Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MODIS”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Additional characterization of Libya-4 in support of post-launch vicarious calibration of satellite imagers

Libya-4 (28.55° N and 23.39° E) is one of the most characterized and utilized Pseudo Invariant Calibration Site (PICS) for post-launch radiometer drift monitoring and sensor pair radiometric scaling. Libya-4 is one of the driest and most reflective PICS located in an extensive sand dune region void of any vegetation with an elevation of 118 m. It is positioned near the northeast border with Egypt. With minimal cloud cover and the monthly mean precipitation of ~ 1mm, Libya-4 is one of the temporally, spectrally, and spatially stable CEOS recommended PICS and Cosnefroy et al. 1996 identified sites. The Libya-4 PICS has been extensively used for post-launch radiometric calibration and validation of high-, medium, and low-resolution satellite imagers, including Landsat, MODIS, VIIRS, and Meteosats. The CERES Imager and Geostationary Calibration Group (IGCG) at NASA LaRC utilizes Libya-4 to perform an independent assessment of the radiometric stability of the MODIS and VIIRS L1B products. The site is also used for absolute radiometric scaling between MODIS, VIIRS, and geostationary imagers to ensure consistent cloud and radiative flux retrievals. Multi-year Terra-MODIS, Aqua-MODIS , NPP-VIIRS and Metoesat-7 observations over Libya-4 show very similar TOA reflectance temporal variability. Although, the surface reflectance and atmospheric column varies seasonally, the inter-annual variability of the seasonal cycle should be small. Especially during 2013, the Libya-4 TOA reflectance was found to be greater than usual in all 4 satellite records. Preliminary comparisons with mean wind speed and aerosol optical depth (AOD) indicate that the year 2013 is marked by elevated levels of both conditions. The goal of this study is to tie the Libya-4 visible reflectance inter-annual variability with corresponding meteorological measurements to further improve the characterization of the site.

David R. Doelling↗
NASA NTRSDate not supplied

Evaluation of Spectral Band Adjustment Factors for Cross-Calibration of Visible Imagers

The CERES EBAF dataset provides TOA SW and LW fluxes for long-term monitoring of the Earth’s energy balance and to validate climate models. The EBAF products, based on the Terra and Aqua CERES instrument observed radiances, rely on coincident measurements from the onboard MODIS imagers to determine cloud properties that are used in the angular distribution model scene selection required to convert the CERES observed radiances into flux values. Once the Terra and Aqua orbits start drifting outside of their 15-minute window, the CERES project will rely solely on the NOAA-20 CERES observations. A seamless transition of fluxes and clouds can only occur if the analogous MODIS and VIIRS channels are properly inter-calibrated. The visible band (0.65 µm) in MODIS (band 1) and VIIRS (M5 or I1) is critical for retrieving the cloud mask and optical depth. The spectral response functions (SRF) of these bands differ noticeably and will require scene dependent spectral band adjustment factors (SBAF) for proper radiometric scaling between them. Fortunately, the VIIRS I1 and M5 bands are calibrated using the same solar diffuser as a reference, which implies that band reflectance differences must be due to the spectral disparity. The coincident I1 and M5 TOA reflectance measurements provide the optimal opportunity to validate SBAFs over many surface and cloud conditions. The CERES project maintains SCIAMACHY, GOME-2, Hyperion, and radiative transfer model-based scene-stratified hyper-spectral reflectance measurements that can be convolved with sensor pair SRFs to compute the corresponding SBAF. This study will evaluate the SCIAMACHY, GOME-2, Hyperion, and RTM based SBAFs over tropical ocean targets used in SNO inter-calibration of MODIS and VIIRS, including clear-sky ocean, deep convective clouds (DCC), and liquid cloud targets in terms of their applicability to absolute intercomparison of visible imagers. The large SCIAMACHY and GOME-2 footprints and the lack of Hyperion cloudy scenes will impact the resulting SBAFs and necessitates the need for future CLARREO hyper-spectral reflectances to decrease SBAF uncertainties Preliminary results indicate that the SCIAMACHY and GOME-2 based SBAFs for the VIIRS I1 and M5 band pairs may differ by 1.5% for some ATO scene types. Because most of the modern GEOs visible band SRFs encompass the VIIRS I1 band SRF, these evaluations are critical to ensure that the MODIS, VIIRS, and GEO clouds and fluxes are consistent. SBAFs for Libya-4, Dome-C and other Earth invariant target will also be evaluated.

D. R. Doelling↗
NASA NTRSDate not supplied

Fusing Surface BRDF from Geostationary and Polar-Orbiting Satellite Sensors

The bi-directional reflectance distribution function (BRDF) describes the fundamental optical property of a surface and therefore has been retrieved from both geostationary (GEO) and polar-orbiting (or Low-Earth Orbit, LEO) satellite observations. In theory, although GEO and LEO observations feature different illumination-view geometries, they reflect the same physical property and the retrieved BRDF should be mutually consistent. This fact also suggests that we may derive a better BRDF product by synergistically fusing the GEO and LEO datasets. Here we demonstrate the idea by fusing Terra/Aqua MODIS and GOES16/17 ABI surface BRDF with the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Our processing is implemented in two steps. First, we compile and project both MODIS and ABI data on the same GeoNEX common grid, and then use the MODIS MAIAC BRDF (MCD19A3) as the prior information to perform atmospheric correction of ABI data and retrieve surface BRDF with the ABI MAIAC code. In the second step, we run the MAIAC code in the forward mode to simulate MODIS and ABI TOA radiances from the jointly retrieved surface BRDF and the corresponding illumination-view geometries. By comparing the simulated TOA radiances with corresponding observations (e.g., MOD02 and ABI05), we can quantitatively evaluate whether the jointly retrieved ABI surface BRDF improves over the separately retrieved MODIS and ABI BRDF products. We expect that the jointly retrieved BRDF data are more robust than the standard products and can help us reduce uncertainties in higher-level earth observation satellite products.

Geostationary satellite↗
NASA NTRSDate not supplied

Using DSCOVR EPIC as an Additional Calibration Reference to Radiometrically Scale the Geo Imagers for the NASA CERES Project

The NASA CERES project provides climate-quality observed TOA and calculated surface fluxes to the climate community. The CERES instruments are mounted on the Aqua, Terra, NPP, and NOAA-20 low earth orbiting (LEO) satellites, where they are used alongside the VIIRS and MODIS based cloud properties necessary for converting CERES radiance observations into fluxes. The CERES SYN1deg product provides the regional hourly fluxes in between the Terra and Aqua/NOAA-20-CERES observations based on geostationary imager (GEO) radiances. The GEO radiances are radiometrically scaled to the MODIS or VIIRS calibration references. The GEO and MODIS inter-calibration events are limited by the Aqua-MODIS or VIIRS local equator crossing time of 1:30 PM. The DSCOVR satellite orbits the Lagrange-1 (L1) point about 1.5 million kilometers from Earth, and the Earth Polychromatic Imaging Camera (EPIC) instrument onboard DSCOVR has a constant view of the sunlit side of the Earth while taking images ranging from the UV to the NIR throughout the day. While the EPIC sensor has no onboard calibration systems, multiple inter-calibration studies have indicated that EPIC is radiometrically stable, which allows it to potentially be used as a transfer radiometer between imagers. This study will assess the viability of using EPIC as a consistent and stable calibration source for GEO imagers. EPIC will be inter-calibrated with Himawari-8 that has been radiometrically scaled with Aqua. The Himawari-8 based EPIC calibration gains will be verified by the EPIC calibration gains determined directly from Aqua-MODIS matches. The EPIC calibration gains based on Himawari-8 will be stratified by local time to determine any diurnal dependence of the GEO/EPIC calibration strategy.

C O Haney↗
DOE OSTI2025

Data and Scripts associated with a manuscript on ecosystem responses to wildfires in the Columbia River Basin

This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Comparison of Coincident Multiangle Imaging Spectroradiometer and Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depths over Land and Ocean Scenes Containing Aerosol Robotic Network Sites

The Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), launched on 18 December 1999 aboard the Terra spacecraft, are making global observations of top-of-atmosphere (TOA) radiances. Aerosol optical depths and particle properties are independently retrieved from these radiances using methodologies and algorithms that make use of the instruments corresponding designs. This paper compares instantaneous optical depths retrieved from simultaneous and collocated radiances measured by the two instruments at locations containing sites within the Aerosol Robotic Network (AERONET). A set of 318 MISR and MODIS images, obtained during the months of March, June, and September 2002 at 62 AERONET sites, were used in this study. The results show that over land, MODIS aerosol optical depths at 470 and 660 nm are larger than those retrieved from MISR by about 35% and 10% on average, respectively, when all land surface types are included in the regression. The differences decrease when coastal and desert areas are excluded. For optical depths retrieved over ocean, MISR is on average about 0.1 and 0.05 higher than MODIS in the 470 and 660 nm bands, respectively. Part of this difference is due to radiometric calibration and is reduced to about 0.01 and 0.03 when recently derived band-to-band adjustments in the MISR radiometry are incorporated. Comparisons with AERONET data show similar patterns.

Moderate Resolution Imaging Spectroradiometer (MOD↗
NASA NTRS2014

Air Pollution Over the Ganges Basin and Northwest Bay of Bengal in the Early Postmonsoon Season Based on NASA MERRAero Data

The MERRA Aerosol Reanalysis (MERRAero) has been recently developed at NASA's Global Modeling Assimilation Office. This reanalysis is based on a version of the Goddard Earth Observing System-5 (GEOS-5) model radiatively coupled with Goddard Chemistry, Aerosol, Radiation, and Transport aerosols, and it includes assimilation of bias-corrected aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on both Terra and Aqua satellites. In October over the period 2002-2009, MERRAero showed that AOT was lower over the east of the Ganges basin than over the northwest of the Ganges basin: this was despite the fact that the east of the Ganges basin should have produced higher anthropogenic aerosol emissions because of higher population density, increased industrial output, and transportation. This is evidence that higher aerosol emissions do not always correspond to higher AOT over the areas where the effects of meteorological factors on AOT dominate those of aerosol emissions. MODIS AOT assimilation was essential for correcting modeled AOT mainly over the northwest of the Ganges basin, where AOT increments were maximal. Over the east of the Ganges basin and northwest Bay of Bengal (BoB), AOT increments were low and MODIS AOT assimilation did not contribute significantly to modeled AOT. Our analysis showed that increasing AOT trends over northwest BoB (exceeding those over the east of the Ganges basin) were reproduced by GEOS-5, not because of MODIS AOT assimilation butmainly because of the model capability of reproducing meteorological factors contributing to AOT trends. Moreover, vertically integrated aerosol mass flux was sensitive to wind convergence causing aerosol accumulation over northwest BoB.

AOT↗
NASA NTRS2015

CHIMAERA System for Cloud Retrievals v 6.0.85

Organizers of the MODIS-VIIRS Science Team Meeting, held May 18-22, 2015 in Silver Spring, MD plan to post the presentations and posters to the NASA MODIS website: http:modis.gsfc.nasa.govsci_teammeetings201505index.php. The MODIS Science Team Meeting is held twice a year, so that the members of the science team may assemble and discuss data they have collected, ideas they have formed, and future issues that apply to the MODIS Mission.

CHIMAERA↗
NASA NTRS2015

Spatiotemporal Coherence of Mean and Extreme Aerosol Particle Events over Eastern North America as Observed from Satellite

Quantifying spatiotemporal scales of coherence and variability of aerosol particle properties provides (i) metrics for evaluating observational data sets and model simulations, (ii) inferences regarding dominant causes of variability, and (iii) information that is necessary to determine their climate forcing and air quality impacts. Accordingly, analyses of columnar aerosol optical depth (AOD) and Ångstrom exponent (AE) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites, and AErosol RObotic NETwork (AERONET) stations are used to evaluate the spatially averaged (Level-3) MODIS products, and to characterize the spatiotemporal scales of variability (and coherence) of aerosol particles over eastern North America (2000-2013). Results show a high degree of consistency in AOD retrievals from the different data sets but larger discrepancies in AE estimates, highest mean AOD and lowest day-to-day variability during the summer, and largest scales of spatial coherence in summer and fall. Consistent with the scales of spatial coherence from MODIS data, in both data sets power spectra indicate AOD variability is manifest primarily on synoptic and annual time scales. Conversely, AE variability in MODIS data is primarily focused on seasonal, semiannual, and annual time scales, and there is an additional mode of AE variability at approx. 30 days in the AERONET measurements. The frequency of cooccurrence of extreme AOD values (>local 90th percentile) decreases to below 50% at approx. 150 km from a central grid cell, but is above that expected by random chance over almost all of eastern North America, indicating supra-regional scale extreme events.

MODIS↗
NASA NTRS2016

Validation of AIRS V6 Surface Temperature over Greenland with GCN and NOAA Stations

This work compares the temporal and spatial characteristics of the AIRSAMSU (Atmospheric Infrared Sounder Advanced Microwave Sounding Unit A) Version 6 and MODIS (Moderate resolution Imaging Spectroradiometer) Collection 5 derived surface temperatures over Greenland. To estimate uncertainties in space-based surface temperature measurements, we re-projected the MODIS Ice Surface Temperature (IST) to 0.5 by 0.5 degree spatial resolution. We also re-gridded AIRS Skin Temperature (Ts) into the same grid but classified with different cloud conditions and surface types. These co-located data sets make intercomparison between the two instruments relatively straightforward. Using this approach, the spatial comparison between the monthly mean AIRS Ts and MODIS IST is in good agreement with RMS 2K for May 2012. This approach also allows the detection of any long-term calibration drift and the careful examination of calibration consistency in the MODIS and AIRS temperature data record. The temporal correlations between temperature data are also compared with those from in-situ measurements from GC-Net (GCN) and NOAA stations. The coherent time series of surface temperature evident in the correlation between AIRS Ts and GCN temperatures suggest that at monthly time scales both observations capture the same climate signal over Greenland. It is also suggested that AIRS surface air temperature (Ta) can be used to estimate the boundary layer inversion.

AIRS↗
NASA NTRS2016

Consistency of Vegetation Index Seasonality Across the Amazon Rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

MAIAC↗
NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗
NASA NTRS2021

Assessment of aerosol burden over Ghana

Although air pollution in Ghana is ranked number one in environmental health threats to public health and sixth to cause of deaths, routine monitoring is rare. This paper presents fourteen years (2005-2018) assessment of aerosol optical depth (AOD) at 3 km resolution from MODIS Aqua and Terra satellites to ascertain the Spatio-temporal and seasonal distribution of aerosols over Ghana and its major cities. The MODIS AOD at 3 km were validated against ground-based Aerosol Robotic Network (AERONET) AODs to ascertain the suitability of the MODIS 3 km data for air quality application in the region. The contribution of distant aerosols to city aerosol loadings was also assessed with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backscatter model. A moderate-high aerosol burden (AODs ~ 0.50) was observed over Ghana with a significant contribution from the pre-monsoon season. City centres of Takoradi and Kumasi showed higher aerosol loads (AODs ~ 0.80) than Accra and Tamale. The HYSPLIT model showed that distant or transported aerosol sources to the city centres were of both marine and land generated origins. Linear regression analysis between MODIS AOD and AERONET AOD showed a reasonably good correlation of ~ 0.60 for Aqua and Terra. From the validation analysis, both Aqua and Terra satellites can be used for air quality monitoring over Ghana; however, more ground research must be conducted to ascertain better aerosol model assumptions for the region.

Aerosols↗
NASA NTRSDate not supplied

FluxSat: Long-term Earth Science Data Record (ESDR) for Terrestrial Gross Primary Production (GPP) based on satellite data calibrated with eddy covariance data

Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.

gross Primary Production↗
DOE OSTI2021

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗
DOE OSTI2023

Evaluation of E3SM land model snow simulations over the western United States

Abstract. Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125∘ resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product), the spatially and temporally complete (STC) snow-covered area and grain size (MODSCAG) and MODIS dust and radiative forcing in snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the snow property inversion from remote sensing (SPIReS) product and two data assimilation products of snow water equivalent and snow depth – i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability, and elevation gradients for different snow properties including snow cover fraction (fsno), surface albedo (αsur) over snow cover regions, snow water equivalent (SWE), and snow depth (Dsno). However, there are large biases of fsno with dense forest cover and αsur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size, and light-absorbing particle-induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of −20.7 mm (−35.9 %) and −20.4 mm (−35.5 %), respectively, for spring, and −13.8 mm (−27.8 %) and −10.2 mm (−22.2 %), respectively, for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69 but negative mean biases of −122.7 mm. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset dates by 17.3 and 12.4 d, earlier snow end dates by 35.5 and 26.8 d, and shorter snow durations by 52.9 and 39.5 d, respectively. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

The moderate resolution imaging spectrometer data system

Science requirements for a moderate resolution imaging spectrometer (MODIS) data system are outlined, as well as the operations concept and system specifications based on these requirements. Two systems are considered - MODIS-N for a 40-channel nadir-viewing scanner and MODIS-T for a 64-channel tiltable scanner. The first system collects data from 15 thermal-infrared channels at all times and from 25 reflected-energy channels during daytime, while MODIS-T will take data from 64 channels on a 100-percent duty cycle during daytime only. Standard data products, data acquisition and processing, and requirements on data are discussed. Emphasis is placed on the interfaces between the science data processing facilities and the scientific users.

Han, Daesoo↗
NASA NTRS1992

EOS Moderate Resolution Imaging Spectroradiometer - Phase C/D status and comments on calibration and georeferencing approaches

The Moderate Resolution Imaging Spectroradiometer-Nadir (MODIS-N) is a key observing instrument to be flown on the Earth Observing System (EOS). The MODIS-N is being built under contract to NASA by the Santa Barbara Research Center. MODIS-N is a 36-band instrument with bands chosen in the visible through the emitted thermal IR part of the electromagnetic spectrum to observe key features of the Earth's land and ocean surfaces plus the properties of clouds and other constituents in the troposphere. To provide improved radiometric accuracy, several on-board approaches are used to monitor the calibration of the instrument. The calibration accuracy requirements are 5 percent absolute in the reflected solar radiation observations, 2 percent relative to the sun, and 1 percent absolute in the emitted thermal IR. Requirements for georeferencing of observations are still under study. At the present time, it appears that MODIS-N pixels can be georeferenced to better than 500 meters based on the EOS pointing knowledge capabilities.

Salomonson, Vincent V.↗