Nima Pahlevan

Impact of multiple satellite ocean color samplings in a day on assessing phytoplankton dynamics

Ocean-color imagers on conventional polar-orbiting satellites have a revisit time of ∼2 days for most regions, which is further reduced if the area is frequently cloudy. The Geostationary Ocean Color Imager (GOCI), the first ocean-color imager on a geostationary satellite, provides measurements 8 times a day, thus significantly improving the frequency of measurements for studies of ocean environments. Here, we use results derived from GOCI measurements over Taihu Lake to demonstrate that the extra sampling can be used to improve the accuracy of statistically averaged longer-term (daily) measurements. Additionally, using numerical simulations, we demonstrate that the coupling of diurnal variations of both biomass and photosynthetic available radiation can improve the accuracy of daily primary production estimates. These results echo that higher sampling frequency can improve our estimates of longer-term dynamics of biogeochemical processes and highlights the value of ocean color measurements from geostationary satellites.

Radiance transmittance measured at the ocean surface

The radiance transmittance (Tr) is the ratio of the water-leaving radiance (Lw(0+)) to the sub-surface upwelling radiance (Lu(0-)), which is an important optical parameter for ocean optics and ocean color remote sensing. Historically, a constant value (~0.54) based on theoretical presumptions has been adopted for Tr and is widely used. This optical parameter, however, has never been measured in the aquatic environments. With a robust setup to measure both Lu(0-) and Lw(0+) simultaneously in the field, this study presents Tr in the zenith direction between 350 and 700 nm measured in a wide range of oceanic waters. It is found that the measured Tr values are generally consistent with the long-standing theoretical value of 0.54, with mean relative difference less than 10%. In particular, the agreement within the spectral domain of 400-600 nm is found to be the best (with the averaged difference less than 5%). The largest difference is observed for wavelengths longer than 600 nm with the average difference less than 15%, which is related to the generally very small values in both Lu(0-) and Lw(0+) and rough environmental conditions. These results provide a validation of the setup for simultaneous measurements of upwelling radiance and water-leaving radiance and confidence in the theoretical Tr value used in ocean optics studies at least for oceanic waters.

Atmospheric Correction Inter-Comparison Exercise

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of an AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate this challenge, the inter-comparison protocol and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be investigated in future ACIX experiments.

Revisiting Short-Wave-Infrared (SWIR) Bands for Atmospheric Correction in Coastal Waters

The shortwave infrared (SWIR) bands on the existing Earth Observing missions like MODIS have been designed to meet land and atmospheric science requirements. The future geostationary and polar-orbiting ocean color missions, however, require highly sensitive SWIR bands (> 1550nm) to allow for a precise removal of aerosol contributions. This will allow for reasonable retrievals of the remote sensing reflectance (Rrs) using standard NASA atmospheric corrections over turbid coastal waters. Design, fabrication, and maintaining high-performance SWIR bands at very low signal levels bear significant costs on dedicated ocean color missions. This study aims at providing a full analysis of the utility of alternative SWIR bands within the 1600nm atmospheric window if the bands within the 2200nm window were to be excluded due to engineering/cost constraints. Following a series of sensitivity analyses for various spectral band configurations as a function of water vapor amount, we chose spectral bands centered at 1565 and 1675nm as suitable alternative bands within the 1600nm window for a future geostationary imager. The sensitivity of this band combination to different aerosol conditions, calibration uncertainties, and extreme water turbidity were studied and compared with that of all band combinations available on existing polar-orbiting missions. The combination of the alternative channels was shown to be as sensitive to test aerosol models as existing near-infrared (NIR) band combinations (e.g., 748 and 869nm) over clear open ocean waters. It was further demonstrated that while in extremely turbid waters the 1565/1675 band pair yields Rrs retrievals as good as those derived from all other existing SWIR band pairs (> 1550nm), their total calibration uncertainties must be < 1% to meet current science requirements for ocean color retrievals (i.e., Δ Rrs (443) < 5%). We further show that the aerosol removal using the NIR and SWIR bands (available on the existing polar-orbiting missions) can be very sensitive to calibration uncertainties. This requires the need for monitoring the calibration of these bands to ensure consistent multi-mission ocean color products in coastal/inland waters.

INTEGRATING LANDSAT-7 IMAGERY WITH PHYSICS-BASED MODELS FOR QUANTITATIVE MAPPING OF COASTAL WATERS NEAR RIVER DISCHARGES

Remote sensing has traditionally been used to retrieve water constituents by establishing a relationship between in- situ measured quantities and image-derived products. Motivated by the dramatically improved potential of the Landsat Data Continuity Mission (LDCM), this paper describes a different approach for water constituent retrieval where both thermal and visible spectral bands of the Enhanced Thematic Mapper Plus (ETM+) instrument on board Landsat-7 are utilized. In this effort, Landsat data is integrated with a 3D hydrodynamic model to obtain profiles of particles and dissolved matter in the near shore zone in the vicinity of two river discharges. The procedure is based upon performing many hydrodynamic simulations by adjusting input environmental/physical variables and generating Look-Up-Tables (LUTs). This is conducted in two phases, namely the model calibration and the constituent retrieval. In the calibration phase, the best model output is determined by searching the LUT for the optimal surface temperature map compared to the Landsat-derived surface temperature map. The profiles of particles and dissolved matter are retrieved in the second step by comparing several modeled surface reflectance maps with atmospherically compensated Landsat-7 imagery. Various case scenarios of simulated water constituent profiles drive an in-water radiative transfer code, i.e. Hydrolight, which simulates water-leaving reflectance ( d r ). The best match, obtained via optimization, demonstrated an average root-mean-squared-error (RMSE) of 0.68%, i.e., 0.0068 reflectance units, calculated over the two river plumes. It is concluded that calibrating a physics-based model using the Landsat-7 imagery can provide a more lucid insight into the dynamics of spatially non-uniform waters. Ongoing efforts show that, due to its enhanced radiometric fidelity, the LDCM should significantly improve our proposed approach for the retrieval of water constituents.

Diurnal remote sensing of coastal/oceanic waters: a radiometric analysis for Geostationary Coastal and Air Pollution Events

Optical remote sensing systems aboard geostationary platforms can provide high-frequency observations of bio-optical properties in dynamical coastal/oceanic waters. From the end-user standpoint, it is recognized that the fidelity of daily science products relies heavily on the radiometric sensitivity/performance of the imaging system. This study aims to determine the theoretical detection limits for bio-optical properties observed diurnally from a geostationary orbit. The analysis is based upon coupled radiative transfer simulations and the minimum radiometric requirements defined for the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) mission. The diurnal detection limits are found for the optically active constituents of water, including near-surface concentrations of chlorophyll-a (CHL) and total suspended solids (TSS), and the absorption of colored dissolved organic matter (aCDOM). The diurnal top-of-atmosphere radiance (Lt) is modeled for several locations across the field of regard (FOR) to investigate the radiometric sensitivity at different imaging geometries. It is found that, in oceanic waters (CHL=0.07  mg/m3), detecting changes smaller than 0.01  mg/m3 in CHL is feasible for all locations and hours except for late afternoon observations on the edge of the FOR. For more trophic/turbid waters (0.6<CHL<4.5), the proposed system is found sensitive to changes (in CHL) smaller than 0.1  mg/m3 when the air mass fraction (AMF) is less than 5. For aCDOM(440), detecting the changes larger than 0.02  m(-1) (0.08<aCDOM(440)<0.36) is found feasible for most of the imaging geometries. This is equivalent to AMF<5. For TSS, changes on the order of ΔTSS=0.1  g/m3 (0.5<TSS<4.5) are detectable from early morning to late afternoon across the entire FOR. This study gives insights into the radiometric sensitivity of the GEO-CAPE mission in identifying the changes in bio-optical properties at top-of-atmosphere (TOA), which aids in a more lucid understanding of the uncertainties associated with the surface products.

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Abstract The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

Spectral band adjustments for remote sensing reflectance spectra in coastal/inland waters

Matchup analyses of satellite-derived multispectral remote sensing reflectance (Rrs) products using the ocean color component of the aerosol robotic network (AERONET-OC) is now common practice. Robust matchup analyses are crucial in consistent monitoring of ocean and coastal/inland waters. Differences in the spectral bands of various multispectral satellite sensors and in situ radiometers are one of the sources of uncertainties in matchup analyses. These uncertainties are also present in direct sensor-to-sensor comparisons of Rrs products. To account for the differences in the spectral bands, this manuscript evaluates the utility of deep neural networks (DNN) and compares its performance against other existing methods. A large database of simulated Rrs spectra and a fairly comprehensive hyperspectral in situRrs data set were utilized for training and testing. It was found that the DNN outperforms other existing methods leading to band-average root-mean squared errors of < 4e−4 and < 2.5e−4 1/sr for matchup analyses and sensor-to-sensor Rrs intercomparisons, respectively. These uncertainties are at least ~2X and 3X better than the other methods. The largest uncertainties (i.e., differences in 1/sr) were found when dealing with the green bands over highly turbid and eutrophic waters. The analyses of actual AERONET-OC matchups indicated the need for spectral band adjustments, in particular, in the red channel. It was further revealed that the DNN performance is particularly superior in highly eutrophic and turbid waters. Further research may include exploring other machine-learning techniques or improving the architecture of the neural networks.

Analyzing radiometric requirements for diurnal observations of coastal/oceanic waters from geostationary orbits

Over the decades, ocean color imaging sensors placed in Low Earth Orbits (LEO) have enabled nearly daily measurements of ocean water properties. Such observations, however, are restricted by cloud/atmospheric conditions. More importantly, such systems could not provide sufficient number of measurements to study the diurnal dynamics of coastal/oceanic ecosystems. One way to surmount such limitations is to leverage geo-stationary orbits to significantly improve temporal observations over such dynamical coastal/oceanic environments. In this study, it is desired to examine whether 50% changes in chlorophyll-a concentration (< 1.5 ug⁄l) on a semi-diurnal basis are above the noise level. To do so, the top-of-atmosphere radiance (Lt) is modeled for the planned GEO-CAPE mission intended for monitoring coastal ecosystem and river plumes. The input to the simulations includes diurnal remote sensing reflectances (Rrs), which are propagated through a moderately clear atmospheric conditions using a radiative transfer code. The simulations are carried out for two footprints to investigate two extremely different sun-sensor geometries. From these simulations, the temporal change in spectral reflectances between the hours relative to an average noise is examined. Based on the preliminary results, it was found that while the signal change is, on average, 13x the average noise for near-nadir footprints, the change in signal, on average, is only 1.5x the average noise level for near-edge footprints at top of the atmosphere. Such a contrast suggests difficulties in retrieving diurnal variability for locations near the edge of the field of regard (FOR).

Evaluating radiometric sensitivity of Landsat 8 over coastal/inland waters

The operational Land Imager (OLI) aboard Landsat 8 was launched in February 2013 to continue the Landsats mission of monitoring earth resources at relatively high spatial resolution. Compared to Landsat heritage sensors, OLI has an additional 443-nm band (termed coastal/aerosol (CA) band), which extends Landsats potential for mapping/monitoring water quality in coastal/inland waters. In addition, OLIs pushbroom design allows for longer integration time and, as a result, higher signal-to-noise ratio (SNR). Using a series of radiative transfer simulations, we provide insights into the radiometric sensitivity of OLI when studying coastal/inland waters. This will address how the changes in water constituents manifest at the sensor level and whether the changes are resolvable (focal plane) relative to OLIs overall noise1.