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NASA NTRS2019

Ocean Color Instrument Integration and Testing

This paper describes the plans, flows, key facilities, components and equipment necessary to fully integrate, functionally test, qualify and calibrate the Ocean Color Instrument (OCI) on the Plankton, Aerosols, Clouds, and oceans Ecosystem (PACE) observatory. PACE is currently in the design phase of mission development. It is scheduled to launch in 2022, extending and improving NASA's twenty-year record of satellite observations of global ocean biology, aerosols and clouds. PACE will advance the assessment of ocean health by measuring the distribution of phytoplankton, which are small plants and algae that sustain the marine food web. It will also continue systematic records of key atmospheric variables associated with air quality and the Earth's climate. PACE's primary sensor, the OCI, is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies. The color of the ocean is determined by the interaction of sunlight with substances or particles present in seawater such as chlorophyll. By monitoring global phytoplankton distribution and abundance with unprecedented detail, the OCI will contribute to a better understanding of the complex systems that drive ocean ecology and it's impacts on global fisheries. This paper will focus on the Integration and Test (I&T) activities for OCI while it is at the NASA Goddard Space Flight Center. The OCI integration consists of assembly and alignment of the rotating telescope, electronics box integration, fixed deck assembly integration, thermal systems integration and the final assembly and testing. This I&T phase will be followed by the OCI calibration and characterization, environmental tests which include electromagnetic interference (EMI)/electromagnetic compatibility (EMC), vibration with sine sweep, acoustics, shock, thermal balance, thermal vacuum, mass properties and center of gravity. This paper will briefly discuss OCI shipment and delivery to the spacecraft vendor for observatory level I&T as well as some launch preparation activities.

Petro, Susanna↗

NASA NTRS2025

PACE Water Resources: Demonstrating the Use of NASA's PACE Hyperspectral Ocean Color Instrument Data for Enhanced Coastal Management

This project developed tools to support the future use of Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) hyperspectral imagery in water resource monitoring and research by NASA DEVELOP teams and members of the PACE applications community. We sought to address a need for support in processing and visualizing hyperspectral PACE Ocean Color Instrument (OCI) data among researchers and decision-makers working in coastal water quality management and harmful algal bloom (HAB) monitoring. To supplement the day of simulated PACE imagery available, we used Aqua MODIS earth observations with Level 3 processing from March 2022 to build a Python graphical user interface (GUI) for visualizing ocean biogeochemical parameters relevant to the early detection and monitoring of HABs. We used simulated PACE OCI Level 2 data derived from the Python Top of Atmosphere Simulation Tool (PyTOAST) to build Jupyter Notebooks for band subset and selection. The Level 3 PACE Viewer components support users with quick visualizations as well as the creation of geoTIFFs and time-series. The Level 2 Jupyter Notebooks address users’ concerns over the volume and complexity of hyperspectral imagery. The PACE Viewer is useful for visual inspection and netCDF data processing but should not be used for geospatial analysis. Once PACE launches, this tool will alleviate the technical burdens of working with hyperspectral data and support the early detection and monitoring of HABs using PACE satellite imagery.

Python Top of Atmosphere Simulation Tool↗

NASA NTRSDate not supplied

On-Orbit OCI Characterization Measurements from the first 6 Months of the PACE Mission

The Ocean Color Instrument (OCI) on NASA’s Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission has been providing data to the science community since April 2024. OCI is a hyperspectral imager, providing almost daily global coverage, at a spatial resolution of 1.2km. Its design specifications were optimized for ocean color and atmospheric applications, but terrestrial studies could benefit from its hyperspectral coverage as well. The ocean color requirements called for very high radiometric accuracy, which could benefit a wide variety of applications. This paper presents results from the first 6 months of on-orbit calibration and characterization measurements, including absolute calibration, spectral registration, temporal trending of radiometric sensitivity, signal to noise ratio, and linearity, with a focus on the commissioning results obtained in the first 2 months after launch.

calibration↗

NASA NTRSDate not supplied

On-Orbit OCI Characterization Measurements from the first 6 Months of the PACE Mission

calibration↗

NASA NTRSDate not supplied

Optical and Detector Design of the Ocean Color Instrument for the NASA Pace Mission

The Ocean Color Instrument (OCI) on NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem mission is a hyperspectral imager with high SNR, precision and dynamic range, and with a very low striping artifact level in the 342-887 nm wavelength range with a spectral resolution of 5 nm in 2.5 nm steps, providing a significant technological advancement over previous ocean imagers. To achieve this, OCI is designed with specialized optical imaging and opto-electronic detection systems that push the boundaries of several state-of-the-art technologies. This paper provides an overview of these systems together with their achieved performances and discussions of their key design challenges.

Remote sensing↗

NASA NTRSDate not supplied

Optical and Detector Design of the Ocean Color Instrument for the NASA Pace Mission

Remote sensing↗

NASA NTRSDate not supplied

Simultaneous Aerosol and Ocean Polarimeter Products Using Coupled Atmosphere-Ocean Vector Radiative Transfer and Neural Networks: The PACE-MAPP Algorithm

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multi-channel polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. A key feature of the MAPP family of algorithms is the use of a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, conservation of energy ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of aerosol and ocean constituents, find the optimal solution, and reliably account for the uncertainties of each parameter. This coupled approach, together with multiangle, multi-channel polarimeter measurements, will enable retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations. We provide an overview of the PACE-MAPP framework and also describe its modular components including its aerosol and hydrosol models, ocean bio-optical models, and thin cirrus model.

Snorre Stamnes↗

NASA NTRSDate not supplied

PACE OCI Polarization Sensitivity Based on Pre-launch Testing

The Ocean Color Instrument (OCI) is a sensor on the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in early 2024. OCI is a grating spectrometer with hyperspectral coverage from the ultraviolet (about 310 nm) to near-infrared (about 900 nm), with additional filtered channels in the short-wave infrared (940 nm – 2260 nm). This instrument will provide ocean color science data to continue the data sets collected by heritage sensors MODIS, SeaWiFs, and VIIRS, but with increased spectral coverage and improved accuracy. In order to achieve the high levels of accuracy demanded by the science community, a rigorous ground test program was conducted to calibrate the instrument and ensure that the calibration can be transferred to on-orbit operations. Some calibration parameters can only be measured during pre-launch testing; one such parameter is the polarization sensitivity. Polarization testing measured the Mueller matrix components needed to determine the polarization sensitivity for all spectral bands for a series of telescope scan angles covering the expected on-orbit scan range. Results indicate that the sensitivity is below 0.6 % except at the shortest wavelengths (less than 340 nm) and was characterized to better than 0.1 % above 340 nm. This indicates that any polarized scenes measured on orbit can be corrected for with a high degree of confidence.

PACE↗

NASA NTRSDate not supplied

PACE OCI Polarization Sensitivity Based on Pre-launch Testing

PACE↗

NASA NTRS2022

Effective Uncertainty Quantification for Multi-Angle Polarimetric Aerosol Remote Sensing Over Ocean

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

PACE↗

NASA NTRSDate not supplied

Mission Overview and Status IGARSS Conference

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). The PACE project office at NASA’s GSFC is responsible for the satellite development, launch and operations. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which will include field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The mission is planned for launch into a Sun synchronous polar orbit at 676.5 km with an inclination of 98 degrees and a 1 pm local ascending node crossing time. The PACE observatory is comprised of three instruments, an Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone). The OCI is the primary instrument on the observatory and is being developed at Goddard Space Flight Center (GSFC). The OCI is a hyperspectral scanning radiometer designed to measure spectral radiances from the ultraviolet to shortwave infrared (SWIR) to enable advanced ocean color and heritage cloud and atmospheric aerosol science [2]. The HARP2 and SPEXone are complimentary instruments on the PACE observatory, acquired outside of GSFC. The HARP2 is multi-spectral, wide swath (supporting atmospheric correction of OCI), and hyper-angular, with capabilities for cloud science that exceed what is capable from OCI alone. The SPEXone is narrow swath, multi-angular, and hyperspectral, with capabilities for atmospheric aerosol science that exceed what can be accomplished with OCI. NASA Headquarters directed the mission development to be guided by a Design-to-Cost (DTC) process. All elements of the mission, other than the cost, are in the DTC trade space. At the heart of the DTC process are the mission studies, performed across all the mission elements. The mission studies were used to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. Baseline mission requirements are a product of the mission studies and are defined by the project office as part of the DTC process. At the time of this writing, OCI, HARP2 and SPEXone are integrated onto the PACE spacecraft, creating the PACE observatory. The instruments and spacecraft subsystems are undergoing combined functional performance testing in preparation for observatory-level environmental testing prior to shipment to the launch site. Here, we will provide an update on science capabilities and observatory readiness for its early 2024 launch.

remote sensing↗

NASA NTRS2024

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (R rs ) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and R rs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

signal-to-noise ratio↗

NASA NTRSDate not supplied

Pace OCI Crosstalk Characterization Based on Pre-Launch Testing

Scheduled to launch in 2024, the Ocean Color Instrument (OCI) onboard the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will collect hyperspectral data from 315 nm – 895 nm via two grating spectrometers (in both the blue and red spectral regions) and 9 multi-spectral bands in the short-wave infrared (940 nm – 2260 nm). The increased spectral resolution and radiometric accuracy is expected to improve upon data collected by heritage sensors such as SeaWiFs, MODIS, and VIIRS, allowing new applications in ocean color, aerosol, and cloud science. During ground testing, higher than expected spatial-spectral crosstalk was measured for the hyperspectral bands in the blue spectrograph. Using a monochromatic-collimated light source, light from a single science pixel (1km x 1km) was found to produce crosstalk signals over 31 pixels in the cross-track direction. This spatial augmentation is caused by the spectral crosstalk’s asynchronous spatial movement during Time Delay Integration (TDI). To fully characterized the magnitude and spectral dependency from this, a crosstalk model was developed by synthesizing data collected from monochromatic-collimated light and monochromatic light that filled the OCI optical aperture. The model was validated by showing good agreement between predicted values and other relevant test data collected using both monochromatic and white light sources.

PACE↗

NASA NTRS2019

Multi-Band Atmospheric Correction Algorithm for Ocean Color Retrievals

NASA's current Atmospheric Correction (AC) algorithm for ocean color utilizes two bands and their ratio in the Near Infrared (NIR) to estimate aerosol reflectance and aerosol type. The algorithm then extrapolates the spectral dependence of aerosol reflectance to the visible wavelengths based on modeled spectral dependence of the identified aerosol type. Future advanced ocean color sensors, such as the Ocean Color Instrument (OCI) that will be carried on the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite, will be capable of measuring the hyperspectral radiance from 340 to 890 nm at 5-nm spectral resolution and at 7 discrete Short-wave Infrared (SWIR) channels: 940, 1038,1250, 1378, 1615, 2130, and 2260 nm. To optimally employ this unprecedented instrument capability, we propose an improved AC algorithm that utilizes all atmospheric-window channels in the NIR to SWIR spectral range to reduce the uncertainty in the AC process. A theoretical uncertainty analysis of this, namely Multi-Band AC (MBAC), indicates that the algorithm can reduce the uncertainty in remote sensing reflectance (Rrs) retrievals of the ocean caused by sensor random noise. Furthermore, in optically complex waters, where the NIR signal is affected by contributions from highly-reflective turbid waters, the MBAC algorithm can be adaptively weighted to the strongly-absorbing SWIR channels to enable improved ocean color retrievals in coastal waters. We provide here a description of the algorithm and demonstrate the improved performance in ocean color retrievals, relative to the current NASA standard AC algorithm, through comparison with field measurements and assessment of propagated uncertainties in applying the MBAC algorithm to MODIS and simulated PACE OCI data.

PACE↗

NASA NTRS2007

Remote Sensing of the Radiative and Microphysical Properties of Clouds during TC4: Results from MAS, MASTER, MODIS, and MISR

The MODIS Airborne Simulator (MAS) and MODIS/ASTER Airborne Simulator (MASTER) were used to obtain measurements of the bidirectional reflectance and brightness temperature of clouds at 50 discrete wavelengths between 0.47 and 14.3 (12.9 m for MASTER). These observations were obtained from the NASA ER-2 aircraft as part of the Tropical Composition, Clouds and Climate Coupling Experiment (TC4) conducted over Central America and surrounding Pacific and Atlantic Oceans between July 17 and August 8, 2007. Multispectral images in eight distinct bands were used to derive a confidence in clear sky (or alternatively the probability of cloud) over land and ocean ecosystems. Based on the results of individual tests run as part of this cloud mask, an algorithm was developed to estimate the phase of the clouds (liquid water, ice, or undetermined phase). Finally, the cloud optical thickness and effective radius were derived for both liquid water and ice clouds that were detected during each flight, using a nearly identical algorithm as that implemented operationally to process MODIS cloud data from the Aqua and Terra satellites (Collection 5). This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS and MASTER date in TC4, is quite capable of distinguishing both liquid water and ice clouds during daytime conditions over both land and ocean. The cloud optical thickness and effective radius retrievals used three distinct bands of the MAS (or MASTER), and these results were compared with nearly simultaneous retrievals of MODIS on the Terra spacecraft. Finally, this MODIS-based algorithm was adapted to MISR data to infer the cloud optical thickness of liquid water clouds from MISR. Results of this analysis will be presented and discussed.

King, Michael D.↗

NASA NTRS2010

Remote Sensing of Radiative and Microphysical Properties of Clouds During TC (sup 4): Results from MAS, MASTER, MODIS, and MISR

The Moderate Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator (MAS) and MODIS/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Airborne Simulator (MASTER) were used to obtain measurements of the bidirectional reflectance and brightness temperature of clouds at 50 discrete wavelengths between 0.47 and 14.2 microns (12.9 microns for MASTER). These observations were obtained from the NASA ER-2 aircraft as part of the Tropical Composition, Cloud and Climate Coupling (TC4) experiment conducted over Central America and surrounding Pacific and Atlantic Oceans between 17 July and 8 August 2007. Multispectral images in eleven distinct bands were used to derive a confidence in clear sky (or alternatively the probability Of cloud) over land and ocean ecosystems. Based on the results of individual tests run as part of the cloud mask, an algorithm was developed to estimate the phase of the clouds (liquid water, ice, or undetermined phase). The cloud optical thickness and effective radius were derived for both liquid water and ice clouds that were detected during each flight, using a nearly identical algorithm to that implemented operationally to process MODIS Cloud data from the Aqua and Terra satellites (Collection 5). This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS and MASTER data in TC(sup 4), is quite capable of distinguishing both liquid water and ice clouds during daytime conditions over both land and ocean. The cloud optical thickness and effective radius retrievals use five distinct bands of the MAS (or MASTER), and these results were compared with nearly simultaneous retrievals of marine liquid water clouds from MODIS on the Terra spacecraft. Finally, this MODIS-based algorithm was adapted to Multiangle Imaging SpectroRadiometer (MISR) data to infer the cloud optical thickness Of liquid water clouds from MISR. Results of this analysis are compared and contrasted.

King, Michael D.↗

NASA NTRS2023

Use of Machine Learning and Principal Component Analysis to Retrieve Nitrogen Dioxide (NO 2 ) With Hyperspectral Imagers and Reduce Noise in Spectral Fitting

Nitrogen dioxide (NO 2 ) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO 2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO 2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO 2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO 2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO 2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO 2 retrievals. Nevertheless, the NO 2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO 2 retrievals. Once trained, NNs can also substantially speed up NO 2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

NO2↗

NASA NTRS2022

The Future: Nasa’s Surface Biology and Geology Mission and the Dark (Aquatic) Side

With the release of NASA’s Earth System Observatory (ESO) measurement targets that includes studying the Earth’s Surface Biology and Geology (SBG), a global visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager have been planned to launch in the late 2020s. This mission will enable unprecedented interdisciplinary science and applications relevant to studying the biology and geology of the Earth's surface. Measurement targets are directly aligned for studying coastal and inland waters ecosystems and water quality, including coralreef ecosystems, snow and ice, mineralogy, volcanology, biology, ecology, biodiversity, and components of radiative forcing from the surface such as greenhouse gas emissions. The observations not only have scientific value in studying feedbacks and interactions of surface processes (e.g., harmful algal blooms, domoic acid, oil spills, and other hazardous events), but also societal benefit – with the capacity to support real-world decision-making such as response to theseevents and inland water conservation, protection of drinking water quality for public and environmental health, and habitat conservation. The work presented here outlines the conducted mission and architecture study and the initiation of science performance trades (signal sensitivity; spectral, spatial, and temporal resolution; and atmospheric effects) informing the design of the SBG segment of the ESO providing the most value to myriad science and applications as possible. Science and applications synergies and algorithms with the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) and the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) is opportunistic for advancing aquatics research and applications.

THE FUTURE↗