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By European Space Agency
Image: These summer images from the Copernicus Sentinel-2 and Sentinel-1 missions showcase different satellite views of Greenland’s west coast. View the full article
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By USH
The ongoing mystery and debate surrounding UFO and drone sightings across the U.S. continue to captivate public attention. The lack of transparency and definitive answers from government agencies combined with the apparent absence of military action against these drones, has fueled speculation about possible cover-ups or incompetence.
Local, county, and state governments seem to have no knowledge of who is operating these drones, where they originate, or their purpose. Despite this, officials confidently assert that "there is no credible threat." This raises the question: how can they be so certain? The reality suggests they cannot.
Recently, the Pentagon issued a statement following claims by a New Jersey congressman that Iran had deployed a "mothership" off the U.S. East Coast, launching drones. The Pentagon denied any military origin for the drones and ruled out links to known foreign entities, but questions persist about whether critical information is being withheld.
If these drones are not linked to Iran, the U.S., Russia, China, or any other nation, some experts propose they may be part of clandestine "deep state" programs. These programs could involve advanced aerospace technologies being tested by private companies under classified initiatives.
Witness accounts, including those from a New Jersey sheriff and Coast Guard officials, suggest the drones exhibit highly unusual behaviors. These include emerging from the ocean and performing movements like abrupt 90-degree turns—characteristics that could imply the use of advanced propulsion systems not publicly known.
Another theory posits that the drones may not be physical objects at all but rather holographic projections, akin to the controversial "Project Blue Beam" concept. If true, this would explain why attempts to intercept them could fail—they might not physically exist.
The sheer number, endurance, and sophistication of these drones hint at a coordinated operation. Some theorists believe this might be part of a psychological operation designed to distract from pressing political, economic, or social issues. The timing of such events often appears suspiciously aligned with periods of public, economic unrest or uncertainty.
In the event that the "deep state" is orchestrating these phenomena, some fear it could be a prelude to a false flag operation, with motives and consequences yet to be revealed.
The situation remains shrouded in speculation, leaving the public to grapple with more questions than answers.
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By NASA
6 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
This animation shows data taken by NASA’s PACE and the international SWOT satellites over a region of the North Atlantic Ocean. PACE captured phytoplankton data on Aug. 8, 2024; layered on top is SWOT sea level data taken on Aug. 7 and 8, 2024. NASA’s Scientific Visualization Studio One Earth satellite can see plankton that photosynthesize. The other measures water surface height. Together, their data reveals how sea life and the ocean are intertwined.
The ocean is an engine that drives Earth’s weather patterns and climate and sustains a substantial portion of life on the planet. A new animation based on data from two recently launched missions — NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the international Surface Water and Ocean Topography (SWOT) satellites — gives a peek into the heart of that engine.
Physical processes, including localized swirling water masses called eddies and the vertical movement of water, can drive nutrient availability in the ocean. In turn, those nutrients determine the location and concentration of tiny floating organisms known as phytoplankton that photosynthesize, converting sunlight into food. These organisms have not only contributed roughly half of Earth’s oxygen since the planet formed, but also support economically important fisheries and help draw carbon out of the atmosphere, locking it away in the deep sea.
“We see great opportunity to dramatically accelerate our scientific understanding of our oceans and the significant role they play in our Earth system,” said Karen St. Germain, director of the Earth Science Division at NASA Headquarters in Washington. “This visualization illustrates the potential we have when we begin to integrate measurements from our separate SWOT and PACE ocean missions. Each of those missions is significant on its own. But bringing their data together — the physics from SWOT and the biology from PACE — gives us an even better view of what’s happening in our oceans, how they are changing, and why.”
A collaboration between NASA and the French space agency CNES (Centre National d’Études Spatiales), the SWOT’ satellite launched in December 2022 to measure the height of nearly all water on Earth’s surface. It is providing one of the most detailed, comprehensive views yet of the planet’s ocean and its freshwater lakes, reservoirs, and rivers.
Launched in February 2024, NASA’s PACE satellite detects and measures the distribution of phytoplankton communities in the ocean. It also provides data on the size, amount, and type of tiny particles called aerosols in Earth’s atmosphere, as well as the height, thickness, and opacity of clouds.
“Integrating information across NASA’s Earth System Observatory and its pathfinder missions SWOT and PACE is an exciting new frontier in Earth science,” said Nadya Vinogradova Shiffer, program scientist for SWOT and the Integrated Earth System Observatory at NASA Headquarters.
Where Physics and Biology Meet
The animation above starts by depicting the orbits of SWOT (orange) and PACE (light blue), then zooms into the North Atlantic Ocean. The first data to appear was acquired by PACE on Aug. 8. It reveals concentrations of chlorophyll-a, a vital pigment for photosynthesis in plants and phytoplankton. Light green and yellow indicate higher concentrations of chlorophyll-a, while blue signals lower concentrations.
Next is sea surface height data from SWOT, taken during several passes over the same region between Aug. 7 and 8. Dark blue represents heights that are lower than the mean sea surface height, while dark orange and red represent heights higher than the mean. The contour lines that remain once the color fades from the SWOT data indicate areas of the ocean with the same height, much like the lines on a topographic map indicate areas with the same elevation.
The underlying PACE data then cycles through several groups of phytoplankton, starting with picoeukaryotes. Lighter green indicates greater concentrations of this group. The final two groups are cyanobacteria — some of the smallest and most abundant phytoplankton in the ocean — called Prochlorococcus and Synechococcus. For Prochlorococcus, lighter raspberry colors represent higher concentrations. Lighter teal colors for Synechococcus signal greater amounts of the cyanobacteria.
The animation shows that higher phytoplankton concentrations on Aug. 8 tended to coincide with areas of lower water height. Eddies that spin counterclockwise in the Northern Hemisphere tend to draw water away from their center. This results in relatively lower sea surface heights in the center that draw up cooler, nutrient-rich water from the deep ocean. These nutrients act like fertilizer, which can boost phytoplankton growth in sunlit waters at the surface.
Overlapping SWOT and PACE data enables a better understanding of the connections between ocean dynamics and aquatic ecosystems, which can help improve the management of resources such as fisheries, since phytoplankton form the base of most food chains in the sea. Integrating these kinds of datasets also helps to improve calculations of how much carbon is exchanged between the atmosphere and the ocean. This, in turn, can indicate whether regions of the ocean that absorb excess atmospheric carbon are changing.
More About SWOT
The SWOT satellite was jointly developed by NASA and CNES, with contributions from the Canadian Space Agency (CSA) and the UK Space Agency. NASA’s Jet Propulsion Laboratory, managed for the agency by Caltech in Pasadena, California, leads the U.S. component of the project. For the flight system payload, NASA provided the Ka-band radar interferometer (KaRIn) instrument, a GPS science receiver, a laser retroreflector, a two-beam microwave radiometer, and NASA instrument operations. The Doppler Orbitography and Radioposition Integrated by Satellite system, the dual frequency Poseidon altimeter (developed by Thales Alenia Space), the KaRIn radio-frequency subsystem (together with Thales Alenia Space and with support from the UK Space Agency), the satellite platform, and ground operations were provided by CNES. The KaRIn high-power transmitter assembly was provided by CSA.
To learn more about SWOT, visit:
https://swot.jpl.nasa.gov
More About PACE
The PACE mission is managed by NASA Goddard Space Flight Center, which also built and tested the spacecraft and the Ocean Color Instrument, which collected the data shown in the visualization. The satellite’s Hyper-Angular Rainbow Polarimeter #2 was designed and built by the University of Maryland, Baltimore County, and the Spectro-polarimeter for Planetary Exploration was developed and built by a Dutch consortium led by Netherlands Institute for Space Research, Airbus Defence, and Space Netherlands.
To learn more about PACE, visit:
https://pace.gsfc.nasa.gov
News Media Contacts
Jacob Richmond (for PACE)
NASA’s Goddard Space Flight Center, Greenbelt, Md.
jacob.a.richmond@nasa.gov
Jane J. Lee / Andrew Wang (for SWOT)
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 626-379-6874
jane.j.lee@jpl.nasa.gov / andrew.wang@jpl.nasa.gov
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Last Updated Dec 09, 2024 Related Terms
PACE (Plankton, Aerosol, Cloud, Ocean Ecosystem) Climate Science Oceans SWOT (Surface Water and Ocean Topography) Explore More
7 min read Six Ways Supercomputing Advances Our Understanding of the Universe
Article 3 weeks ago 4 min read NASA Data Helps International Community Prepare for Sea Level Rise
Article 4 weeks ago 6 min read Inia Soto Ramos, From the Mountains of Puerto Rico to Mountains of NASA Earth Data
Dr. Inia Soto Ramos became fascinated by the mysteries of the ocean while growing up…
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By NASA
9 Min Read Towards Autonomous Surface Missions on Ocean Worlds
Artist’s concept image of a spacecraft lander with a robot arm on the surface of Europa. Credits:
NASA/JPL – Caltech Through advanced autonomy testbed programs, NASA is setting the groundwork for one of its top priorities—the search for signs of life and potentially habitable bodies in our solar system and beyond. The prime destinations for such exploration are bodies containing liquid water, such as Jupiter’s moon Europa and Saturn’s moon Enceladus. Initial missions to the surfaces of these “ocean worlds” will be robotic and require a high degree of onboard autonomy due to long Earth-communication lags and blackouts, harsh surface environments, and limited battery life.
Technologies that can enable spacecraft autonomy generally fall under the umbrella of Artificial Intelligence (AI) and have been evolving rapidly in recent years. Many such technologies, including machine learning, causal reasoning, and generative AI, are being advanced at non-NASA institutions.
NASA started a program in 2018 to take advantage of these advancements to enable future icy world missions. It sponsored the development of the physical Ocean Worlds Lander Autonomy Testbed (OWLAT) at NASA’s Jet Propulsion Laboratory in Southern California and the virtual Ocean Worlds Autonomy Testbed for Exploration, Research, and Simulation (OceanWATERS) at NASA’s Ames Research Center in Silicon Valley, California.
NASA solicited applications for its Autonomous Robotics Research for Ocean Worlds (ARROW) program in 2020, and for the Concepts for Ocean worlds Life Detection Technology (COLDTech) program in 2021. Six research teams, based at universities and companies throughout the United States, were chosen to develop and demonstrate autonomy solutions on OWLAT and OceanWATERS. These two- to three-year projects are now complete and have addressed a wide variety of autonomy challenges faced by potential ocean world surface missions.
OWLAT
OWLAT is designed to simulate a spacecraft lander with a robotic arm for science operations on an ocean world body. The overall OWLAT architecture including hardware and software components is shown in Figure 1. Each of the OWLAT components is detailed below.
Figure 1. The software and hardware components of the Ocean Worlds Lander Autonomy Testbed and the relationships between them. NASA/JPL – Caltech The hardware version of OWLAT (shown in Figure 2) is designed to physically simulate motions of a lander as operations are performed in a low-gravity environment using a six degrees-of-freedom (DOF) Stewart platform. A seven DOF robot arm is mounted on the lander to perform sampling and other science operations that interact with the environment. A camera mounted on a pan-and-tilt unit is used for perception. The testbed also has a suite of onboard force/torque sensors to measure motion and reaction forces as the lander interacts with the environment. Control algorithms implemented on the testbed enable it to exhibit dynamics behavior as if it were a lightweight arm on a lander operating in different gravitational environments.
Figure 2. The Ocean Worlds Lander Autonomy Testbed. A scoop is mounted to the end of the testbed robot arm. NASA/JPL – Caltech The team also developed a set of tools and instruments (shown in Figure 3) to enable the performance of science operations using the testbed. These various tools can be mounted to the end of the robot arm via a quick-connect-disconnect mechanism. The testbed workspace where sampling and other science operations are conducted incorporates an environment designed to represent the scene and surface simulant material potentially found on ocean worlds.
Figure 3. Tools and instruments designed to be used with the testbed. NASA/JPL – Caltech The software-only version of OWLAT models, visualizes, and provides telemetry from a high-fidelity dynamics simulator based on the Dynamics And Real-Time Simulation (DARTS) physics engine developed at JPL. It replicates the behavior of the physical testbed in response to commands and provides telemetry to the autonomy software. A visualization from the simulator is shown on Figure 4.
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Figure 7. Screenshot of OceanWATERS lander on a terrain modeled from the Atacama Desert. A scoop operation has just been completed. NASA/JPL – Caltech The autonomy software module shown at the top in Figure 1 interacts with the testbed through a Robot Operating System (ROS)-based interface to issue commands and receive telemetry. This interface is defined to be identical to the OceanWATERS interface. Commands received from the autonomy module are processed through the dispatcher/scheduler/controller module (blue box in Figure 1) and used to command either the physical hardware version of the testbed or the dynamics simulation (software version) of the testbed. Sensor information from the operation of either the software-only or physical testbed is reported back to the autonomy module using a defined telemetry interface. A safety and performance monitoring and evaluation software module (red box in Figure 1) ensures that the testbed is kept within its operating bounds. Any commands causing out of bounds behavior and anomalies are reported as faults to the autonomy software module.
Figure 5. Erica Tevere (at the operator’s station) and Ashish Goel (at the robot arm) setting up the OWLAT testbed for use. NASA/JPL – Caltech OceanWATERS
At the time of the OceanWATERS project’s inception, Jupiter’s moon Europa was planetary science’s first choice in searching for life. Based on ROS, OceanWATERS is a software tool that provides a visual and physical simulation of a robotic lander on the surface of Europa (see Figure 6). OceanWATERS realistically simulates Europa’s celestial sphere and sunlight, both direct and indirect. Because we don’t yet have detailed information about the surface of Europa, users can select from terrain models with a variety of surface and material properties. One of these models is a digital replication of a portion of the Atacama Desert in Chile, an area considered a potential Earth-analog for some extraterrestrial surfaces.
Figure 6. Screenshot of OceanWATERS. NASA/JPL – Caltech JPL’s Europa Lander Study of 2016, a guiding document for the development of OceanWATERS, describes a planetary lander whose purpose is collecting subsurface regolith/ice samples, analyzing them with onboard science instruments, and transmitting results of the analysis to Earth.
The simulated lander in OceanWATERS has an antenna mast that pans and tilts; attached to it are stereo cameras and spotlights. It has a 6 degree-of-freedom arm with two interchangeable end effectors—a grinder designed for digging trenches, and a scoop for collecting ground material. The lander is powered by a simulated non-rechargeable battery pack. Power consumption, the battery’s state, and its remaining life are regularly predicted with the Generic Software Architecture for Prognostics (GSAP) tool. To simulate degraded or broken subsystems, a variety of faults (e.g., a frozen arm joint or overheating battery) can be “injected” into the simulation by the user; some faults can also occur “naturally” as the simulation progresses, e.g., if components become over-stressed. All the operations and telemetry (data measurements) of the lander are accessible via an interface that external autonomy software modules can use to command the lander and understand its state. (OceanWATERS and OWLAT share a unified autonomy interface based on ROS.) The OceanWATERS package includes one basic autonomy module, a facility for executing plans (autonomy specifications) written in the PLan EXecution Interchange Language, or PLEXIL. PLEXIL and GSAP are both open-source software packages developed at Ames and available on GitHub, as is OceanWATERS.
Mission operations that can be simulated by OceanWATERS include visually surveying the landing site, poking at the ground to determine its hardness, digging a trench, and scooping ground material that can be discarded or deposited in a sample collection bin. Communication with Earth, sample analysis, and other operations of a real lander mission, are not presently modeled in OceanWATERS except for their estimated power consumption. Figure 7 is a video of OceanWATERS running a sample mission scenario using the Atacama-based terrain model.
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Figure 7. Screenshot of OceanWATERS lander on a terrain modeled from the Atacama Desert. A scoop operation has just been completed. NASA/JPL – Caltech Because of Earth’s distance from the ocean worlds and the resulting communication lag, a planetary lander should be programmed with at least enough information to begin its mission. But there will be situation-specific challenges that will require onboard intelligence, such as deciding exactly where and how to collect samples, dealing with unexpected issues and hardware faults, and prioritizing operations based on remaining power.
Results
All six of the research teams funded by the ARROW and COLDTech programs used OceanWATERS to develop ocean world lander autonomy technology and three of those teams also used OWLAT. The products of these efforts were published in technical papers, and resulted in development of software that may be used or adapted for actual ocean world lander missions in the future. The following table summarizes the ARROW and COLDTech efforts.
Principal Investigator (PI) PI Institution Project Testbed Used Purpose of Project ARROW Projects Jonathan Bohren Honeybee Robotics Stochastic PLEXIL (SPLEXIL) OceanWATERS Extended PLEXIL with stochastic decision-making capabilities by employing reinforcement learning techniques. Pooyan Jamshidi University of South Carolina Resource Adaptive Software Purpose-Built for Extraordinary Robotic Research Yields (RASPBERRY SI) OceanWATERS & OWLAT Developed software algorithms and tools for fault root cause identification, causal debugging, causal optimization, and causal-induced verification. COLDTech Projects Eric Dixon Lockheed Martin Causal And Reinforcement Learning (CARL) for COLDTech OceanWATERS Integrated a model of JPL’s mission-ready Cold Operable Lunar Deployable Arm (COLDarm) into OceanWATERS and applied image analysis, causal reasoning, and machine learning models to identify and mitigate the root causes of faults, such as ice buildup on the arm’s end effector. Jay McMahon University of Colorado Robust Exploration with Autonomous Science On-board, Ranked Evaluation of Contingent Opportunities for Uninterrupted Remote Science Exploration (REASON-RECOURSE) OceanWATERS Applied automated planning with formal methods to maximize science return of the lander while minimizing communication with ground team on Earth. Melkior Ornik U Illinois, Urbana-Champaign aDaptive, ResIlient Learning-enabLed oceAn World AutonomY (DRILLAWAY) OceanWATERS & OWLAT Developed autonomous adaptation to novel terrains and selecting scooping actions based on the available image data and limited experience by transferring the scooping procedure learned from a low-fidelity testbed to the high-fidelity OWLAT testbed. Joel Burdick Caltech Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO) OceanWATERS & OWLAT Developed autonomous 1) detection and identification of off-nominal conditions and procedures for recovery from those conditions, and 2) sample site selection Acknowledgements: The portion of the research carried out at the Jet Propulsion Laboratory, California Institute of Technology was performed under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The portion of the research carried out by employees of KBR Wyle Services LLC at NASA Ames Research Center was performed under a contract with the National Aeronautics and Space Administration (80ARC020D0010). Both were funded by the Planetary Science Division ARROW and COLDTech programs.
Project Leads: Hari Nayar (NASA Jet Propulsion Laboratory, California Institute of Technology), K. Michael Dalal (KBR, Inc. at NASA Ames Research Center)
Sponsoring Organizations: NASA SMD PESTO
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By NASA
8 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Return to 2024 SARP Closeout Faculty Advisors:
Dr. Tom Bell, Woods Hole Oceanographic Institution
Dr. Kelsey Bisson, NASA Headquarters Science Mission Directorate
Graduate Mentor:
Kelby Kramer, Massachusetts Institute of Technology
Kelby Kramer, Graduate Mentor
Kelby Kramer, graduate mentor for the 2024 SARP Ocean Remote Sensing group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.
Lucas DiSilvestro
Shallow Water Benthic Cover Type Classification using Hyperspectral Imagery in Kaneohe Bay, Oahu, Hawaii
Lucas DiSilvestro
Quantifying the changing structure and extent of benthic coral communities is essential for informing restoration efforts and identifying stressed regions of coral. Accurate classification of shallow-water benthic coral communities requires high spectral and spatial resolution, currently not available on spaceborne sensors, to observe the seafloor through an optically complex seawater column. Here we create a shallow water benthic cover type map of Kaneohe Bay, Oahu, Hawaii using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) without requiring in-situ data as inputs. We first run the AVIRIS data through a semi-analytical inversion model to derive color dissolved organic matter, chlorophyll concentration, bottom albedo, suspended sediment, and depth parameters for each pixel, which are then matched to a Hydrolight simulated water column. Pure reflectance for coral, algae, and sand are then projected through each water column to create spectral endmembers for each pixel. Multiple Endmember Spectral Mixture Analysis (MESMA) provides fractional cover of each benthic class on a per-pixel basis. We demonstrate the efficacy of using simulated water columns to create surface reflectance spectral endmembers as Hydrolight-derived in-situ endmember spectra strongly match AVIRIS surface reflectance for corresponding locations (average R = 0.96). This study highlights the capabilities of using medium-fine resolution hyperspectral imagery to identify fractional cover type of localized coral communities and lays the groundwork for future spaceborne hyperspectral monitoring of global coral communities.
Atticus Cummings
Quantifying Uncertainty In Kelp Canopy Remote Sensing Using the Harmonized Landsat Sentinel-2 Dataset
Atticus Cummings
California’s giant kelp forests serve as a major foundation for the region’s rich marine biodiversity and provide recreational and economic value to the State of California. With the rising frequency of marine heatwaves and extreme weather onset by climate change, it has become increasingly important to study these vital ecosystems. Kelp forests are highly dynamic, changing across several timescales; seasonally due to nutrient concentrations, waves, and predator populations, weekly with typical growth and decay, and hourly with the tides and currents. Previous remote sensing of kelp canopies has relied on Landsat imagery taken with a eight-day interval, limiting the ability to quantify more rapid changes. This project aims to address uncertainty in kelp canopy detection using the Harmonized Landsat and Sentinel-2 (HLS) dataset’s zero to five-day revisit period. A random forest classifier was used to identify pixels that contain kelp, on which Multiple Endmember Spectral Mixture Analysis (MESMA) was then run to quantify intrapixel kelp density. Processed multispectral satellite images taken within 3 days of one another were paired for comparison. The relationship between fluctuations in kelp canopy density with tides and currents was assessed using in situ data from an acoustic doppler current profiler (ADCP) at the Santa Barbara Long Term Ecological Research site (LTER) and a NOAA tidal buoy. Preliminary results show that current and tidal trends cannot be accurately correlated with canopy detection due to other sources of error. We found that under cloud-free conditions, canopy detection between paired images varied on average by 42%. Standardized image processing suggests that this uncertainty is not created within the image processing step, but likely arises due to exterior factors such as sensor signal noise, atmospheric conditions, and sea state. Ultimately, these errors could lead to misinterpretation of remotely sensed kelp ecosystems, highlighting the need for further research to identify and account for uncertainties in remote sensing of kelp canopies.
Jasmine Sirvent
Kelp Us!: A Methods Analysis for Predicting Kelp Pigment Concentrations from Hyperspectral Reflectance
Jasmine Sirvent
Ocean color remote sensing enables researchers to assess the quantity and physiology of life in the ocean, which is imperative to understanding ecosystem health and formulating accurate predictions. However, without proper methods to analyze hyperspectral data, correlations between spectral reflectance and physiological traits cannot be accurately derived. In this study, I explored different methods—single variable regression, partial least squares regressions (PLSR), and derivatives—in analyzing in situ Macrocystis pyrifera (giant kelp) off the coast of Santa Barbara, California in order to predict pigment concentrations from AVIRIS hyperspectral reflectance. With derivatives as a spectral diagnostic tool, there is evidence suggesting high versus low pigment concentrations could be diagnosed; however, the fluctuations were within 10 nm of resolution, thus AVIRIS would be unable to reliably detect them. Exploring a different method, I plotted in situ pigment measurements — chlorophyll a, fucoxanthin, and the ratio of fucoxanthin to chlorophyll a—against hyperspectral reflectance that was resampled to AVIRIS bands. PLSR proved to be a more successful model because of its hyperdimensional analysis capabilities in accounting for multiple wavelength bands, reaching R2 values of 0.67. Using this information, I constructed a model that predicts kelp pigments from simulated AVIRIS reflectance using a spatial time series of laboratory spectral measurements and photosynthetic pigment concentrations. These results have implications, not only for kelp, but many other photosynthetic organisms detectable by hyperspectral airborne or satellite sensors. With these findings, airborne optical data could possibly predict a plethora of other biogeochemical traits. Potentially, this research would permit scientists to acquire data analogous to in situ measurements about floating matters that cannot financially and pragmatically be accessed by anything other than a remote sensor.
Isabelle Cobb
Correlations Between SSHa and Chl-a Concentrations in the Northern South China Sea
Isabelle Cobb
Sea surface height anomalies (SSHa)–variations in sea surface height from climatological averages–occur on seasonal timescales due to coastal upwelling and El Niño-Southern Oscillation (ENSO) cycles. These anomalies are heightened when upwelling plumes bring cold, nutrient-rich water to the surface, and are particularly strong along continental shelves in the Northern South China Sea (NSCS). This linkage between SSHa and nutrient availability has interesting implications for changing chlorophyll-a (chl-a) concentrations, a prominent indicator of phytoplankton biomass that is essential to the health of marine ecosystems. Here, we evaluate the long-term (15 years) relationship between SSHa and chl-a, in both satellite remote sensing data and in situ measurements. Level 3 SSHa data from Jason 1/2/3 satellites and chl-a data from MODIS Aqua were acquired and binned to monthly resolution. We found a significant inverse correlation between SSHa and chl-a during upwelling months in both the remote sensing (Spearman’s R=-0.57) and in situ data, with higher resolution in situ data from ORAS4 (an assimilation of buoy observations from 2003-2017) showing stronger correlations (Spearman’s R=-0.75). In addition, the data reveal that the magnitude of SSH increases with time during instances of high correlation, possibly indicating a trend of increased SSH associated with reduced seasonal chl-a concentrations. Thus, this relationship may inform future work predicting nutrient availability and threats to marine ecosystems as climate change continues to affect coastal sea surface heights.
Alyssa Tou
Exploring Coastal Sea Surface Temperature Anomalies and their effect on Coastal Fog through analyzing Plant Phenology
Alyssa Tou
Marine heat waves (MHW) have been increasing in frequency, duration and intensity, giving them substantial potential to influence ecosystems. Do these MHWs sufficiently enhance coastal precipitation such that plant growth is impacted? Recently, the Northeast Pacific experienced a long, intense MHW in 2014/2015, and another short, less intense MHW in 2019/2020. Here we investigate how the intensity and duration of MHWs influence the intensity and seasonal cycle of three different land cover types (‘grass’, ‘trees’, and a combination of both ‘combined’’) to analyze plant phenology trends in Big Sur, California. We hypothesize that longer intense MHWs decrease the ocean’s evaporative capacity, decreasing fog, thus lowering plant productivity, as measured by Normalized Difference Vegetation Index (NDVI). Sea surface temperature (SST) and NDVI data were collected from the NOAA Coral Reef Watch, and NASA MODIS/Terra Vegetation Indices 16-Day L3 Global 250m products respectively. Preliminary results show no correlation (R2=0.02) between SSTa and combined NDVI values and no correlation (R2=0.01) between SST and NDVI. This suggests that years with anomalously high SST do not significantly impact plant phenology. During the intense and long 2014/2015 MHW, peak NDVI values for ‘grass’ and ‘combined’ pixels were 2.0 and 1.7 standard deviations above the climatological average, while the shorter 2019/2020 MHW saw higher peaks of 3.2 and 2.4 standard deviations. However, the ‘grass’, ‘tree’ and ‘combined’ NDVI anomalies were statistically insignificant during both MHWs, showing that although NDVI appeared to increase during the shorter and less intense MHW, these values may be attributed to other factors. The data qualitatively suggest that MHW’s don’t impact the peak NDVI date, but more data at higher temporal resolution are necessary. Further research will involve analyzing fog indices and exploring confounding variables impacting NDVI, such as plant physiology, anthropogenic disturbance, and wildfires. In addition, it’s important to understand to what extent changes in NDVI are attributed to the driving factors of MHWs or the MHWs themselves. Ultimately, mechanistically understanding the impacts MHW intensity and duration have on terrestrial ecosystems will better inform coastal community resilience.
Return to 2024 SARP Closeout Share
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Last Updated Nov 22, 2024 Related Terms
General Explore More
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