SEARCH · Search NASA

Results for “Sentinel 3”

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.

SourceResearch connectionFrom yearThrough year

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

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS2024

Clear Lake Volcanic Field Disasters: Creating a Deformation Record Using InSAR to Assess Hazards and Detect Volcanic Unrest in Clear Lake Volcanic Field

Clear Lake Volcanic Field (CLVF) in northern California is at a high threat potential for volcanic hazards. Eruptions leading to increased seismic activity could result in silicic domes, cinder cones, and flows that would be dangerous to the residential areas near the volcanic field. Remotely sensed Earth observations can reveal volcanic processes in the subsurface, which are essential to the timely monitoring of potential volcanic activity. In particular, Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Digital Elevation Model (DEM) data capture relative surface deformation at unprecedented high spatial and temporal resolutions. Leveraging C-SAR and DEMs, we conducted interferometric analysis from January 2016 to December 2023. Our results demonstrate 1) the mean surface displacement velocity of the Clear Lake Volcanic Field is measured to undergo 5 to 10-centimeter scale deformation and shows a strong relationship with the surrounding faults, 2) apparent seasonal differences in rates of surface change, and 3) seismic activity associated with the geyser geothermal field has a strong association with cumulative surface displacement, with active fault zones having 2 to 5 cm of additional displacement. Results indicate that deformation is linked to deep pressure sources causing stresses on the surficial environment that should be considered in hazard mitigation. This study provides a baseline of historic deformation, aiding hazard analysts in communication efforts and streamlining decision-making for potential risks to region residents.

remote sensing↗

DOE OSTI2021

Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery

As the spatial and temporal resolution of remotely sensed imagery has improved over the last four decades, algorithms for monitoring and mapping seasonal changes in surface properties have evolved rapidly. Most recently, the availability of daily PlanetScope imagery has created new opportunities for monitoring the land surface phenology (LSP) of terrestrial ecosystems at high spatial resolution. However, the quality and value of LSP information from PlanetScope imagery have not been systematically examined. In this paper, we evaluate the character and quality of LSP information derived from PlanetScope by comparing time series of vegetation indices and LSP metrics from PlanetScope to corresponding time series and LSP metrics derived from Harmonized Landsat 8 and Sentinel-2 (HLS) imagery and PhenoCams at six sites that span a diverse range of land cover types and climate. Results show that vegetation index time series from all three data sources show high temporal correlation, and LSP metrics derived from HLS, PlanetScope, and PhenoCam show high agreement with negligible bias. Semi-variograms for phenometrics estimated from PlanetScope imagery indicate that the majority of spatial variance captured in PlanetScope phenometrics occurs well below the spatial resolution HLS imagery. At the same time, LSP metrics from HLS are most strongly correlated with the 50–75% quantiles of 3 m LSP metrics from PlanetScope. This indicates that HLS captures the average phenology at sub-pixel scale captured in PlanetScope imagery. Here, our results represent the first comprehensive comparison of LSP metrics estimated from PlanetScope and publicly available moderate spatial resolution imagery, and provide insights regarding: (1) the quality and character of LSP metrics derived from HLS and PlanetScope; and (2) the relative merits and trade-offs associated with the use of each data source for LSP studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates

Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In this study, we used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). We then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, we found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Uptake, Efflux, and Sequestration of Mercury in the Asian Clam, Corbicula fluminea , at Environmentally Relevant Concentrations, and the Implications for Mercury Remediation

(1) Mercury (Hg) is a persistent, ubiquitous contaminant that readily biomagnifies into higher trophic level species in aquatic environments across the globe. It is crucial to understand the movement of environmentally relevant concentrations of Hg in impacted freshwater streams to minimize risks to ecological and human health. (2) The bioconcentration kinetics of aqueous Hg exposure (20, 100, and 200 ng/L) in the invasive Asian Clam, Corbicula fluminea, were measured. A toxicokinetic model, the first parameterized for Hg accumulation in freshwater clams, was developed to estimate uptake and efflux parameters and compared to previous parameter values estimated for other mollusk species. (3) Results demonstrated that even at low Hg concentrations, Corbicula record signals of contamination through bioconcentration, and both direct measurement and toxicokinetic models demonstrate large Hg bioconcentration factors (as high as 1.34 × 10 5 mL/g dry tissue), similar to partitioning coefficients seen in engineered Hg sorbents. (4) Our study found that Corbicula accumulated Hg at aqueous concentrations relevant to impacted streams, but well below regulatory drinking water limits, demonstrating their utility as a sensitive sentinel species and potential bioremediator.

bioaccumulation↗

NASA NTRS2017

Satellite Monitoring of Cyanobacterial Harmful Algal Bloom Frequency in Recreational Waters and Drinking Water Sources

Cyanobacterial harmful algal blooms (cyanoHAB) cause extensive problems in lakes worldwide, including human and ecological health risks, anoxia and sh kills, and taste and odor problems. CyanoHABs are a particular concern in both recreational waters and drinking water sources because of their dense biomass and the risk of exposure to toxins. Successful cyanoHAB assessment using satellites may provide an indicator for human and ecological health protection. In this study, methods were developed to assess the utility of satellite technology for detecting cyanoHAB frequency of occurrence at locations of potential management interest. The European Space Agency's MEdium Resolution Imaging Spectrometer (MERIS) was evaluated to prepare for the equivalent series of Sentinel-3 Ocean and Land Colour Imagers (OLCI) launched in 2016 as part of the Copernicus program. Based on the 2012 National Lakes Assessment site evaluation guidelines and National Hydrography Dataset, the continental United States contains 275,897 lakes and reservoirs greater than 1 ha in area. Results from this study show that 5.6% of waterbodies were resolvable by satellites with 300 m single-pixel resolution and 0.7% of waterbodies were resolvable when a three by three pixel (3 x 3-pixel) array was applied based on minimum Euclidian distance from shore. Satellite data were spatially joined to U.S. public water surface intake (PWSI) locations, where single-pixel resolution resolved 57% of the PWSI locations and a 3 x 3-pixel array resolved 33% of the PWSI locations. Recreational and drinking water sources in Florida and Ohio were ranked from 2008 through 2011 by cyanoHAB frequency above the World Health Organizations (WHO) high threshold for risk of 100,000 cells m/L. The ranking identified waterbodies with values above the WHO high threshold, where Lake Apopka, FL (99.1%) and Grand Lake St. Marys, OH (83%) had the highest observed bloom frequencies per region. The method presented here may indicate locations with high exposure to cyanoHABs and therefore can be used to assist in prioritizing management resources and actions for recreational and drinking water sources.

Cyanobacteria↗

NASA NTRS2020

Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Total suspended solids↗

NASA NTRS2005

Vacuum Deployment and Testing of a 4-Quadrant Scalable Inflatable Solar Sail System

Solar sails reflect photons streaming from the sun and transfer momentum to the sail. The thrust, though small, is continuous and acts for the life of the mission without the need for propellant. Recent advances in materials and ultra-low mass gossamer structures have enabled a host of useful missions utilizing solar sail propulsion. The team of L'Garde, Jet Propulsion Laboratories, Ball Aerospace, and Langley Research Center, under the direction of the NASA In-Space Propulsion office, has been developing a scalable solar sail configuration to address NASA s future space propulsion needs. The baseline design currently in development and testing was optimized around the 1 AU solar sentinel mission. Featuring inflatably deployed sub-T(sub g), rigidized beam components, the 10,000 sq m sail and support structure weighs only 47.5 kg, including margin, yielding an areal density of 4.8 g/sq m. Striped sail architecture, net/membrane sail design, and L'Garde's conical boom deployment technique allows scalability without high mass penalties. This same structural concept can be scaled to meet and exceed the requirements of a number of other useful NASA missions. This paper discusses the interim accomplishments of phase 3 of a 3-phase NASA program to advance the technology readiness level (TRL) of the solar sail system from 3 toward a technology readiness level of 6 in 2005. Under earlier phases of the program many test articles have been fabricated and tested successfully. Most notably an unprecedented 4-quadrant 10 m solar sail ground test article was fabricated, subjected to launch environment tests, and was successfully deployed under simulated space conditions at NASA Plum Brook s 30m vacuum facility. Phase 2 of the program has seen much development and testing of this design validating assumptions, mass estimates, and predicted mission scalability. Under Phase 3 a much larger 20 m square test article including subscale vane has been fabricated and tested. A 20 m system ambient deployment has been successfully conducted after enduring Delta-2 launch environment testing. The program will culminate in a vacuum deployment of a 20 m subscale test article at the NASA Glenn s Plum Brook 30 m vacuum test facility to bring the TRL level as close to 6 as possible in 1 g. This focused program will pave the way for a flight experiment of this highly efficient space propulsion technology.

Lichodziejewski, David↗

NASA NTRSDate not supplied

Creating a Deformation Time Series Utilizing InSAR to Assess Hazards and Detect Volcanic Unrest in Clear Lake Volcanic Field, California

According to the U.S Geological Survey, the Clear Lake Volcanic Field (CLVF) in northern California is at a high threat potential for volcanic hazards. Eruptions leading to increased seismic activity could result in silicic domes, cinder cones, and flows that would be dangerous to the residential areas near the volcanic field. Remotely sensed Earth observations can reveal volcanic processes in the subsurface, which are essential to the timely monitoring of potential volcanic activity. In particular, Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Digital Elevation Model (DEM) data capture relative surface deformation at unprecedented high spatial and temporal resolutions. Leveraging C-SAR and DEMs, we conducted interferometric analysis from January 2016 to December 2023. Our results demonstrate 1) the mean surface displacement velocity of the Clear Lake Volcanic field is measured to undergo 5 to 10-centimeter scale deformation and shows a strong correlation with the surrounding faults, 2) apparent seasonal differences in rates of surface change, and 3) seismic activity associated with the geyser geothermal field has a strong correlation with cumulative surface displacement, with active fault zones having 2 to 5 cm of additional displacement. Results indicate that deformation is linked to deep pressure sources causing stresses on the surficial environment that should be considered in hazard mitigation. This study provides a baseline of historic deformation, aiding hazard analysts in communication efforts and streamlining decision-making for potential risks to region residents.

Ivan Tochimani-Hernandez↗

DOE OSTI2025

Drought-induced changes in groundwater-surface water exchange at Lake Mead area

This study focuses on the Lake Mead region in the southwestern United States, a key water reservoir serving over 25 million people and agricultural lands across several states. The area has experienced recurring anthropogenic droughts since the early 2000s. We investigate the hydrological response of the coupled surface-groundwater system to the 2020–2022 drought, one of the most severe on record. To this end, we use Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data to quantify vertical land motion caused by the elastic response of the crust to water-mass loss in the lake vicinity. Next, we apply an inverse elastic load modeling framework to quantify water loss. We further assess possible hydraulic connectivity between Lake Mead and adjacent groundwater reservoirs. We detect ground uplift of up to 8 mm/yr near the lake center, likely due to crustal rebound from reduced water-mass loading. We estimated the total water storage loss at 3.03 ± 0.25 km 3 /yr across a 3150 km 2 area surrounding Lake Mead. Groundwater accounts for approximately a third of that, being 0.94 ± 0.32 km 3 /yr. In addition, observed time lags of 6–98 days between lake and groundwater level responses, corresponding to a lateral diffusivity of 3.2–86 m 2 /s, suggest spatially variable connectivity between the lake and aquifers. These findings highlight that drought impacts propagate through the subsurface within interconnected systems, resulting in reduced buffering capacity of groundwater resources following droughts, and emphasizing the need for more integrated surface and groundwater management strategies to enhance resilience under climate and anthropogenic stressors.

58 GEOSCIENCES↗

NASA NTRSDate not supplied

Exploring Anomalous PM 2.5 from Wildfires and Dust Storms using Data and Services at NASA GES DISC

The presence of fine particles in the atmosphere with a diameter of less than 2.5 µm, called particulate matter 2.5 (PM 2.5 ), poses a significant threat to human health as a criteria air pollutant. Fortunately, NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) provides easy access to several PM 2.5 concentration products. These datasets include the reanalysis of global hourly and monthly aerosol components including PM 2.5 data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), as well as 3-hourly real-time ensemble forecasts of PM 2.5 from the Hazardous Air Quality Ensemble System (HAQES). The HAQES products are developed by the George Mason University Air Quality Laboratory as part of NASA's Health Air Quality Applied Science Team (HAQAST). The GES DISC is actively collaborating with scientists in the HAQAST program to further expand air quality data collections. Two new datasets are currently being archived: one is the machine learning-based global hourly PM 2.5 derived from MERRA-2; the other is the localized data (NO 2 , O 3 , and PM 2.5 ) time series derived from NASA's GEOS Composition Forecasting (GEOS-CF) system. In this presentation, we will explore the spatial patterns and long-distance transport characteristics of elevated PM 2.5 during extreme pollution events, such as the June 2023 Canadian wildfires, which are still active at the time of writing; and severe spring dust storms in 2023 over Asia. To gain comprehensive insights, we will utilize various PM 2.5 data in conjunction with satellite-observed aerosol data from TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5P. The primary focus of this presentation will be to demonstrate effective use of data tools and services to visualize and explore extreme air pollution phenomena. Additionally, we will provide guidance on how users can download specific data of interest, facilitating further analysis and research in this critical area.

air quality↗

NASA NTRS2018

Remote Sensing Applications for Water Resources Management, Including Droughts, Floods, and Associated Water Cycle Extremes II

Water resources management can benefit from applications of remote sensing and hydrologic models. These tools can be especially valuable during extreme events and in data-sparse regions. Observational platforms include the GPM, SMAP, Terra, Aqua, Landsat, GRACE, and Sentinel satellites, and other satellite and airborne platforms. They can support the operational water resources management community in responding to climate change, increases in climate variability and the frequency of extreme events. This session will highlight advances in the use of satellite, airborne and ground-based sensor networks to: measure the quantity/quality of hydrologic resources in the U.S. and internationally; provide information to water managers to improve water resources management; and support risk-based decision making. Topics of interest include (1) extreme events such as floods and droughts; (2) water supply and snow water resource monitoring and forecasting; (3) evapotranspiration, soil moisture, groundwater, and agricultural water management; (4) water quality and (5) global water sustainability.

Bolten, John D.↗

NASA NTRS2018

H53E: Remote Sensing Applications for Water Resources Management, Including Droughts, Floods, and Associated Water Cycle Extremes I

Remote Sensing↗

NASA NTRS2020

NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%.

Chirayath, Ved↗

NASA NTRS2023

Lake Anna Water Resources: Using NASA Earth Observations to Identify Algal Event Risk Factors in Lake Anna and Help Inform Future Management Practices

Lake Anna is a man-made reservoir and popular recreation destination that spans over 13,000 acres-9,600 public and 3,400 private-in the Piedmont region of Virginia. The Lake has recently seen a rise in documented harmful algal blooms (HABs), which pose a variety of community and ecological concerns and are often exacerbated by anthropogenic factors, such as excess nutrient loads from agricultural runoff. NASA DEVELOP has partnered with the Virginia Department of Environmental Quality (DEQ) to help monitor cyanobacteria and nutrient pollution indicators across Lake Anna. The team utilized Earth observations (EO) and in situ ancillary data to identify and monitor algal bloom trends. The team used Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-2 Multispectral Instrument (MSI), and Sentinel-3 Ocean and Land Color Instrument (OLCI) to analyze water quality variables such as chlorophyll-a, turbidity, surface temperature, and cyanobacteria. Due to the lack of comprehensive in situ data and historic HAB event records, our ability to compare and validate EOs was limited. Additionally, deficient spatial resolutions, along with a geographically complicated shoreline, accentuated the spatial constraints we faced in our analysis. After examining EOs, our results indicated conditions conducive to the formation of HABs within the upper reaches of Lake Anna. Yet, spatially dependent limiting factors may have also influenced where these phenomena developed. When used in concert with existing in situ datasets, NASA EOs provide relevant stakeholders with more comprehensive analyses with which to engage in enhanced monitoring and watershed management.

cyanobacteria↗

DOE OSTI2024

Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance

Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C 4 ), Chloridoideae (C 4 ), and Pooideae (C 3 ). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. Here, we assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf-level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite-based spectrometers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

PAVC Gridded 20m Alaska NGEE Tier3 PFTs v1.0

These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Assimilation of Sentinel 1 and SMAP – Based Satellite Soil Moisture Retrievals into SWAT Hydrological Model: the Impact of Satellite Revisit Time and Product Spatial Resolution on Flood Simulations in Small Basins

In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km2) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR obtained via S1 over ASCAT was small likely due to very challenging areas for S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the soil moisture pre-storm conditions.

Small basins↗

NASA NTRS2019

Assimilation of Sentinel 1 and SMAP–based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km2) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via the Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR-SWI obtained via S1 over ASCAT was small, likely due to very challenging areas for the S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the pre-storm soil moisture conditions.

Shima Azimi↗