers deployed on fixed platforms (towers, lighthouses, navigation aids, etc.) to support ocean color validation activities.

The ADEOS/POLDER sensor provides the first opportunity to measure the atmospheric water vapor content on a global scale. A POLDER sensor, mounted on the Earth observation satellite ADEOS in 1996, is a unique sensor which can gather multi-(up to 14) directional polarization measurements of one target. Two channels of POLDER in the near infrared wavelengths are examined in this study. The first channel is in the water vapor absorption band of 0.910 /spl mu/m and the second is in the gas absorption-free band of 0.865 /spl mu/m. In practice, a ratio of each reflectance for these two channels is used to estimate the total column water vapor content. This algorithm is referred to as the two-channel ratio method. It is shown that the global distribution of water vapor content from the POLDER data present the typical characteristics of spatial and temporal changes. The water vapor content has high values over the tropical zone and decreases with latitude. Furthermore, the Indian monsoon is clearly demonstrated in the water vapor map. It should be noted that the water vapor content retrieved from the POLDER data has been validated with AERONET measurements.

Correction of Advanced Very High Resolution Radiometer (AVHRR) imagery for the aerosol effect requires retrieval of the aerosol loading from the images. Two retrieval algorithms that were previously developed for Landsat are modified for the AVHRR. The methods determine the aerosol optical thickness over land surfaces from AVHRR band one data independently of ancillary information. The first method retrieves aerosols based on the atmospheric effect on the path radiance. This method requires the surface reflectance to be 0.02+or-0.01, which is found over forests in the red channel. Two techniques are used to screen an AVHRR scene for pixels that have this low reflectance. The qualifying requirements for these techniques are discussed, and the method is demonstrated to retrieve aerosol optical thicknesses to approximately +or-0.1. The second method uses the change in contrast for several scenes to determine the change in the optical thickness between the scenes. A reference scene allows absolute determination. The method has an rms error of approximately 0.1.>

Investigation of the effect of atmospheric constituents on NOAA Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared data is presented. The general remote sensing equation, including scattering, absorption, and bidirectional reflectance effects for the AVHRR solar bands, is described. The magnitude of the atmospheric effects for AVHRR solar bands with respect to their impact on the normalized difference vegetation index (NDVI) and the surface bidirection reflectance is examined. Possible approaches for acquiring atmospheric information are discussed, and examples of atmospheric correction of surface reflectance and NDVI are given. Invariant effects (ozone absorption and molecular scattering) and variant effects (water vapor absorption and aerosol scattering) are shown to dominate the atmospheric effects in the AVHRR solar bands.>