Estimation of new production in the ocean by compound remote sensing

Estimation of sea surface nitrate from space: Current status and future potential

Sea surface nitrate (SSN) plays an important role in assessing phytoplankton growth and new production in the ocean. Field sampling of SSN data is important, but limited by data quantity both spatially and temporally. Satellite remote sensing can contribute through providing spatial and temporal data to such assessments. During the past 30 years many studies have been published focusing on SSN retrievals from satellites to a greater or less extent. In this study, we reviewed the progresses of SSN estimation from satellites in both open ocean and coastal waters. Because of the lack of electromagnetic properties of SSN, satellite retrievals of SSN were most realized by developing relationships between SSN and related environmental variables (e.g., sea surface temperature, chlorophyll-a concentration, sea surface salinity), using traditional empirical regressions and novel machine learning techniques. We synthesized most of the peer-reviewed studies for both open and coastal oceans, in terms of study areas, model inputs, regression formulas, and model uncertainties. In general, regional SSN algorithms were most developed in coastal oceans with upwelling or river discharges. The published SSN algorithms had varying uncertainties with a wide range of 0.83-6.87 μmol/L, and the uncertainties were significantly reduced in recent studies, with more field measurements available and better understanding of the physical and biogeochemical processes in driving nitrate dynamics.

Inversion diffuse attenuation coefficient of photosynthetically active radiation based on deep learning

Accurate estimation of the diffuse attenuation coefficient of photosynthetically active radiation, Kd(PAR), is critical for understanding and modeling key physical, chemical, and biological processes in waters. In this study, a deep learning model (DLKPAR) was developed for remotely estimating Kd(PAR). Compared to the traditional empirical algorithms and semi-analytical algorithm, DLKPAR demonstrated an improvement in the model's stability and accuracy. By using in situ NOMAD data to evaluate the model's performance, DLKPAR had lower root mean square difference (RMSD; 0.028 vs. 0.030-0.048 m-1) and mean absolute relative difference (MARD; 0.14 vs. 0.17-0.25) and higher R2 (0.94 vs. 0.82-0.94). The statistical results of the matchup NOMAD and Argo data to the MODIS also indicated DLKPAR improves the inversion accuracy of Kd(PAR) and could be applied to remotely estimate Kd(PAR) in the global oceans. Therefore, we anticipate that DLKPAR could yield reliable Kd(PAR) values from ocean color remote sensing, providing an accurate estimation of visible light attenuation in the upper ocean and facilitating biogeochemical cycle research.

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

The hidden influence of large particles on ocean colour

Optical constituents in the ocean are often categorized as water, phytoplankton, sediments and dissolved matter. However, the optical properties of seawater are influenced, to some degree, by scattering and absorption by all particles in the water column. Here we assess the relevant size ranges for determining the optical properties of the ocean. We present a theoretical basis supporting the hypothesis that millimetre-size particles, including zooplankton and fish eggs, can provide a significant contribution to bulk absorption and scattering of seawater and therefore ocean color. Further, we demonstrate that existing in situ instruments are not capable of correctly resolving the impact of such large particles, possibly leading to their optical significance being overlooked. These findings refresh our perspective on the potential of ocean color and invite new applications of remote sensing for monitoring life close to the ocean surface.

The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

We present a model that estimates the spectral phytoplankton absorption coefficient (aph(λ)) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on aph(λ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total aph(λ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine aph(λ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific aph(λ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (R_rs) and Secchi disk depth (Z_SD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01-6.8  m^-1) from R_rs(λ) for the 350-560 nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly "exact" analytical inversion of an R_rs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350-560 nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of Z_SD places an extra constraint in the inversion of R_rs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of Z_SD and R_rs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

Enhanced coupling of light into a turbid medium through microscopic interface engineering

There are many optical detection and sensing methods used today that provide powerful ways to diagnose, characterize, and study materials. For example, the measurement of spontaneous Raman scattering allows for remote detection and identification of chemicals. Many other optical techniques provide unique solutions to learn about biological, chemical, and even structural systems. However, when these systems exist in a highly scattering or turbid medium, the optical scattering effects reduce the effectiveness of these methods. In this article, we demonstrate a method to engineer the geometry of the optical interface of a turbid medium, thereby drastically enhancing the coupling efficiency of light into the material. This enhanced optical coupling means that light incident on the material will penetrate deeper into (and through) the medium. It also means that light thus injected into the material will have an enhanced interaction time with particles contained within the material. These results show that, by using the multiple scattering of light in a turbid medium, enhanced light-matter interaction can be achieved; this has a direct impact on spectroscopic methods such as Raman scattering and fluorescence detection in highly scattering regimes. Furthermore, the enhanced penetration depth achieved by this method will directly impact optical techniques that have previously been limited by the inability to deposit sufficient amounts of optical energy below or through highly scattering layers.

Seasonal nitrate algorithms for nitrate retrieval using OCEANSAT-2 and MODIS-AQUA satellite data

In situ datasets of nitrate, sea surface temperature (SST), and chlorophyll a (chl a) collected during the monthly coastal samplings and organized cruises along the Tamilnadu and Andhra Pradesh coast between 2009 and 2013 were used to develop seasonal nitrate algorithms. The nitrate algorithms have been built up based on the three-dimensional regressions between SST, chl a, and nitrate in situ data using linear, Gaussian, Lorentzian, and paraboloid function fittings. Among these four functions, paraboloid was found to be better with the highest co-efficient of determination (postmonsoon: R2=0.711, n=357; summer: R2=0.635, n=302; premonsoon: R2=0.829, n=249; and monsoon: R2=0.692, n=272) for all seasons. Based on these fittings, seasonal nitrate images were generated using the concurrent satellite data of SST from Moderate Resolution Imaging Spectroradiometer (MODIS) and chlorophyll (chl) from Ocean Color Monitor (OCM-2) and MODIS. The best retrieval of modeled nitrate (R2=0.527, root mean square error (RMSE)=3.72, and mean normalized bias (MNB)=0.821) was observed for the postmonsoon season due to the better retrieval of both SST MODIS (28 February 2012, R2=0.651, RMSE=2.037, and MNB=0.068) and chl OCM-2 (R2=0.534, RMSE=0.317, and MNB=0.27). Present results confirm that the chl OCM-2 and SST MODIS retrieve nitrate well than the MODIS-derived chl and SST largely due to the better retrieval of chl by OCM-2 than MODIS.

Biogenic carbon cycling in the upper ocean: effects of microbial respiration

Food-web processes are important controls of oceanic biogenic carbon flux and ocean-atmosphere carbon dioxide exchange. Two key controlling parameters are the growth efficiencies of the principal trophic components and the rate of carbon remineralization. We report that bacterial growth efficiency is an inverse function of temperature. This relationship permits bacterial respiration in the euphotic zone to be computed from temperature and bacterial production. Using the temperature-growth efficiency relationship, we show that bacterial respiration generally accounts for most community respiration. This implies that a larger fraction of assimilated carbon is respired at low than at high latitudes, so a greater proportion of production can be exported in polar than in tropical regions. Because bacterial production is also a function of temperature, it should be possible to compute euphotic zone heterotrophic respiration at large scales using remotely sensed information.

Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing

A new generation of ocean colour satellites is now operational, with frequent observation of the global ocean. This paper reviews the potential to estimate marine primary production from satellite images. The procedures involved in retrieving estimates of phytoplankton biomass, as pigment concentrations, are discussed. Algorithms are applied to SeaWiFS ocean colour data to indicate seasonal variations in phytoplankton biomass in the Celtic Sea, on the continental shelf to the south west of the UK. Algorithms to estimate primary production rates from chlorophyll concentration are compared and the advantages and disadvantage discussed. The simplest algorithms utilise correlations between chlorophyll concentration and production rate and one equation is used to estimate daily primary production rates for the western English Channel and Celtic Sea; these estimates compare favourably with published values. Primary production for the central Celtic Sea in the period April to September inclusive is estimated from SeaWiFS data to be 102 gC m(-2) in 1998 and 93 gC m(-2) in 1999; published estimates, based on in situ incubations, are ca. 80 gC m(-2). The satellite data demonstrate large variations in primary production between 1998 and 1999, with a significant increase in late summer in 1998 which did not occur in 1999. Errors are quantified for the estimation of primary production from simple algorithms based on satellite-derived chlorophyll concentration. These data show the potential to obtain better estimates of marine primary production than are possible with ship-based methods, with the ability to detect short-lived phytoplankton blooms. In addition, the potential to estimate new production from satellite data is discussed.

Raman scattering by pure water and seawater

Measurements of the magnitude and spectral distribution of the Raman-scattering coefficients of pure water (b(rw)) and seawater (b(rs)) are presented. Two independent measurements of the spectral distribution of the Raman-scattering coefficient of pure water were made for incident wavelengths ranging from 250 to 500 nm. These measurements revealed a strong dependence of b(rw) on wavelength that could be represented by a (lambda')(-5.3+/-0.3) relationship, where lambda' is the incident wavelength, or a lambda(-4.6+/-0.3) relationship, where lambda is the Raman-scattered wavelength, when normalized to units of photons. The corresponding relationships for normalization to energy are (lambda')(-5.5+/-0.4) and lambda(-4.8+/-0.3), respectively. These relationships are found to be consistent with resonance Raman theory for an absorption wavelength of 130 nm. The absolute value of b(rw) for the 3400-cm(-1) line was found to be (2.7 +/- 0.2) x 10(-4) m(-1) for an incident wavelength of 488 nm, which is consistent with a number of earlier reports. The difference between the magnitudes of the Raman-scattering coefficients for pure water and seawater was statistically insignificant.

Remote sensing of primary production in the ocean: promise and fulfilment

Remote sensing of ocean colour affords us our only window into the synoptic state of the pelagic ecosystem, and is likely to remain the only such option into the foreseeable future. Estimation of primary production from remotely sensed data on ocean colour is a research problem in two parts: (i) the construction of a local algorithm; and (ii) the development of a protocol for extrapolation. Good local algorithms exist but their proper implementation requires that certain parameters be specified. Protocols for extrapolation have to include procedures for the assignment of these parameters. One suitable approach is based on partition of the ocean into a suite of domains and provinces within which physical forcing, and the algal response to it, are distinct. This approach is still in its infancy, but is best developed for the North Atlantic. Using this method, and using the accumulated data from oceanographic expeditions, leads to an estimate for the annual primary production of the North Atlantic at the basin scale. Direct validation of the result is not possible in the absence of an independent calculation, but the potential errors involved may be assessed.

Vertical Flux of Biogenic Carbon in the Ocean: Is There Food Web Control?

Models of biogenic carbon (BC) flux assume that short herbivorous food chains lead to high export, whereas complex microbial or omnivorous food webs lead to recycling and low export, and that export of BC from the euphotic zone equals new production (NP). In the Gulf of St. Lawrence, particulate organic carbon fluxes were similar during the spring phytoplankton bloom, when herbivory dominated, and during nonbloom conditions, when microbial and omnivorous food webs dominated. In contrast, NP was 1.2 to 161 times greater during the bloom than after it. Thus, neither food web structure nor NP can predict the magnitude or patterns of BC export, particularly on time scales over which the ocean is in nonequilibrium conditions.

Covariance of the absorption of phytoplankton, colored dissolved organic matter, and detritus in case I waters, as deduced from the Coastal Zone Color Scanner bio-optical algorithm

The universal bio-optical algorithm of the Coastal Zone Color Scanner (CZCS) for case I waters implicitly contains an average covariance of the absorption by phytoplankton and colored dissolved organic matter (CDOM) and detritus. We made that covariance explicit by combining the CZCS algorithm with an expression for reflectance. The spectral variation of absorption by CDOM plus detritus for case I waters may be estimated by the expression a(gd(λ)) = 2a(ph)(443)*chl{exp[-0.013(λ - 443)].

Spontaneous Magnetic Ordering in the Fullerene Charge-Transfer Salt (TDAE)C60

The zero-field muon spin relaxation technique has been used in the direct observation of spontaneous magnetic order below a Curie temperature (T(c)) of approximately 16.1 kelvin in the fullerene charge-transfer salt (tetrakisdimethylaminoethylene)C(60) [(TDAE)C(60)]. Coherent ordering of the electronic magnetic moments leads to a local field of 68(1) gauss at the muon site at 3.2 kelvin (parentheses indicate the error in the last digit). Substantial spatially inhomogeneous effects are manifested in the distribution of the local fields, whose width amounts to 48(2) gauss at the same temperature. The temperature evolution of the internal magnetic field below the freezing temperature mirrors that of the saturation magnetization, closely following the behavior expected for collective spin wave (magnon) excitations. The transition to a ferromagnetic state with a T(c) higher than that of any other organic material is now authenticated.