Downwelling Irradiance Research Articles

RSUIGM: Realistic Synthetic Underwater Image Generation with Image Formation Model

In this paper, we propose to synthesize realistic underwater images with a novel image formation model, considering both downwelling depth and line of sight (LOS) distance as cue and call it as Realistic Synthetic Underwater Image Generation Model, RSUIGM. The light interaction in the ocean is a complex process and demands specific modeling of direct and backscattering phenomenon to capture the degradations. Most of the image formation models rely on complex radiative transfer models and in-situ measurements for synthesizing and restoration of underwater images. Typical image formation models consider only line of sight distance z and ignore downwelling depth d in the estimation of effect of direct light scattering. We derive the dependencies of downwelling irradiance in direct light estimation for generation of synthetic underwater images unlike state-of-the-art image formation models. We propose to incorporate the derived downwelling irradiance in estimation of direct light scattering for modeling the image formation process and generate realistic synthetic underwater images with the proposed RSUIGM, and name it as RSUIGM dataset . We demonstrate the effectiveness of the proposed RSUIGM by using RSUIGM dataset in training deep learning based restoration methods. We compare the quality of restored images with state-of-the-art methods using benchmark real underwater image datasets and achieve improved results. In addition, we validate the distribution of realistic synthetic underwater images versus real underwater images both qualitatively and quantitatively. The proposed RSUIGM dataset is available here.

Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements

Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.

Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Spectral remote sensing reflectance, Rrs(λ) (sr-1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rrs. Furthermore, since the associated algorithms require Rrs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in Rrs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into Rrs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in Rrs. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in Rrs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chla, mg/m3) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd(490), m-1). Accounting for the error covariance in Rrs generally reduces the estimated relative uncertainty in chla by ∼1-2% (absolute value) in waters with chla < 0.25 mg/m3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chla > 0.35 mg/m3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For Kd(490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in Rrs is further verified through forward-calculating chla from MODIS-retrieved and in situ Rrs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chla can be achieved over deep ocean waters (chla ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in Rrs, some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Vertically Resolved Global Ocean Light Models Using Machine Learning

The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., Rrs, PAR, and kd(490)) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m−2 s−1 for PAR and 0.04, 0.08, and 0.09 W m−2 nm−1 for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications.

A significant change in sea ice diffuse attenuation coefficient with temperature and its implications for the Arctic Ocean

AbstractSea ice transparency is essential to the ecology of the ice‐covered sea. Research suggests that the diffuse attenuation coefficient of downwelling irradiance () varies with ice temperature (); however, the characteristics of its response are not well understood yet, particularly from a quantitative perspective. In an attempt to fill this gap, three independent laboratory experiments were performed between 2016 and 2022 to investigate the variations of with changing . Validation experiments were further performed in Liaodong Bay in 2018 and 2022. To explore the dominant factors controlling this phenomenon, corresponding changes in scattering properties were estimated through a two‐stream radiative transfer model. To examine its implications for local ice algal primary production and radiation transport, of Arctic sea ice was parameterized as a function of . Our results from the laboratory and field experiments showed that decreases (increases) with ice warming (cooling), and a 1°C change in results in a 0.29 m−1 average change in . The response of to between −9°C and −2°C is more notable than that observed between −24°C and −9°C. This is associated with the different variations in the scattering properties of sea ice. It reveals a significant effect on ice algal primary production under severe light‐limited conditions but a weaker effect on radiation transport. Knowledge of this ‐temperature dependence would further improve our understanding of the optical properties of sea ice and the parameterization of ice transparency in large‐scale climate models.

Observation and simulation of the diffuse attenuation coefficient of downwelling irradiance in the Polar Ocean

Observation and simulation of the diffuse attenuation coefficient of downwelling irradiance in the Polar Ocean

An Intra-Tissue Radiometry Microprobe for Measuring Radiance In Situ in Living Tissue.

Organisms appear opaque largely because their outer tissue layers are strongly scattering to incident light; strongly absorbing pigments, such as blood, typically have narrow absorbances, such that the mean free path of light outside the absorbance peaks can be quite long. As people cannot see through tissue, they generally imagine that tissues like the brain, fat, and bone contain little or no light. However, photoresponsive opsin proteins are expressed within many of these tissues, and their functions are poorly understood. Radiance internal to tissue is also important for understanding photosynthesis. For example, giant clams are strongly absorbing yet maintain a dense population of algae deep in the tissue. Light propagation through systems like sediments and biofilms can be complex, and these communities can be major contributors to ecosystem productivity. Therefore, a method for constructing optical micro-probes for measuring scalar irradiance (photon flux intersecting a point) and downwelling irradiance (photon flux crossing a plane perpendicularly) to better understand these phenomena inside living tissue has been developed. This technique is also tractable in field laboratories. These micro-probes are made from heat-pulled optical fibers that are then secured in pulled glass pipettes. To change the angular acceptance of the probe, a 10-100 µm sized sphere of UV-curable epoxy mixed with titanium dioxide is then secured to the end of a pulled, trimmed fiber. The probe is inserted into living tissue, and its position is controlled using a micromanipulator. These probes are capable of measuring in situ tissue radiance at spatial resolutions of 10-100 µm or on the scale of single cells. These probes were used to characterize the light reaching the adipose and brain cells 4 mm below the skin of a living mouse and to characterize the light reaching similar depths within living algae-rich giant clam tissue.

Remote sensing and machine learning method to support sea surface pCO2 estimation in the Yellow Sea

With global climate changing, the carbon dioxide (CO2) absorption rates increased in marginal seas. Due to the limited availability of in-situ spatial and temporal distribution data, the current status of the sea surface carbon dioxide partial pressure (pCO2) in the Yellow Sea is unclear. Therefore, a pCO2 model based on a random forest algorithm has been developed, which was trained and tested using 14 cruise data sets from 2011 to 2019, and remote sensing satellite sea surface temperature, chlorophyll concentration, diffuse attenuation of downwelling irradiance, and in-situ salinity were used as the input variables. The seasonal and interannual variations of modeled pCO2 were discussed from January 2003 and December 2021 in the Yellow Sea. The results showed that the model developed for this study performed well, with a root mean square difference (RMSD) of 43 μatm and a coefficient of determination (R2) of 0.67. Moreover, modeled pCO2 increased at a rate of 0.36 μatm year-1 (R2 = 0.27, p &amp;lt; 0.05) in the YS, which is much slower than the rate of atmospheric pCO2 (pCO2air) rise. The reason behind it needs further investigation. Compared with pCO2 from other datasets, the pCO2 derived from the RF model exhibited greater consistency with the in-situ pCO2 (RMSD = 55 μatm). In general, the RF model has significant improvement over the previous models and the global data sets.

Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation

This paper evaluates the Arable Mark 2 sensor, an automated and low-cost radiometer, for its potential to retrieve surface reflectance data and validate orbital sensors such as the Landsat-8 (L8) Operational Land Imager (OLI) Level 2 product. While orbital sensors are widely used for monitoring solar radiation changes, managing natural resources, and understanding climatic trends, atmospheric effects can make it challenging to obtain accurate measurements. Equipped with multiple sensors, including long-wave and short-wave radiometers, the Arable Mark 2 sensor can measure upwelling and downwelling irradiance to calculate surface reflectance. To assess the accuracy and consistency of the Arable Mark 2 sensor, the study performed a cross-calibration using a ground truth measurement collected with the Analytical Spectral Device (ASD) as the reference point. Additionally, a spectral band adjustment factor (SBAF) was applied across the calibrated Arable surface reflectance to compare it against the orbital sensor. An automated library aided in calculating SBAF for the days with unavailable hyperspectral data. The study found that the Arable Mark 2 sensor can provide accurate surface reflectance data that can be used for orbital sensor validation. The Arable sensor was successfully calibrated against the ASD FieldSpec with an average difference of less than 1/10 reflectance unit (reflectance unit = 0.01) for the blue, green, yellow, and red bands. The red-edge and NIR-1 bands showed an average difference of less than 1/2 reflectance units, while the NIR-2 band had an average difference of less than 1/10 reflectance unit of calibration accuracy. The calibrated Arable surface reflectance data was then compared against orbital sensor surface reflectance data, and the results showed good agreement between the two datasets. The study concludes that the low-cost and automated nature of the Arable Mark 2 sensor makes it a promising tool for surface reflectance retrieval and orbital sensor validation.

The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals

Abstract. We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T, pronounced []) for quantifying and mitigating artifacts in atmospheric radiation science algorithms due to spatially inhomogeneous clouds and surfaces and show the benefits of automated, realistic radiance and irradiance generation along extended satellite orbits, flight tracks from entire aircraft field missions, and synthetic data generation from model data. EaR3T is a modularized Python package that provides high-level interfaces to automate the process of 3D radiative transfer (3D-RT) calculations. After introducing the package, we present initial findings from four applications, which are intended as blueprints to future in-depth scientific studies. The first two applications use EaR3T as a satellite radiance simulator for the NASA Orbiting Carbon Observatory 2 (OCO-2) and Moderate Resolution Imaging Spectroradiometer (MODIS) missions, which generate synthetic satellite observations with 3D-RT on the basis of cloud field properties from imagery-based retrievals and other input data. In the case of inhomogeneous cloud fields, we show that the synthetic radiances are often inconsistent with the original radiance measurements. This lack of radiance consistency points to biases in heritage imagery cloud retrievals due to sub-pixel resolution clouds and 3D-RT effects. They come to light because the simulator's 3D-RT engine replicates processes in nature that conventional 1D-RT retrievals do not capture. We argue that 3D radiance consistency (closure) can serve as a metric for assessing the performance of a cloud retrieval in presence of spatial cloud inhomogeneity even with limited independent validation data. The other two applications show how airborne measured irradiance data can be used to independently validate imagery-derived cloud products via radiative closure in irradiance. This is accomplished by simulating downwelling irradiance from geostationary cloud retrievals of Advanced Himawari Imager (AHI) along all the below-cloud aircraft flight tracks of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex, NASA 2019) and comparing the irradiances with the colocated airborne measurements. In contrast to case studies in the past, EaR3T facilitates the use of observations from entire field campaigns for the statistical validation of satellite-derived irradiance. From the CAMP2Ex mission, we find a low bias of 10 % in the satellite-derived cloud transmittance, which we are able to attribute to a combination of the coarse resolution of the geostationary imager and 3D-RT biases. Finally, we apply a recently developed context-aware Convolutional Neural Network (CNN) cloud retrieval framework to high-resolution airborne imagery from CAMP2Ex and show that the retrieved cloud optical thickness fields lead to better 3D radiance consistency than the heritage independent pixel algorithm, opening the door to future mitigation of 3D-RT cloud retrieval biases.

Study of the Profile Distribution of the Diffuse Attenuation Coefficient and Secchi Disk Depth in the Northwestern South China Sea

The diffuse attenuation coefficient for the downwelling irradiance Kdz,λ is a critical parameter in terms of the optical properties of the ocean. In the northwestern South China Sea, there are complex physical processes, and the accurate estimation of Kdz,λ in the northwestern South China Sea is critical for the study and application of the underwater light field and water constituents. In this study, using Hydrolight 6.0 (HL60) software, Kdz,λ was simulated based on the inherent optical properties (IOPs) and chlorophyll a concentration dataset in the northwestern South China Sea. The simulations were in good agreement with the results calculated by the model of Lee (2005), and the spectral characteristics of Kdz,λ were consistent with several oceanic types according to Jerlov’s classification. The horizontal and vertical distribution characteristics of Kd(z,490) were studied in the two typical upwelling areas of eastern Hainan Island and eastern Vietnam. Kd(z,490) in eastern Hainan Island exhibited an overall decreasing trend from west to east at the same depth, while the vertical depth of the maximum value of Kd(z,490) in eastern Hainan Island was found to increase from west to east, which was significantly associated with the distribution trend of the temperature and salinity. Kd(z,490) in eastern Vietnam exhibited unique horizontal and vertical distribution characteristics due to upwelling, with a low temperature and high salinity. A satisfactory linear relationship between Kd(z,490) and Kdz,λ was found from 420 to 580 nm with R2 &gt; 0.76, root mean square (RMSE) ≤ 0.010 m−1, and mean absolute percentage error (MAPE) &lt; 9%, and this result indicated that Kdz,λ from 420~580 nm could be estimated with Kd(z,490). The regression accuracy sharply decreased after 580 nm, indicating that Kdz,λ estimation based on Kd(z,490) can be more suitably achieved from 420~580 nm and becomes inaccurate after 580 nm. Based on the simulations, an empirical relationship for ZSDD estimation involving Kd,10%490 was developed, and ZSDD in the northwestern South China Sea was calculated, with a range of 5–23 m and a suitable agreement with ZSDD obtained via the method of Lee (2018).

Transfer model to determine the above-water remote-sensing reflectance from the underwater remote-sensing ratio.

Remote-sensing reflectance, Rrs(λ, θ, Δϕ, θs), contains the spectral color information of the water body below the sea surface and is a fundamental parameter to derive satellite ocean color products such as chlorophyll-a, diffuse light attenuation, or inherent optical properties. Water reflectance, i.e., spectral upwelling radiance, normalized by the downwelling irradiance, can be measured under- or above-water. Several models to extrapolate this ratio from underwater "remote-sensing ratio", rrs(λ), to the above-water Rrs, have been proposed in previous studies, in which the spectral dependency of water refractive index and off-nadir viewing directions have not been considered in detail. Based on measured inherent optical properties of natural waters and radiative transfer simulations, this study proposes a new transfer model to spectrally determine Rrs from rrs for different sun-viewing geometries and environmental conditions. It is shown that, compared to previous models, ignoring spectral dependency leads to a bias of ∼2.4% at shorter wavelengths (∼400 nm), which is avoidable. If nadir-viewing models are used, the typical 40°-off nadir viewing geometry will introduce a difference of ∼5% in Rrs estimation. When the solar zenith angle is higher than 60°, these differences of Rrs have implications for the downstream retrievals of ocean color products, e.g., > 8% difference for phytoplankton absorption at 440 nm and >4% difference for backward particle scattering at 440 nm by the quasi-analytical algorithm (QAA). These findings demonstrate that the proposed rrs-to-Rrs model is applicable to a wide range of measurement conditions and provides more accurate estimates of Rrs than previous models.

Relationships among diurnal variations of polar night cloud, precipitation, surface temperatures, and the fair-weather return current of the global electric circuit (GEC)

Over the past five years (June 2017-current), the vertical electric field (Ez) as well as numerous cloud, precipitation and radiation properties have been monitored at the Department of Energy-Atmospheric Radiation Measurement (DOE ARM) North Slope of Alaska (NSA) field site. Comparisons between the composite diurnal averaged fair-weather Ez, and composite ceilometer derived cloud base height during the polar night, reveal a significant correlation between the parameters (r = 0.62), supporting previous studies that there is high correlation between local electric field and cloud properties. With the use of extensive instrumentation at the site, such as the Micro Pulse Lidar (MPL), Ka-band Zenith Radar (KAZR), ceilometer, SKYRAD, Precipitation Imaging Package (PIP), among others, this study provides a more comprehensive examination of the diurnal cycle of cloud and precipitation properties along with the localized fair-weather return current of the larger Global Electric Circuit (GEC) system. Comparisons between the composite diurnal averaged fair-weather Ez, and cloud thickness, maximum column backscatter, and precipitation particle counts all show similar diurnal variability during the polar night, indicating that during the largest magnitude fair weather Ez time periods, clouds bases tend to be higher, clouds are thicker, have a larger column backscatter, and display more precipitating particles. Furthermore, a slight diurnal variability in the polar night surface temperature was found to be highly correlated (r = 0.87) to the longwave downwelling irradiance measured by SKYRAD, indicating that the variations in the physical properties of local clouds could modulate the diurnal polar night surface temperature variability on the order of 0.5 °C/day. These findings emphasize the importance and global nature of the GEC system, with the global aggregate of thunderstorms and electrified clouds potentially influencing polar night cloud properties as well as diurnal wintertime polar surface temperature variation.

Non-photochemical quenching estimates from in situ spectroradiometer measurements: implications on remote sensing of sun-induced chlorophyll fluorescence in lakes.

Quantum yield of fluorescence (ϕF) is key to interpret remote measurements of sun-induced fluorescence (SIF), and whether the SIF signal is governed by photochemical quenching (PQ) or non-photochemical quenching (NPQ). Disentangling PQ from NPQ allows using SIF estimates in various applications in aquatic optics. However, obtaining ϕF is challenging due to its high temporal and physiological variability, and the combined measurements needed to enclose all relevant optical paths. In inland waters, this type of data is scarce and information on diurnal and seasonal ϕF dynamics are almost unknown. Using an autonomous hyperspectral Thetis profiler in Lake Geneva, we demonstrate how to estimate ϕF using an ensemble of in-situ measurements acquired between 2018 to 2021. We use vertical and temporal changes in retrieved ϕF to determine NPQ and PQ conditions. We observed NPQ in 36% of the total daytime profiles used in the ϕF analysis. While downwelling irradiance is a significant contributor to ϕF, its role cannot be easily interpreted. Other factors such as phytoplankton photoregulation and assemblages also likely play significant roles in quenching mechanisms. We conclude that an adapted approach exploiting in-situ data is suitable to determine diurnal and seasonal NPQ occurrence, and helps develop future remote sensing algorithms.

4D structure of bio-optical characteristics of the upper 70 m layer of the Black Sea: Bio-Argo floats and ocean color scanners

• Vertical profiles of bio-optical properties varied between seasons and regions. • “Bridge” between bio-Argo floats and satellite oceancolor data. • Retrieval of 4D structure of bio-optical properties euphotic layer of the Black Sea based bio-Argo floats data and merged regional ocean color products. Assessment of the marine ecosystem functioning and energy and matter fluxes from one trophic level to another in marine food web requires the knowledge on light field in the sea upper layer. The detailed information about spatial and temporal variability in light intensity provides the correct assessment of the primary production in the sea. To describe downwelling irradiance in the sea the vertical distribution of the all in water optically active components should be known. The mean profile and two eigenvectors for chlorophyll- a concentration, particle backscattering coefficient, colored dissolved organic matter absorption coefficient and temperature have been derived from bio-Argo float data within 0-70m layer with 5 m resolution. Long-term (2013-2018) dataset of bio-Argo float data have been used to get ten-days averaged parameter values - 37 data subsets/year. To get link between surface satellite data and statistics (parameter profiles) derived from bio-Argo float the chlorophyll- a concentration, light absorption coefficient of colored detrital matter at 490 nm and particle backscattering coefficient at 555 nm retrieved based on regional algorithms, temperature - standard satellite product have been related with statistics derived from bio-Argo float measurements of chlorophyll- a concentration, particle backscattering, colored dissolved organic matter and temperature in surface layer. The obtained relationships allowed to solve the task - to retrieve 4D fields (x,y,z,t) of parameters in the 0-70 m layer of the Black Sea.

Integrated fiber optic spectrally resolved downwelling irradiance sensor for pushbroom spectrometers.

We present an integrated fiber optic spectrally resolved downwelling irradiance sensor for pushbroom hyperspectral imagers. The system comprises of a cosine corrector and custom fiber patch cables, collecting the ambient light in a large solid angle and feeding it directly to the entrance slit of the spectrometer. The system enables simultaneous measurement of downwelling and upwelling irradiance using the main hyperspectral camera sensor. As a demonstration, the spectral reflectance of a soil sample was measured with a RMSE of 8.4%, a significant improvement on the RMSE of 54% found without correction. At a weight of approximately 10 grams, this system provides a substantial weight saving over standalone incident light sensing instruments.

Asymptotic optical attenuation in sea water

A simple method is proposed for calculating the asymptotic attenuation coefficient K∞ of sea water. The method is based on the scaling analysis of the radiative transfer equation within the small-angle approximation. From the scaling, it follows that K∞ is analytically expressed in terms of the absorption coefficient, the transport scattering coefficient, and two dimensionless numeric constants (scaling exponents) depending on a specific scattering phase function. For a given phase function, the scaling exponents can be determined by numerical calculations of the downwelling irradiance. We test the method on a number of oceanlike scattering models. A direct numerical integration of the radiative transfer equation is carried out with the DISORT code for the Petzold, Morel et al., Fournier–Forand and Kopelevich phase functions. Our numerical results agree perfectly with the small-angle scaling and allow us to establish an explicit expression for K∞ for each phase function. We find that the scaling exponents depend rather weakly on the specific angular profile of the phase function and can be taken equal to their values for the Henyey–Greenstein function (this approximation leads to a relative error in the value K∞ less than 5%). So that, within a few percent accuracy, the coefficient K∞ is governed only by the absorption and transport scattering coefficients and is described by a universal formula.

Retrieval of chlorophyll fluorescence from a large distance using oxygen absorption bands

The detection of solar induced chlorophyll fluorescence (SIF) in the field with spectrometers is based on the depth of the solar Fraunhofer or oxygen absorption lines in the upwelling radiance compared to that in the downwelling irradiance. This relative depth enables the differentiation of SIF from the reflected radiation. Recent studies have shown that if oxygen bands are used to retrieve SIF from tower-based measurements, then atmospheric correction is required. This study presents a band shape fitting (BSF) approach to retrieve both the relative optical path length (deepening) and SIF (infilling) from field measurements at the same time, using information in the measured spectral shape of the O2 feature. This approach is an alternative to using radiative transfer process models for estimating atmospheric transmittance. The method was applied to measurements taken from 100 m elevation above a forest, yielding plausible results for SIF in the O2A and O2B bands. The sensitivity to combined atmospheric and instrument characteristics prohibits application at much greater distances from the surface.

Calibration of BRDF Based on the Field Goniometer System Using a UAV Multispectral Camera.

The bidirectional reflectance distribution function (BRDF) is important for estimating the physical properties of a surface in remote sensing. In the laboratory, the BRDF can be estimated quickly and accurately using a goniometer, but it is very difficult to operate in the field. The purpose of this study was to evaluate whether estimating the BRDF with reasonable accuracy using an unmanned aerial vehicle (UAV) with a multispectral camera is possible in the field. Hemispherical reflectance was created from images taken using an UAV multispectral camera. The ground targets were four calibrated reference tarps (CRTs) of different reflectance, and the UAV was operated five times. Down-welling irradiance for reflectance calculation was measured in two ways: a sunlight sensor was mounted on a UAV, and a spectroradiometer with a remote cosine receptor (RCR) was installed on the ground. The BRDF was assessed through the anisotropy factor (ANIF) of the CRT reflectance derived from the collected data. As a result, the irradiance data for the reflectance calculation were more effective from the spectroradiometer with RCR on the ground than from the sunlight sensor mounted on an UAV. Furthermore, the high reflectance CRTs, ANIF, and BRDF had similar results. Therefore, when analyzing the BRDF, the effectiveness can be guaranteed when the reflectance of the target is over 21~46%, because a low reflectance tendency differs due to the adjacency effect. In addition, weather affects irradiance, so it is more effective to conduct fieldwork in clear weather.

Spatiotemporal variability in bio-optical characteristics of the southwestern tropical Indian Ocean during boreal summer: Biophysical influences

The bio-optical characteristics of the surface ocean play a pivotal role in radiative transfer and photosynthetic carbon fixation. To examine spatiotemporal variability of bio-optical properties in the tropical Indian Ocean, a 10-day time-series comparative study was made at one fixed and 3 variable stations in the southwestern tropical Indian Ocean (SWTIO), and 4-stations in the Equatorial Indian Ocean (EIO) during June 2014. A total of 24 hyperspectral radiometer profiles were collected along with physicochemical parameters and phytoplankton pigments analysis. A negative sea level anomaly (SLA), higher precipitation, a more stable mixed layer, and higher buoyancy frequency (N2) were observed in SWTIO compared to EIO. The deep chlorophyll maxima (DCM) in SWTIO were characterised by deep biomass maxima (DBM) whereas in the EIO it was due to deep photoacclimation maxima (DAM). The photosynthetically available radiation (PAR) profiles were obtained by integrating downwelling irradiance from 400 to 700 nm suggesting that the SWTIO had a greater abundance of optically active substances (OAS) than the EIO. Surface chlorophyll a (Chl-a) was found to have significant relationships with phytoplankton absorption coefficient (aph(443)). Chl-a-specific phytoplankton absorption coefficients (a*ph(443)), total absorption coefficient (at(443)), and particulate backscattering (bbp(555)) were also related to Chl-a implying that the phytoplankton was in growth/active phase. The time-series variability of the phytoplankton and changes in the photosynthetic pigments (PSP) to photoprotective pigments (PPP) ratio at SWTIO suggested phytoplankton community shift during 5th to 7th days. Microplankton percent-abundances showed distinct linear relationships with Chl-a and bbp(555) implying that carbon fixation per unit Chl-a was higher in microplankton than in nano and picoplankton at SWTIO. The in situ Chl-a, aph(443), adg(443), Kd(490) and C¯pd were used to validate the various empirical algorithms available as well as the satellite-retrieved data. The MODIS and VIIRS data correlated moderately. This study would help towards improving regional optimized algorithms for OAS and provide a baseline understanding of the scale of variability of primary production and carbon flux from space. In addition, this would contribute valuable bio-optical data from an under-sampled geographical area.