scholarly journals Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data

2010 ◽  
Vol 413 ◽  
pp. 163-183 ◽  
Author(s):  
EA Becker ◽  
KA Forney ◽  
MC Ferguson ◽  
DG Foley ◽  
RC Smith ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
California Current ◽  
Remotely Sensed ◽  
Temperature Data ◽  
Sea Surface ◽  
Habitat Models ◽  
Surface Temperature Data

Dynamic-stochastic model for assimilation of remotely-sensed sea surface temperature data

1993 ◽  
Vol 4 (2) ◽  
pp. 103-112
Author(s):  
I. E. Timchenko ◽  
V. D. Yarin ◽  
E. F. Vasechkina ◽  
V. Yu. Polyanichev
Keyword(s):  
Stochastic Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Remotely Sensed ◽  
Temperature Data ◽  
Sea Surface ◽  
Dynamic Stochastic ◽  
Surface Temperature Data

An Evaluation of the Use of Remotely Sensed Parameters for Prediction of Incidence and Risk Associated with Vibrio parahaemolyticus in Gulf Coast Oysters (Crassostrea virginica)

2007 ◽  
Vol 70 (4) ◽  
pp. 879-884 ◽  
Author(s):  
A. M. B. PHILLIPS ◽  
A. DePAOLA ◽  
J. BOWERS ◽  
S. LADNER ◽  
D. J. GRIMES
Keyword(s):  
Risk Assessment ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Water Temperature ◽  
Vibrio Parahaemolyticus ◽  
Remotely Sensed ◽  
Temperature Data ◽  
Sea Surface ◽  
The U.S ◽  
Surface Temperature Data

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R2 = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

Sign in / Sign up

Export Citation Format

Share Document