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NASA NTRS2017

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data.

da/night band↗
NASA NTRS2017

Postlaunch Assessment of the Response Versus Scan Angle for the Thermal Emissive Bands of Visible Infrared Imaging Radiometer Suite On-Board the Suomi National Polar-Orbiting Partnership Satellite

The visible infrared imaging radiometer suite (VIIRS) is a key sensor carried on the Suomi national polar-orbiting partnership (S-NPP) satellite, which was launched in October 2011. It has several on-board calibration components, including a solar diffuser and a solar diffuser stability monitor for the reflective solar bands, a V-groove blackbody for the thermal emissive bands (TEB), and a space view port for background subtraction. These on-board calibrators are located at fixed scan angles. The VIIRS response versus scan angle (RVS) was characterized prelaunch in lab ambient conditions and is currently used to characterize the on-orbit response for all scan angles relative to the calibrator scan angle. Since the RVS is vitally important to the quality of calibrated radiance products, several independent studies were performed to analyze the prelaunch RVS measurement data. A spacecraft level pitch maneuver was scheduled during the first 3 months of intensive Cal/Val. The S-NPP pitch maneuver provided a rare opportunity for VIIRS to make observations of deep space over the entire range of Earth view scan angles, which can be used to characterize the TEB RVS. This study provides our analysis of the pitch maneuver data and assessment of the derived TEB RVS by comparison with prelaunch results. In addition, the stability of the RVS after the first 5 years of operation is examined using observed brightness temperatures (BT) over a clear ocean at various angles of incidence (AOI). To reduce the impact of variations in the BT measurements, the daily overpasses collected over the ocean are screened for cloud contamination, normalized to the results obtained at the blackbody AOI, and averaged each year.

Wu, Aisheng↗
NASA NTRS2018

NASA's Black Marble Nighttime Lights Product Suite

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 meters resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF (Bidirectional Reflectance Distribution Function) effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.

Suomi-NPP↗
NASA NTRS2018

Correcting for Trace Gas Absorption When Retrieving Aerosol Optical Depth from Satellite Observations of Reflected Shortwave Radiation

Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, onboard Suomi-NPP) sensors, use wavelength bands in "window" regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper we use High-resolution TRANsmission (HITRAN) database and Line-by-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are "similar", they are different enough that applying MODIS specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases up to 0.07. As recent studies are attempting to create a long-term data record by joining multiple satellite datasets, including MODIS and VIIRS, the consistency of gas correction becomes even more crucial.

Satellite Remote Sensing↗
NASA NTRS2017

Earth-observing Satellite Intercomparison Using the Radiometric Calibration Test Site at Railroad Valley

This paper describes the current ground-based calibration results of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi National Polar orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS), and Sentinel-2A Multispectral Instrument (MSI), using an automated suite of instruments located at Railroad Valley, Nevada, USA. The period of this study is 2012 to 2016 for MODIS, VIIRS, and ETM+, 2013 to 2016 for OLI, and 2015 to 2016 for MSI. The current results show that all sensors agree with the Radiometric Calibration Test Site (RadCaTS) to within +/-5% in the solar-reflective regime, except for one band on VIIRS that is within +/-6%. In the case of ETM+ and OLI, the agreement is within +/-3%, and, in the case of MODIS, the agreement is within +/-3.5%. MSI agrees with RadCaTS to within +/-4.5% in all applicable bands.

Reflectance↗
NASA NTRS2018

NASA Provides the Capability to Deliver Near Real-Time JPSS Data to Users in Order to Monitor Time-Sensitive Applications Such as Wildfires, Floods, Volcanic Eruptions, Tropical Cyclones and Extreme Weather Events

NASA's Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE https://earthdata.nasa.gov/lance) serves near real time (NRT) data to monitor time sensitive applications such as monitoring wildfires, floods, volcanic eruptions, tropical cyclones and extreme weather events. It currently serves data and imagery from the Visible Infrared Imager Radiometer Suite (VIIRS) and Ozone Mapping and Profiler Suite (OMPS) S NPP (Suomi National Polar-orbiting Partnership) instruments and is in the process of integrating continuity data products from VIIRS and OMPS onboard the Joint Polar Satellite System (JPSS), via the JPSS data Hub, to continue to meet the needs of agencies, scientists and members of the general public. NASA's Earth Science Division (ESD) sponsored the EOSDIS development of LANCE in 2009 to provide a central point of access to high quality NRT data products and imagery for applications users. LANCE makes data available to the public within 3 hours of satellite observation and imagery within 4-5 hours of satellite observation. Full resolution browse imagery from LANCE are provided through the Global Imagery Browse Services (GIBS) which also fuels NASA's Worldview tool so that users can interactively browse near real time data. This data supports time critical applications and allows users to view current natural hazards and events and animate the imagery over time.

Near real time↗
NASA NTRS2018

NASA's Black Marble Product Suite: Validation Strategy

NASA's Black Marble nighttime lights product suite (VNP46) is available at 500m resolution since January 2012 with data fro the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-terrain, vegetation, snow, lunar and stray light corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Current and planned validation activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at difference geographic locations and time periods representing the full range of retrieval conditions.

GEO Human Planet↗
NASA NTRS2019

Improving Satellite Global Chlorophyll a [alpha] Data Products Through Algorithm Refinement and Data Recovery

A recently developed algorithm to estimate surface ocean chlorophyll a concentrations (Chl in milligrams per cubic meter), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl maps. The algorithm is a hybrid between a band‐difference color index algorithm for low‐Chl waters and the traditional band‐ratio algorithms (OCx) for higher‐Chl waters. In this study, the OCI algorithm is revisited for its algorithm coefficients and for its algorithm transition between color index and OCx using a merged data set of high‐performance liquid chromatography and fluorometric Chl. Results suggest that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for Chl less than 0.05 milligrams per cubic meter, but smoother algorithm transition for Chl between 0.25 and 0.40 milligrams per cubic meter. Evaluation using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and cross‐sensor consistency between SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MODISA (Moderate Resolution Imaging Spectroradiometer on Aqua), and VIIRS (Visible Infrared Imaging Radiometer Suite) over the OCx algorithms for oligotrophic oceans. Mean cross‐sensor difference in monthly Chl data products over global oligotrophic oceans reduced from approximately 10 percent for OCx to 1-2 percent for OCI2. More importantly, data statistics suggest that the current straylight masking scheme used to generate global Chl maps can be relaxed from 7 by 5 to 3 by 3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (R (sub rs) by lambda (sensor wavelength), per steradian (sr (sup −1)) for not just oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average relative increase of 39 percent in data quantity for global oceans, thus making it possible to reduce data product uncertainties and fill data gaps.

Hu, Chuanmin↗
NASA NTRS2019

Thirty-Six Combined Years of MODIS Geolocation Trending

Two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been in operations for more than 19 and 17 years (thus 36 combined years) as part of NASA's Earth Observing System (EOS) on the Terra platform that was launched in December 1999 and on the Aqua platform that was launched in May 2002, respectively. Accurate geolocation is a critical element needed for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved MODIS geolocation accuracy in Collection-5 (C5), C6 and C6.1 re-processing and forward-processing data streams. We improved geolocation accuracy in the re-processed data and corrected for geolocation biases found in forward-processed data, including those caused by operations such as the stop-go-stop status of the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument on the Aqua platform. We discuss scan-toscan underlaps near nadir over the equator regions that was discovered in checking the non-underlapping requirement in the Visible Infrared Imaging Radiometer Suite (VIIRS) based on trending parameters from the actual Suomi National Polar-orbiting Partnership (S-NPP) satellite orbit. The underlaps are closely tied to instrument effective focal length that is measured from on-orbit data using a technique we recently developed. We also discuss potential improvements for the upcoming C7 re-processing.

ground control point↗
NASA NTRS2019

Determination of Response Versus Scan Angle for the NOAA-20 Visible Infrared Imaging Radiometer Suite Thermal Emissive Bands from Emissive Radiation Measurements During the Pitch Maneuver

The visible infrared imaging radiometer suite (VIIRS) is a key sensor onboard the newly launched (November 18, 2017) NOAA-20 satellite and the early launched Suomi National Polar-orbiting Partnership (SNPP) satellite. The two VIIRS sensors are nearly identical in design. Its onboard calibration components include a solar diffuser (SD) and a SD stability monitor for the reflective solar bands, a V-grooved blackbody for the thermal emissive bands (TEBs),and a space view port for background subtraction. These onboard calibrators are located at fixed scan angles. The response versus scan angle (RVS) of the VIIRS scan mirror was characterized prelaunch in lab-ambient conditions and is currently used to calibrate the on-orbit response for all scan angles relative to the calibrator's scan angle. A spacecraft-level pitch maneuver was scheduled during the initial intensive calibration and validation testing for both the NOAA-20 and SNPP. The pitch maneuver provided a rare opportunity for VIIRS to make observations of deep space over the entire range of the scan angle, which can be used to characterize the TEBRVS. We analyze the NOAA-20 pitch maneuver data and assess the derived TEB RVS. A comparison between the RVS determined by the pitch maneuver observations and prelaunch lab measurements is conducted for each band, detector, and mirror side of the half-angle mirror.

Wu, Aisheng↗
NASA NTRS2019

Using GES DISC Data to Study Kilauea Volcano of 2018

Kilauea volcano in Hawaii which erupted in early May 2018 injected massive amount of SO2 and ash into the atmosphere. The lava flow during the eruption destroyed many home and neighborhoods. The SO2 plume during the eruption of Kilauea volcano is analyzed from May to August 2018 using multiple satellite products such as Level 2 TROPspheric Monitoring Instrument (TROPOMI) and Level 3 Ozone Monitoring Instrument (OMI) from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). GES DISC hosts multi-disciplinary Earth science data sets that can be used to analyze natural disasters, such as the Kilauea volcano. Additionally, GES DISC's Giovanni tool can be used to visualize these data. We acquired OMI through the subsetting function, which is processed by the GES DISC in-house developed backend software Level3/4 Regrider and Subsetter (L34RS) and TROPOMI using OPeNDAP.Data from the OMI OMSO2e product showed elevated levels of SO2 amounts during the eruption between May to August 2018. Similarly, ground-based stations at Hawaii Volcanoes National Park recorded higher SO2 concentrations during the same time period. This study uses wind direction from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) to analyze the transport and dispersion of SO2 plume and map lava flows from the volcano using thermal images from Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, satellite observations combined with socioeconomic and public health data are used to analyze its impact in public health.

KC, Binita↗
NASA NTRS2017

The Effects of Different Footprint Sizes and Cloud Algorithms on the Top-Of-Atmosphere Radiative Flux Calculation from the Clouds and Earth’s Radiant Energy System (CERES) Instrument on Suomi National Polar-Orbiting Partnership (NPP)

Only one Clouds and Earth’s Radiant Energy System (CERES) instrument is onboard the Suomi National Polar-orbiting Partnership (NPP) and it has been placed in cross-track mode since launch; it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs are used for flux inversions for NPP CERES measurements. However, the footprint size of NPP CERES is greater than that of Aqua CERES, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), which are the imagers sharing the spacecraft with NPP CERES and Aqua CERES, are also different. To quantify the flux uncertainties due to the footprint size difference between Aqua CERES and NPP CERES, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel-level data, which are convolved with the Aqua CERES and NPP CERES point spread functions (PSFs) into their respective footprints. The simulation is designed to isolate the effects of footprint size and cloud property differences on flux uncertainty from calibration and orbital differences between NPP CERES and Aqua CERES. The footprint size difference between Aqua CERES and NPP CERES introduces instantaneous flux uncertainties in monthly gridded NPP CERES measurements of less than 4.0 W/sq. m for SW (shortwave) and less than 1.0 W/sq. m for both daytime and nighttime LW (longwave). The global monthly mean instantaneous SW flux from simulated NPP CERES has a low bias of 0.4 W/sq. m when compared to simulated Aqua CERES, and the root-mean-square (RMS) error is 2.2 W/sq. m between them; the biases of daytime and night- time LW flux are close to zero with RMS errors of 0.8 and 0.2 W/sq. m. These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property (cloud fraction and optical depth) differences are considered, the uncertainties of monthly gridded NPP CERES SW flux can be up to 20 W/sq. m in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated NPP CERES has a high bias of 1.1 W/sq. m and the RMS error increases to 5.2 W/sq. m. LW flux shows less sensitivity to cloud property differences than SW flux, with uncertainties of about 2 W/sq. m in the monthly gridded LW flux, and the RMS errors of global monthly mean daytime and nighttime fluxes increase only slightly. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record.

Su, Wenying↗
NASA NTRS2019

Characterization and Application of Artificial Light Sources for Nighttime Aerosol Optical Depth Retrievals using the Visible Infrared Imager Radiometer Suite Day/Night Band

Using nighttime observations from Visible Infrared Imager Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions, and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East and India) during 2015. Reason-able agreement is found between nighttime AOTs from the VIIRS DNB and temporally adjacent daytime AOTs from the AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.

Jianglong Zhang↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27483-DND

scene to retrieve their microphysical properties. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the infrared channels (6.72 Âμm, 7.33 Âμm, 13.3 Âμm,13.6 Âμm, 13.9 Âμm, and 14.2 Âμm) available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) to build its algorithms to retrieval cloud properties. Unfortunately, The Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi-NPP (National Polar-Orbiting Partnership) lacks these particular measurements. While conducting research using VIIRS data, it is desirable to bring these data into the VIIRS resolution. The Cross-track Infrared Sounder (CrIS), which is also on board Suomi-NPP, has these infrared channels. This paper describes a method to map the CrIS spectral radiances of CO2 and water vapor to VIIRS, with the mapping software in ATOVS and AVHRR Pre-processing (AAPP) package provided by Satellite Application Facilities for Numerical Weather Prediction (NWP SAF). This method is validated comparing mapped10.763 Âμm from CrIS with M15 from VIIRS. The mapped CO2 and water vapor channels were then created and combined with the conventional VIIRS product, as well as compared against corresponding channels of Aqua MODIS instrument.

Yan Chen↗
NASA NTRS2020

Lambayeque Water Resources: Assessing Hydrologic Patterns Using NASA Earth Observations to Address Tree Mortality in Peru’s Coastal Mesquite Forests

The mesquite (Prosopis sp.) forests in Northwestern Peru have had a significant increase in tree mortality in the past fifty years. Within this time frame, 17% of the forest extent was lost and the forest saw an average annual declination rate of 0.33% (Ektvedt, Vetaas, & Lundberg, 2012). These habitats support the region's rich biodiversity and play an important role in local community economies. Several hydrologic causes for mesquite mortality have been hypothesized, but local researchers lack spatially comprehensive techniques to address the problem. While in situ research is currently being utilized in an attempt to explain this recent anomaly, landscape-level visualizations through remote sensing have not been produced to find connections between hydrologic trends and the health of Northwestern Peru’s mesquite forests. Climate Hazards Group InfraRed Precipitation with Station data and Global Land Data Assimilation System data were analyzed to assess hydrologic patterns pertaining to precipitation and soil moisture in the Lambayeque region of Peru. These data were then paired with vegetation indices derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to display how changing hydrologic patterns relate to the health of mesquite trees. These data were then compiled on a monthly basis over the 30-year study period to create a time series product, which was later referenced with background research to find the likely causes of recent forest decline. The results will assist in the Lambayeque Regional Government’s understanding of recent biological declination and will help them design strategies to mitigate forest decline in Lambayeque, Peru and surrounding regions.

Water Resources↗
NASA NTRS2020

Lambayeque Water Resources: Assessing Hydrologic Patterns Using NASA Earth Observations to Address Tree Mortality in Peru’s Coastal Mesquite Forests

The mesquite (Prosopi ssp.) forests in Northwestern Peru have had a significant increase in tree mortality in the past fifty years. Within this time frame, 17% of the forest extent was lost and the forest saw an average annual declination rate of 0.33% (Ektvedt, Vetaas, & Lundberg, 2012). These habitats support the region's rich biodiversity and play an important role in local community economies. Several hydrologic causes for mesquite mortality have been hypothesized, but local researchers lack spatially comprehensive techniques to address the problem. While in situ research is currently being utilized in an attempt to explain this recent anomaly, landscape-level visualizations through remote sensing have not been produced to find connections between hydrologic trends and the health of Northwestern Peru’s mesquite forests. Climate Hazards Group InfraRed Precipitation with Station dataand Global Land Data Assimilation Systemdata were analyzed to assess hydrologic patterns pertaining to precipitation and soil moisture in the Lambayeque region of Peru. These data were then paired with vegetation indices derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to display how changing hydrologic patterns relate to the health of mesquite trees. These data were then compiled on a monthly basis over the 30-year study period to create a time series product, which was later referenced with background research to find the likely causes of recent forest decline. The results will assist in the Lambayeque Regional Government’s understanding of recent biological declination and will help them design strategies to mitigate forest decline in Lambayeque, Peru and surrounding regions.

Water Resources↗
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 NTRS2020

Hawai'i Water Resources: Utilizing NASA Earth Observations to Assess Ocean Conditions Leading to the Spread of the Nuisance Red Algae (Chondria tumulosa) in Papahānaumokuākea Marine National Monument, Hawai’i

Chondria tumulosa, a newly discovered red alga, was observed in low abundance in 2016 but has since proliferated and is now smothering and decimating vast expanse of coral reefs in Manawai, located in Papahānaumokuākea Marine National Monument (PMNM). If the spread persists, the outbreak of this cryptogenic species could potentially cause region-wide ecosystem degradation. In coordination with the U.S. Fish and Wildlife Service, Marine National Monuments of the Pacific and the National Oceanographic and Atmospheric Administration (NOAA) Office of National Marine Sanctuaries’ Papahānaumokuākea Marine National Monument, this project created a tool to analyze oceanographic conditions (sea surface temperature (SST), chlorophyll-a, water velocity, salinity, turbidity) across the Monument that could potentially be driving the algal spread. The Google Earth Engine tool enabled the partners to visualize oceanographic conditions and gather time-series graphs utilizing Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Colour Instrument (OLCI), Hybrid Coordinate Ocean Model (HYCOM) and NOAA's Climate Data Record in a user-friendly interface. The team used in situ SST data from subsurface temperature recorders provided by the partners to validate the tool's accuracy. Preliminary statistical analysis of MODIS data found warming trends in SST in Manawai as well as increased chlorophyll-a levels during the summer months in contrast to the control (non-infected) Lalo atoll. The tool did not aim to classify algal presence due to limited availability of higher resolution satellite imagery but instead enabled PMNM managers to monitor conditions that may be conducive to algal growth around the monument to make informed decisions and mitigation practices.

DEVELOP Project Summary↗