Observation of natural and artificial features on the sea surface from SAR satellite imagery with in-situ measurements

2011 ◽  
Author(s):  
Chris Maingot ◽  
Biao Zhang ◽  
Alexander Soloviev ◽  
Mikhail Gilman ◽  
Silvia Matt ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
In Situ Measurements ◽  
Sea Surface

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

2013 ◽  
Vol 24 (3) ◽  
pp. 147
Author(s):  
Ming LI ◽  
Qinghua YANG ◽  
Jiechen ZHAO ◽  
Lin ZHANG ◽  
Chunhua LI ◽  
...  
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Surface Winds ◽  
Sea Surface Winds

scholarly journals Validation of Aquarius sea surface salinity with in situ measurements from Argo floats and moored buoys

2014 ◽  
Vol 119 (9) ◽  
pp. 6171-6189 ◽  
Author(s):  
Wenqing Tang ◽  
Simon H. Yueh ◽  
Alexander G. Fore ◽  
Akiko Hayashi
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats

scholarly journals Fine-scale features on the sea surface in SAR satellite imagery – Part 1: Simultaneous in-situ measurements

2012 ◽  
Vol 9 (5) ◽  
pp. 2885-2914 ◽  
Author(s):  
A. Soloviev ◽  
C. Maingot ◽  
S. Matt ◽  
R. E. Dodge ◽  
S. Lehner ◽  
...  
Keyword(s):  
Numerical Models ◽  
Three Dimensional ◽  
Companion Paper ◽  
In Situ Measurements ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Upper Ocean ◽  
Fine Scale ◽  
Sar Imagery

Abstract. This work is aimed at identifying the origin of fine-scale features on the sea surface in synthetic aperture radar (SAR) imagery with the help of in-situ measurements as well as numerical models (presented in a companion paper). We are interested in natural and artificial features starting from the horizontal scale of the upper ocean mixed layer, around 30–50 m. These features are often associated with three-dimensional upper ocean dynamics. We have conducted a number of studies involving in-situ observations in the Straits of Florida during SAR satellite overpass. The data include examples of sharp frontal interfaces, wakes of surface ships, internal wave signatures, as well as slicks of artificial and natural origin. Atmospheric processes, such as squall lines and rain cells, produced prominent signatures on the sea surface. This data has allowed us to test an approach for distinguishing between natural and artificial features and atmospheric influences in SAR images that is based on a co-polarized phase difference filter.

scholarly journals Small-Scale Variability in Sea Surface Salinity and Implications for Satellite-Derived Measurements

2013 ◽  
Vol 30 (11) ◽  
pp. 2689-2694 ◽  
Author(s):  
Nadya T. Vinogradova ◽  
Rui M. Ponte
Keyword(s):  
Sampling Error ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Small Scale ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Ocean Salinity ◽  
Mean Square Difference

Abstract Calibration and validation efforts of the Aquarius and Soil Moisture and Ocean Salinity (SMOS) satellite missions involve comparisons of satellite and in situ measurements of sea surface salinity (SSS). Such estimates of SSS can differ by the presence of small-scale variability, which can affect the in situ point measurement, but be averaged out in the satellite retrievals because of their large footprint. This study quantifies how much of a difference is expected between in situ and satellite SSS measurements on the basis of their different sampling of spatial variability. Maps of sampling error resulting from small-scale noise, defined here as the root-mean-square difference between “local” and footprint-averaged SSS estimates, are derived using a solution from a global high-resolution ocean data assimilation system. The errors are mostly <0.1 psu (global median is 0.05 psu), but they can be >0.2 psu in several regions, particularly near strong currents and outflows of major rivers. To examine small-scale noise in the context of other errors, its values are compared with the overall expected differences between monthly Aquarius SSS and Argo-based estimates. Results indicate that in several ocean regions, small-scale variability can be an important source of sampling error for the in situ measurements.

scholarly journals Comparison of Satellite-Based Sea Surface Temperature to In Situ Observations Surrounding Coral Reefs in La Parguera, Puerto Rico

2020 ◽  
Vol 8 (6) ◽  
pp. 453
Author(s):  
Andrea M. Gomez ◽  
Kyle C. McDonald ◽  
Karsten Shein ◽  
Stephanie DeVries ◽  
Roy A. Armstrong ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Coral Reefs ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Thermal Stresses ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Strong Positive Correlation ◽  
La Parguera

Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (>0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

Validation and correction of sea surface salinity retrieval from SMAP

2020 ◽  
Author(s):  
Sisi Qin
Keyword(s):  
Soil Moisture ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Tropical Regions ◽  
Ocean Salinity ◽  
Forecast Models ◽  
Level 3

<p>In this study, Sea Surface Salinity (SSS) Level 3 (L3) daily product derived from Soil Moisture Active Passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from Soil Moisture and Ocean Salinity (SMOS) and in-situ measurements. Generally, the Root Mean Square Error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the Sea Surface Temperature (SST).Then, aregression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.</p>

scholarly journals A Multivariate Regression Approach to Adjust AATSR Sea Surface Temperature to In Situ Measurements

2009 ◽  
Vol 6 (1) ◽  
pp. 8-12 ◽  
Author(s):  
Pierre Tandeo ◽  
Emmanuelle Autret ◽  
Jean FranÇois Piolle ◽  
Jean Tournadre ◽  
Pierre Ailliot
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Multivariate Regression ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Regression Approach

Oceanwide Precise Determination of Sea Surface Height from In-Situ Measurements on Cargo Ships

2014 ◽  
Vol 37 (1) ◽  
pp. 77-96 ◽  
Author(s):  
Ole Roggenbuck ◽  
Jörg Reinking ◽  
Alexander Härting
Keyword(s):  
Sea Surface Height ◽  
Precise Determination ◽  
In Situ Measurements ◽  
Sea Surface

scholarly journals Reconstruction of super-resolution ocean <i>p</i>CO<sub>2</sub> and air–sea fluxes of CO<sub>2</sub> from satellite imagery in the southeastern Atlantic

2015 ◽  
Vol 12 (17) ◽  
pp. 5229-5245 ◽  
Author(s):  
I. Hernández-Carrasco ◽  
J. Sudre ◽  
V. Garçon ◽  
H. Yahia ◽  
C. Garbe ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Partial Pressure ◽  
Sea Surface Temperature ◽  
Ocean Color ◽  
Super Resolution ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Co2 Fluxes ◽  
Partial Pressure Of Co2

Abstract. An accurate quantification of the role of the ocean as source/sink of greenhouse gases (GHGs) requires to access the high-resolution of the GHG air–sea flux at the interface. In this paper we present a novel method to reconstruct maps of surface ocean partial pressure of CO2 ( pCO2) and air–sea CO2 fluxes at super resolution (4 km, i.e., 1/32° at these latitudes) using sea surface temperature (SST) and ocean color (OC) data at this resolution, and CarbonTracker CO2 fluxes data at low resolution (110 km). Inference of super-resolution pCO2 and air–sea CO2 fluxes is performed using novel nonlinear signal processing methodologies that prove efficient in the context of oceanography. The theoretical background comes from the microcanonical multifractal formalism which unlocks the geometrical determination of cascading properties of physical intensive variables. As a consequence, a multi-resolution analysis performed on the signal of the so-called singularity exponents allows for the correct and near optimal cross-scale inference of GHG fluxes, as the inference suits the geometric realization of the cascade. We apply such a methodology to the study offshore of the Benguela area. The inferred representation of oceanic partial pressure of CO2 improves and enhances the description provided by CarbonTracker, capturing the small-scale variability. We examine different combinations of ocean color and sea surface temperature products in order to increase the number of valid points and the quality of the inferred pCO2 field. The methodology is validated using in situ measurements by means of statistical errors. We find that mean absolute and relative errors in the inferred values of pCO2 with respect to in situ measurements are smaller than for CarbonTracker.

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