scholarly journals An operational overview of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Northeast Pacific field deployment

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
David A. Siegel ◽  
Ivona Cetinić ◽  
Jason R. Graff ◽  
Craig M. Lee ◽  
Norman Nelson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Food Web ◽  
Mixed Layer ◽  
Global Ocean ◽  
Nutrient Concentrations ◽  
Data Set ◽  
Northeast Pacific ◽  
Field Deployment ◽  
Ocean Station Papa

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal. The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set.

scholarly journals Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000

2009 ◽  
Vol 6 (11) ◽  
pp. 2333-2353 ◽  
Author(s):  
M. Vichi ◽  
S. Masina
Keyword(s):  
Organic Carbon ◽  
Mixed Layer ◽  
General Circulation ◽  
Net Primary Production ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Data Set ◽  
Surface Ocean ◽  
Ocean Biogeochemistry

Abstract. Global Ocean Biogeochemistry General Circulation Models are useful tools to study biogeochemical processes at global and large scales under current climate and future scenario conditions. The credibility of future estimates is however dependent on the model skill in capturing the observed multi-annual variability of firstly the mean bulk biogeochemical properties, and secondly the rates at which organic matter is processed within the food web. For this double purpose, the results of a multi-annual simulation of the global ocean biogeochemical model PELAGOS have been objectively compared with multi-variate observations from the last 20 years of the 20th century, both considering bulk variables and carbon production/consumption rates. Simulated net primary production (NPP) is comparable with satellite-derived estimates at the global scale and when compared with an independent data-set of in situ observations in the equatorial Pacific. The usage of objective skill indicators allowed us to demonstrate the importance of comparing like with like when considering carbon transformation processes. NPP scores improve substantially when in situ data are compared with modeled NPP which takes into account the excretion of freshly-produced dissolved organic carbon (DOC). It is thus recommended that DOC measurements be performed during in situ NPP measurements to quantify the actual production of organic carbon in the surface ocean. The chlorophyll bias in the Southern Ocean that affects this model as well as several others is linked to the inadequate representation of the mixed layer seasonal cycle in the region. A sensitivity experiment confirms that the artificial increase of mixed layer depths towards the observed values substantially reduces the bias. Our assessment results qualify the model for studies of carbon transformation in the surface ocean and metabolic balances. Within the limits of the model assumption and known biases, PELAGOS indicates a net heterotrophic balance especially in the more oligotrophic regions of the Atlantic during the boreal winter period. However, at the annual time scale and over the global ocean, the model suggests that the surface ocean is close to a weakly positive autotrophic balance in accordance with recent experimental findings and geochemical considerations.

scholarly journals A 40-year global data set of visible-channel remote-sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Vol 10 (4) ◽  
pp. 2043-2054 ◽  
Author(s):  
Benjamin Roger Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Data Set ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Very High

Abstract. A consistently calibrated 40-year-long data set of visible-channel remote-sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time series. The data set uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record for top-of-atmosphere (TOA) visible-channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote-sensing reflectance. The resulting data set is produced at daily, and archived at monthly, resolution, on a 0.1∘×0.1∘ grid at https://doi.org/10.1594/PANGAEA.892175. The primary aim of deriving this data set is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania huxleyi (where lith concentration >2–5×104 mL−1) over the past 40 years.

scholarly journals Apparent optical properties of the Canadian Beaufort Sea – Part 1: Observational overview and water column relationships

2013 ◽  
Vol 10 (3) ◽  
pp. 4025-4065 ◽  
Author(s):  
D. Antoine ◽  
S. B. Hooker ◽  
S. Belanger ◽  
A. Matsuoka ◽  
M. Babin
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Water Column ◽  
Light Propagation ◽  
Beaufort Sea ◽  
The Arctic ◽  
Ocean Colour ◽  
Data Set ◽  
Turbid Waters ◽  
Kd Values

Abstract. A data set of radiometric measurements collected in the Beaufort Sea (Canadian Arctic) in August 2009 (MALINA project) is analysed in order to describe apparent optical properties (AOPs) in this sea, which is subject to dramatic environmental changes for several decades. The two properties derived from the measurements are the spectral diffuse attenuation coefficient for downward irradiance, Kd, and the spectral remote sensing reflectance, Rrs. The former controls light propagation in the upper water column. The latter determines how light is backscattered out of the water and becomes eventually observable from a satellite ocean colour sensor. The data set includes offshore clear waters of the Beaufort basin as well as highly turbid waters of the Mackenzie River plumes. In the clear waters, we show Kd values that are much larger in the ultraviolet and blue parts of the spectrum than what could be anticipated considering the chlorophyll concentration. A larger contribution of absorption by coloured dissolved organic matter (CDOM) is responsible for this high Kd values, as compared to other oligotrophic areas. In turbid waters, attenuation reaches extremely high values, driven by high loads of particulate materials and also by a large CDOM content. In these two extreme types of waters, current satellite chlorophyll algorithms fail. This is questioning the role of ocean colour remote sensing in the Arctic when Rrs from only the blue and green bands are used. Therefore, other parts of the spectrum (e.g. the red) should be explored if one aims at quantifying interannual changes in chlorophyll in the Arctic from space. The very peculiar AOPs in the Beaufort Sea also advocate for developing specific light propagation models when attempting to predict light availability for photosynthesis at depth.

scholarly journals Apparent optical properties of the Canadian Beaufort Sea – Part 1: Observational overview and water column relationships

2013 ◽  
Vol 10 (7) ◽  
pp. 4493-4509 ◽  
Author(s):  
D. Antoine ◽  
S. B. Hooker ◽  
S. Bélanger ◽  
A. Matsuoka ◽  
M. Babin
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Water Column ◽  
Light Propagation ◽  
Ocean Color ◽  
Beaufort Sea ◽  
The Arctic ◽  
Data Set ◽  
Turbid Waters ◽  
Kd Values

Abstract. A data set of radiometric measurements collected in the Beaufort Sea (Canadian Arctic) in August 2009 (Malina project) is analyzed in order to describe apparent optical properties (AOPs) in this sea, which has been subject to dramatic environmental changes for several decades. The two properties derived from the measurements are the spectral diffuse attenuation coefficient for downward irradiance, Kd, and the spectral remote sensing reflectance, Rrs. The former controls light propagation in the upper water column. The latter determines how light is backscattered out of the water and becomes eventually observable from a satellite ocean color sensor. The data set includes offshore clear waters of the Beaufort Basin as well as highly turbid waters of the Mackenzie River plumes. In the clear waters, we show Kd values that are much larger in the ultraviolet and blue parts of the spectrum than what could be anticipated considering the chlorophyll concentration. A larger contribution of absorption by colored dissolved organic matter (CDOM) is responsible for these high Kd values, as compared to other oligotrophic areas. In turbid waters, attenuation reaches extremely high values, driven by high loads of particulate materials and also by a large CDOM content. In these two extreme types of waters, current satellite chlorophyll algorithms fail. This questions the role of ocean color remote sensing in the Arctic when Rrs from only the blue and green bands are used. Therefore, other parts of the spectrum (e.g., the red) should be explored if one aims at quantifying interannual changes in chlorophyll in the Arctic from space. The very peculiar AOPs in the Beaufort Sea also advocate for developing specific light propagation models when attempting to predict light availability for photosynthesis at depth.

scholarly journals Physical and optical properties of 2010 Eyjafjallajökull volcanic eruption aerosol: ground-based, LIDAR and airborne measurements in France

2011 ◽  
Vol 11 (9) ◽  
pp. 24631-24670 ◽  
Author(s):  
M. Hervo ◽  
B. Quennehen ◽  
N. I. Kristiansen ◽  
J. Boulon ◽  
A. Stohl ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Optical Properties ◽  
Mass Concentration ◽  
Dispersion Model ◽  
Free Troposphere ◽  
Data Set ◽  
Model Simulations ◽  
Ash Cloud

Abstract. During the Eyjafjallajökull eruption (14 April to 24 May 2010), the volcanic aerosol cloud was observed across Europe by several airborne in-situ and ground-based remote-sensing instruments. On 18 and 19 May, layers of depolarizing particles (i.e. non-spherical particles) were detected in the free troposphere above the Puy de Dôme station, (France) with a Rayleigh-Mie LIDAR emitting at a wavelength of 355 nm, with parallel and crossed polarization channels. These layers in the free troposphere (FT) were also well captured by simulations with the Lagrangian particle dispersion model FLEXPART, which furthermore showed that the ash was eventually entrained into the planetary boundary layer (PBL). Indeed, the ash cloud was then detected and characterized with a comprehensive set of in-situ instruments at the Puy de Dôme station (PdD). In agreement with the FLEXPART simulation, up to 65 μg m−3 of particle mass and 2.2 ppb of SO2 were measured at PdD, corresponding to concentrations higher than the 95 percentile of 2 years of measurements at PdD. Moreover, the number concentration of particles increased to 24 000 cm−3, mainly in the submicronic mode, but a supermicronic mode was also detected at 2 μm. The resulting optical properties of the ash aerosol were characterized by a low Ångström exponent (1.1), showing the dominance of supermicronic particles. For the first time to our knowledge, the combination of in-situ optical and physical characterization of the volcanic ash allowed the calculation of the mass-to-extinction ratio (η) with no assumptions on the aerosol density, which was found to be significantly different from the background boundary layer aerosol (max: 1.42 g m−2 as opposed to 0.27 ± 0.03 g m−2). Using this ratio, ash mass concentration in the volcanic plume derived from LIDAR measurements was found to be 700 ± 25 μg m−3 when the plume was located in the FT (3000 m a.s.l. – above sea level). This ratio could also be used to retrieve an aerosol mass concentration of 523 ± 54 μg m−3 on 19 April, when LIDAR observations detected the ash cloud at 3000 m a.s.l. in correspondence with model simulations (FLEXPART). On 22 April, another ash plume entered the BL, and although it was more diluted than during the May episode, the French research aircraft ATR42 that passed over Clermont-Ferrand in the PBL confirmed the presence of particles with a supermicronic mode, again centred on a diameter of 2 μm. This data set combining airborne, ground-based and remote sensing observations with dispersion model simulations shows an overall very good coherence during the volcanic eruption period, which allows a good confidence in the characteristics of the ash particles that can be derived from this unique data set.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

2021 ◽  
Author(s):  
Hibiki M. Noda ◽  
Hiroyuki Muraoka ◽  
Kenlo Nishida Nasahara
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Spectral Reflectance ◽  
Canopy Structure ◽  
Terrestrial Ecosystems ◽  
Optical Remote Sensing ◽  
Canopy Reflectance ◽  
Broadleaf Forest ◽  
Leaf Optical Properties ◽  
Deciduous Broadleaf Forest

AbstractThe need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

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