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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Impact of MODIS SWIR Band Calibration Improvements on Level-3 Atmospheric Products

The spectral reflectance measured by the MODIS reflective solar bands (RSB) is used for retrieving many atmospheric science products. The accuracy of these products depends on the accuracy of the calibration of the RSB. To this end, the RSB of the MODIS instruments are primarily calibrated on-orbit using regular solar diffuser (SD) observations. For lambda < 0.94 microns the SDs on-orbit bi-directional reflectance factor (BRF) change is tracked using solar diffuser stability monitor (SDSM) observations. For lambda > 0.94 microns, the MODIS Characterization Support Team (MCST) developed, in MODIS Collection 6 (C6), a time-dependent correction using observations from pseudo-invariant earth-scene targets. This correction has been implemented in C6 for the Terra MODIS 1.24 micron band over the entire mission, and for the 1.375 micron band in the forward processing. As the instruments continue to operate beyond their design lifetime of six years, a similar correction is planned for other short-wave infrared (SWIR) bands as well. MODIS SWIR bands are used in deriving atmosphere products, including aerosol optical thickness, atmospheric total column water vapor, cloud fraction and cloud optical depth. The SD degradation correction in Terra bands 5 and 26 impact the spectral radiance and therefore the retrieval of these atmosphere products. Here, we describe the corrections to Bands 5 (1.24 microns) and 26 (1.375 microns), and produce three sets (B5, B26 correction on/on, on/off, and off/off) of Terra-MODIS Level 1B (calibrated radiance product) data. By comparing products derived from these corrected and uncorrected Terra MODIS Level 1B (L1B) calibrations, dozens of L3 atmosphere products are surveyed for changes caused by the corrections, and representative results are presented. Aerosol and water vapor products show only small local changes, while some cloud products can change locally by > 10%, which is a large change.

Calibration↗
NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗
NASA NTRS2017

Utilizing the Precessing Orbit of TRMM to Produce Hourly Corrections of Geostationary Infrared Imager Data with the VIRS Sensor

Accurate characterization of the Earth's radiant energy is critical for many climate monitoring and weather forecasting applications. For example, groups at the NASA Langley Research Center rely on stable visible- and infrared-channel calibrations in order to understand the temporal/spatial distribution of hazardous storms, as determined from an automated overshooting convective top detection algorithm. Therefore, in order to facilitate reliable, climate-quality retrievals, it is important that consistent calibration coefficients across satellite platforms are made available to the remote sensing community, and that calibration anomalies are recognized and mitigated. One such anomaly is the infrared imager brightness temperature (BT) drift that occurs for some Geostationary Earth Orbit satellite (GEOsat) instruments near local midnight. Currently the Global Space-Based Inter-Calibration System (GSICS) community uses the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) sensor as a common reference to uniformly calibrate GEOsat IR imagers. However, the combination of IASI, which has a 21:30 local equator crossing time (LECT), and hyperspectral Atmospheric Infrared Sounder (AIRS; 01:30 LECT) observations are unable to completely resolve the GEOsat midnight BT bias. The precessing orbit of the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS), however, allows sampling of all local hours every 46 days. Thus, VIRS has the capability to quantify the BT midnight effect observed in concurrent GEOsat imagers. First, the VIRS IR measurements are evaluated for long-term temporal stability between 2002 and 2012 by inter-calibrating with Aqua-MODIS. Second, the VIRS IR measurements are assessed for diurnal stability by inter-calibrating with Meteosat-9 (Met-9), a spin-stabilized GEOsat imager that does not manifest any diurnal dependency. In this case, the Met-9 IR imager is first adjusted with the official GSICS calibration coefficients. Then VIRS is used as a diurnal calibration reference transfer to produce hourly corrections of GEOsat IR imager BT. For the 9 three-axis stabilized GEO imagers concurrent with VIRS, the midnight effect increased the BT on average by 0.5 K (11 microns) and 0.4 K (12 microns), with a peak at approx.01:00 local time. As expected, the spin-stabilized GEOsats revealed a smaller diurnal temperature cycle (mostly < 0.2 K) with inconsistent peak hours.

Scarino, Benjamin↗
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS2004

Preliminary Assessment of Mars Exploration Rover Landing Site Predictions

Selection of the Mars Exploration Rover (MER) landing sites took place over a three year period in which engineering constraints were identified, 155 possible sites were downselected to the final two, surface environments and safety considerations were developed, and the potential science return at the sites was considered. Landing sites in Gusev crater and Meridiani Planum were selected because they appeared acceptably safe for MER landing and roving and had strong morphologic and mineralogical indicators of liquid water in their past and thus appeared capable of addressing the science objectives of the MER missions, which are to determine the aqueous, climatic, and geologic history of sites on Mars where conditions may have been favorable to the preservation of evidence of possible pre-biotic or biotic processes. Engineering constraints important to the selection included: latitude (10 N-15 S) for maximum solar power; elevation (<-1.3 km) for sufficient atmosphere to slow the lander; low horizontal winds, shear and turbulence in the last few kilometers to minimize horizontal velocity; low 10-m scale slopes to reduce airbag spinup and bounce; moderate rock abundance to reduce abrasion or stroke-out of the airbags; and a radar-reflective, load-bearing and trafficable surface safe for landing and roving that is not dominated by fine-grained dust. In selecting the MER landing sites these engineering constraints were addressed via comprehensive evaluation of surface and atmospheric characteristics from existing remote sensing data and models as well as targeted orbital information acquired from Mars Global Surveyor and Mars Odyssey. This evaluation resulted in a number of predictions of the surface characteristics of the sites, which are tested in this abstract. Relating remote sensing signatures to surface characteristics at landing sites allows these sites to be used as ground truth for the orbital data, is essential for selecting and validating landing sites for future missions, and is required for correctly interpreting the surfaces and materials globally present on Mars.

Golombek, M.↗
NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

dust↗
NASA NTRS2013

Daily MODIS 500 m Reflectance Anisotropy Direct Broadcast (DB) Products for Monitoring Vegetation Phenology Dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Vegetation↗
NASA NTRS2003

Advancing Glaciological Applications of Remote Sensing with EO-1: (1) Mapping Snow Grain Size and Albedo on the Greenland Ice Sheet Using an Imaging Spectrometer, and (2) ALI Evaluation for Subtle Surface Topographic Mapping via Shape-from Shading

The Hyperion sensor, onboard NASA's Earth Observing-1 (EO-1) satellite,is an imaging spectroradiometer with 220 spectral bands over the spectral range from 0.4 - 2.5 microns. Over the course of summer 2001, the instrument acquired numerous images over the Greenland ice sheet. Our main motivation is to develop an accurate and robust approach for measuring the broadband albedo of snow from satellites. Satellite-derived estimates of broadband have typically been plagued with three problems: errors resulting from inaccurate atmospheric correction, particularly in the visible wavelengths from the conversion of reflectance to albedo (accounting for snow BRDE); and errors resulting from regression-based approaches used to convert narrowband albedo to broadband albedo. A typerspectral method has been developed that substantially reduces these three main sources of error and produces highly accurate estimates of snow albedo. This technique uses hyperspectral data from 0.98 - 1.06 microns, spanning a spectral absorption feature centered at 1.03 microns. A key aspect of this work is that this spectral range is within an atmospheric transmission window and reflectances are largely unaffected by atmospheric aerosols, water vapor, or ozone. In this investigation, we make broadband albedo measurements at four sites on the Greenland ice sheet: Summit, a high altitude station in central Greenland; the ETH/CU camp, a camp on the equilibrium line in western Greenland; Crawford Point, a site located between Summit and the ETH/CU camp; and Tunu, a site located in northeastern Greenland at 2000 m. altitude. Each of these sites has an automated weather station (AWS) that continually measures broadband albedo thereby providing validation data.

Source record↗
NASA NTRS2022

Estimating pixel-level uncertainty in ocean color retrievals from MODIS

The spectral distribution of marine remote sensing reflectance, R(rs), is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R(rs) (u(c)(R(rs))) at each pixel. Uncertainty in R(rs), in turn, is dependent on the propagation of various uncertainty sources through the R(rs) retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R(rs), as retrieved using NASA’s multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R(rs) at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/cu.m) with valid u(c)(R(rs)) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that u(c)(R(rs)) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Pixel-level uncertainty↗
NASA NTRS2023

A Hyperspectral Inversion Framework for Estimating Absorbing Inherent Optical Properties and Biogeochemical Parameters in Inland and Coastal Waters

The simultaneous remote estimation of biogeochemical parameters (BPs) and inherent optical properties (IOPs) from hyperspectral satellite imagery of globally distributed optically distinct inland and coastal waters is a complex, unsolved, non-unique inverse problem. To tackle this problem, we leverage a machine-learning model termed Mixture Density Networks (MDNs). MDNs outperform operational algorithms by calculating the covariance between the simultaneously estimated products. We train the MDNs on a large ( N = 8237) dataset of co-aligned, in situ measured, hyperspectral remote sensing reflectance (R rs ), BPs, and absorbing IOPs from globally representative optically distinct inland and coastal waters. The estimated IOPs include absorption due to phytoplankton (a ph ), chromophoric dissolved organic matter (a cdom ), and non-algal particles (a nap ). The estimated BPs include chlorophyll-a, total suspended solids, and phycocyanin (PC). MDNs dramatically reduce uncertainty in the retrievals, relative to operational algorithms, when using a 50/50 dataset split, where the MDNs are trained on a randomly selected half of the in situ dataset and validated on the other half. Our model is shown to have higher, or equivalent, generalization performance than the calculated operational algorithms available for all BPs and IOPs (except PC) via a leave-one-out cross-validation assessment. The MDNs are sensitive to uncertainties in the hyperspectral satellite R rs , resulting from instrument noise and atmospheric correction; there is a difference of ~37.4–62.8% (using median symmetric accuracy) between the MDNs’ estimates derived from co-located satellite-derived R rs and in situ R rs . Of the IOPs, a cdom and a nap are less sensitive to uncertainties in hyperspectral satellite imagery relative to a ph , with remote estimates of a ph exhibiting incorrect spectral shape and magnitude relative to in situ measured IOPs. Despite the uncertainties in satellite derived R rs , the spatial distributions of BPs and IOPs in MDN-derived product maps of Lake Erie and the Curonian Lagoon, based on imagery taken with the Hyperspectral Imager for the Coastal Ocean (HICO) and PRecursore Iper-Spettrale della Missione Applicativa (PRISMA), are confirmed via co-aligned in situ measurements and agree with the literature’s understanding of these well-studied regions. The consistency and accuracy of the model on HICO and PRISMA imagery, despite radiometric uncertainties, demonstrate its applicability to future hyperspectral missions, such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, where the simultaneous estimation model will serve as a key part of phytoplankton community composition analysis.

Ryan E. O'Shea↗
NASA NTRS2008

Study of Molecular Line Parameters down to Very Low Temperature

The impact of precise spectroscopic measurements on the P(9) multiplet in the v3 band of 12CH4, obtained using a newly developed cold Herriott cell coupled with a tunable diode laser spectrometer, is examined in the context of remote sensing of methane in the atmosphere. More specifically, the influence of the air-broadening temperature dependence, of line narrowing and of line-mixing effects is assessed when fitting high resolution atmospheric spectra recorded from a balloon in the solar occultation mode. This is achieved by considering residuals and retrieved methane volume mixing ratio profiles. By combining data from previous studies together with new measurements, the expression for the temperature dependence of the broadening coefficient has been revisited. It is shown that a correcting factor applied to the usual law gives better results, when considering all the available measurements from room temperature down to about 10-20 K, as compared to the usual expression of the width versus temperature. These results clearly demonstrate that collision broadening is more sensitive to long-range attractive forces when the temperature is decreased.

Mondelain, D.↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
DOE OSTI2022

Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia

The atmospheric boundary layer height (zi) is a key parameter in the vertical transport of mass, energy, moisture, and chemical species between the surface and the free atmosphere. There is a lack of long-term and continuous observations of zi, however, particularly for remote regions, such as the Amazon forest. Reanalysis products, such as ERA5, can fill this gap by providing temporally and spatially resolved information on zi. In this work, we evaluate the ERA5 estimates of zi (zi-ERA5) for two locations in the Amazon and corrected them by means of ceilometer, radiosondes, and SODAR measurements (zi-experimental). The experimental data were obtained at the remote Amazon Tall Tower Observatory (ATTO) with its pristine tropical forest cover and the T3 site downwind of the city of Manaus with a mixture of forest (63%), pasture (17%), and rivers (20%). We focus on the rather typical year 2014 and the El Niño year 2015. The comparison of the experimental vs. ERA5 zi data yielded the following results: (i) zi-ERA5 underestimates zi-experimental daytime at the T3 site for both years 2014 (30%, underestimate) and 2015 (15%, underestimate); (ii) zi-ERA5 overestimates zi-experimental daytime at ATTO site (12%, overestimate); (iii) during nighttime, no significant correlation between the zi-experimental and zi-ERA5 was observed. Based on these findings, we propose a correction for the daytime zi-ERA5, for both sites and for both years, which yields a better agreement between experimental and ERA5 data. These results and corrections are relevant for studies at ATTO and the T3 site and can likely also be applied at further locations in the Amazon.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Discrete Random Media Techniques for Microwave Modeling of Vegetated Terrain

Microwave remote sensing of vegetated terrain has been studied. Vegetation is modeled so that backscattered radar signals can be used to infer parameters which characterize the vegetation and underlying ground. The vegetation is modeled by discrete lossy dielectric scatterers with prescribed characteristics. The goal of the modeling effort is to remotely sense vegetation type (classification), growth stage, and plant/ground moisture. This information can then be used as input into agricultural, forestry and global circulation models. The microwave frequency spectrum, particularly L and C bands, are especially appropriate for this purpose since the wavelength is comparable to plant leaf and stem size. The resulting resonant interaction leads to backscattered data highly depend on plant shape and orientation. In addition, the transparent nature of the atmosphere in this frequency regime allows for algorithm development which requires no atmospheric correction.

Lang, R. H.↗
NASA NTRS2016

CATS Aerosol Typing and Future Directions

The Cloud Aerosol Transport System (CATS), launched in January of 2015, is a lidar remote sensing instrument that will provide range-resolved profile measurements of atmospheric aerosols and clouds from the International Space Station (ISS). CATS is intended to operate on-orbit for at least six months, and up to three years. Status of CATS Level 2 and Plans for the Future:Version. 1. Aerosol Typing (ongoing): Mode 1: L1B data released later this summer; L2 data released shortly after; Identify algorithm biases (ex. striping, FOV (field of view) biases). Mode 2: Processed Released Currently working on correcting algorithm issues. Version 2 Aerosol Typing (Fall, 2016): Implementation of version 1 modifications Integrate GEOS-5 aerosols for typing guidance for non spherical aerosols. Version 3 Aerosol Typing (2017): Implementation of 1-D Var Assimilation into GEOS-5 Dynamic lidar ratio that will evolve in conjunction with simulated aerosol mixtures.

McGill, Matt↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS2017

A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.

AVHRR↗
NASA NTRS2021

Spectral Fidelity of Earth's Terrestrial and Aquatic Ecosystems

The Surface Biology and Geology (SBG) investigation will create global maps of spectral surface reflectance and emissivity at a cadence of 16 days or better, with coverage to address global questions about Earth's geology, cryosphere and ecosystems. The revolutionary potential poses a commensurate challenge: creating contiguous maps free from regional biases induced by atmosphere, observation geometry, or inversion error. This will require an accurate calibration with precise knowledge of each channel's spectral response. Here, we quantify the impact of spectral calibration on SBG's aquatic and terrestrial ecosystem objectives. We find that contemporary algorithms for ecosystem trait retrieval demand more accurate spectral calibration than historical missions. Errors due to drift or spatial nonuniformity in the wavelength calibration that have previously been considered acceptable can cause systematic errors larger than the instrument noise and of the same order as the variability SBG aims to measure. Moreover, their impact on atmospheric correction can induce climate-dependent systematic errors that thwart comparisons between ecosystems. These results underscore the importance of spectral response accuracy in SBG mission design.

imaging spectroscopy↗