Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement within-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30 . The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS

Dinnat, Emmanuel

Le Vine, David M.

Soldo, Yan

English

NASA Technical Reports Server

SEA SURFACE SALINITY RETRIEVALS FROM AQUARIUS USING NEURAL NETWORKS 
 
Yan Soldo(1,2), David M. Le Vine(1) and Emmanuel Dinnat(1,3) 
 
(1) NASA Goddard Space Flight Center, Greenbelt, MD, USA 
(2) USRA, Greenbelt, MD, USA 
(3) Chapman University, Orange, CA, USA 
 
ABSTRACT 
 
Even though the Sea Surface Salinity (SSS) retrieved from 
Aquarius are generally very close to in-situ measurements, 
the level of similarity varies with the region and with the 
circumstances of the observations (wind speed, sea surface 
temperature, etc.). SSS is currently retrieved from the 
brightness temperatures measured by Aquarius and applying 
the current theoretical model for the propagation and 
emission of the natural thermal radiation. In this contribution 
we consider an alternative retrieval approach based on a 
Neural Network (NN) with the goal of improving the subsets 
of Aquarius SSS data that are in poorer agreement with in-
situ measurements. The subset considered here are the SSS 
retrieved at latitudes higher than 30˚. The output of the NN 
approach are compared against in-situ measurements using 
four statistical metrics (correlation coefficient, bias, RMSD 
and 5% trimmed range). The output of the NN and the 
nominal Aquarius SSS are compared against SSS values from 
in-situ measurements and from ocean models. From these 
comparisons it appears that the output of the NN matches the 
in-situ measurements better than the nominal Aquarius SSS.  
 
Index Terms— Sea surface salinity, Aquarius, neural 
networks 
 
1. INTRODUCTION 
 
The Aquarius instrument measured the Earth’s natural 
thermal radiation for almost 4 years (2011-2015) in order to 
provide accurate measurements of the Sea Surface Salinity 
(SSS) [1] . Aquarius was equipped with three radiometers 
imaging in a push-broom fashion. In the Aquarius processing, 
the SSS is retrieved from the measured antenna temperatures 
using a retrieval algorithm based on the current understanding 
of the emission and propagation of thermal radiation from the 
ground up to the satellite altitude, with some empirical 
corrections [2] , [3] . The SSS retrieved with this approach 
are generally very accurate, in the sense that they match 
closely with in-situ measurements. However, the level of 
accuracy of the Aquarius SSS varies with the region. 
In this contribution we investigate a different approach 
to retrieve the SSS from Aquarius TB (brightness 
temperature), which is based on a Neural Network (NN).  
NNs have already been implemented for similar 
purposes by the SMOS (Soil Moisture Ocean Salinity) 
mission to retrieve soil moisture [4] and to retrieve SSS from 
simulated measurements [5] . They have also been applied to 
Aquarius data to calibrate satellite retrievals before 
assimilation by ocean models [6]  but as far as we know not 
to retrieve SSS from the L-band brightness temperatures 
themselves. 
 
2. MARGIN FOR IMPROVEMENTS IN THE 
AQUARIUS SSS 
 
The first step of this investigation has been to identify the 
subsets of Aquarius data that we might be able to improve 
with the NN approach. We began with the difference between 
the SSS retrieved from Aquarius (SSS_Aquarius) and the 
SSS measured by the Argo floats (SSS_Argo). The 
colocations between measurements by Aquarius and Argo are 
provided in the latest release of Aquarius data (version 5) and 
do not include averaging. The differences SSS_Aquarius–
SSS_Argo have been plotted as a function of several 
parameters (wind speed, sea surface temperature, etc.), in 
order to better understand which subsets of Aquarius data 
have clearer margins for improvements.  
The clearest margin for improvement was noticed by 
plotting the SSS difference as a function of latitude (see 
Figure 1). The map in Figure 1 shows the colocations 
between Aquarius footprints and the surfacing of Argo floats 
for the entire duration of the mission. The color indicates the 
difference in salinity (SSS_Aquarius – SSS_Argo). The 
bottom plot was obtained by grouping the SSS differences in 
intervals of 5 degrees latitude and then by computing the 
mean and the standard deviation within each interval. The 
blue circles in Figure 1 represent the means and the error bars 
heights indicate ±1 standard deviation.  
Some of the differences at high latitudes are likely to be 
due to low-level RFI (e.g., northeastern Atlantic and near 
Japan [7] ). Attempts to detect these instances have been 
made (e.g., [8] , [9] ), but they have not been implemented for 
the time being. Other differences (north Pacific, Southern 
Ocean) are probably unrelated to RFI and they correspond to 
regions with cold temperatures, low SSS and high winds, 
which are generally considered difficult conditions to retrieve 
SSS using theoretical approaches [10] . The bottom plot of 
https://ntrs.nasa.gov/search.jsp?R=20200000464 2020-03-11T12:39:41+00:00Z
Figure 1 shows that bias is near zero in the range [-50˚, 30˚] 
and that the standard deviations increases south of -40˚ and 
north of 30˚.  
 
 
Figure 1: Difference between SSS_Aquarius and 
SSS_Argo. Map (top) and error bars (bias ± s) as a 
function of latitude (bottom). 
Based on these observations, we apply a NN approach to 
the Aquarius measurements at latitudes greater than 30˚ (in 
both hemispheres) to see whether we can obtain a set of SSS 
that would be more similar to our reference SSS, provided by 
the in-situ measurements of the Argo floats. 
 
3. NEURAL NETWORKS 
 
Three neural networks have been developed, one for each 
Aquarius radiometer. Each NN is a feed-forward NN with 
four layers (an input layer, two hidden layers and an output 
layer) and each layer is dense (every input is connected to 
every output). The input parameters of the NN are: TBs (H- 
and V-pol); sea surface temperature; significant wave height; 
wind speed; weighted fractions of land and sea-ice in the field 
of view; instantaneous rain rate; latitude and longitude. All 
these input parameters are read from the Aquarius data 
products (version 5). The TB used as input is the TB at the 
surface before roughness correction. Other choices of TB are 
also possible.  For example, the NN could be trained to use 
the antenna temperatures or the TB at the “top of ionosphere” 
(i.e., before polarization rotation).  However, using TB earlier 
in the processing chain would require that the number of input 
parameters increase to correct for propagation to the surface 
(e.g., polarization rotation angles, contribution from the 
direct and reflected signals from the galaxy, the sun and the 
moon, and others). The number of neurons in the hidden 
layers is set to 15 in each layer, as above 15 no significant 
improvement was noticed. We also considered different 
options for the activation function (e.g., sigmoid, tanh) and 
found that ReLU (rectified linear units) provided slightly 
better results.  
Training a NN consists in minimizing a cost function by 
adjusting the weights that correspond to the links between 
neurons. The NN used here was trained by minimizing the 
sum of the mean squared errors between the SSS from the NN 
(SSS_NN) and the SSS from Argo (SSS_Argo). The 
algorithm used for the minimization is the “Adam” algorithm 
[11]  and the weights are initialized randomly. The three NNs 
(one for each beam) have the same architecture. 
Each NN was trained using Aquarius version 5 data, 
applying the following criteria: 
• valid match-up with an Argo buoy; 
• |latitude| > 30˚ (except Mediterranean Sea); 
• water fraction (geometric fraction of water in the 
field of view, weighted by the antenna gains) > 99%; 
• only one Aquarius beam per NN. 
The Mediterranean Sea is above 30˚ N; however, the 
typical issues affecting SSS retrievals at high latitudes (high 
winds, low SSS, low temperature) do not generally affect the 
Mediterranean Sea and therefore it was excluded. 
The dataset of Aquarius-Argo colocations is divided into 
three subsets: the training, the validation and the test subsets. 
The first is used to train the NN. The second serves to assess 
the quality of the NN on data not used during training and it 
is used to determine the best architecture for the NN.  The 
third is only used at the end to perform a final evaluation on 
a dataset that neither the NN nor the architects have seen 
before. Training, validation and test subsets correspond to 
70%, 15% and 15% of the entire dataset, respectively, and the 
members are selected randomly. The entire dataset is made 
up of roughly 50,000 Aquarius-Argo colocations. 
 
4. RESULTS 
 
4.1 Comparison between SSS_NN, SSS_Aquarius and 
SSS_Argo  
 
The results for the Aquarius inner beam are shown in Figure 
2 (left), which is a density plot between SSS_NN and 
SSS_Argo and includes only the test subset. 
Figure 2 (left) shows that SSS_NN corresponds well 
with SSS_Argo. However, the NN approach is justified only 
if the comparison between SSS_NN and SSS_Argo is better 
than the comparison between SSS_Aquarius and SSS_Argo. 
The comparison between SSS_Aquarius and SSS_Argo is 
shown in Figure 2 (right). The axes of Figure 2 (right) have 
been set equal to those of Figure 2 (left) so that they can be 
compared.  As a result, there are a few data points with 
SSS_Aquarius below 30 psu and that are not visible in Figure 
2 (right).  
In order to assess these comparisons more quantitively, 
we have considered four statistical metrics: the correlation 
coefficient (Pearson’s r), the bias, the Root-Mean-Square 
Difference (RMSD), and the 5% trimmed range (range from 
the 5th to the 95th percentile). They are reported for all three 
beams in Tables 1 to 3. 
 
 
Figure 2: Comparison on the test set (inner beam) 
between: (left) SSS_NN and SSS_Argo; (right) 
SSS_Aquarius and SSS_Argo 
 
 NN vs Argo Aquarius vs Argo 
r 0.949 0.697 
bias -0.034 -0.099 
RMSD 0.290 0.869 
5th percentile -0.416 -1.103 
95th percentile 0.296 0.812 
5% TR 0.712 1.915 
Table 1: SSS [psu] statistical comparison (inner beam) 
 NN vs Argo Aquarius vs Argo 
r 0.960 0.676 
bias 0.018 -0.093 
RMSD 0.257 0.946 
5th percentile -0.308 -1.074 
95th percentile 0.358 0.804 
5% TR 0.666 1.878 
Table 2: SSS [psu] statistical comparison (middle beam) 
 NN vs Argo Aquarius vs Argo 
r 0.950 0.711 
bias -0.013 -0.096 
RMSD 0.285 0.808 
5th percentile -0.395 -1.118 
95th percentile 0.331 0.802 
5% TR 0.726 1.920 
Table 3: SSS [psu] statistical comparison (outer beam) 
All metrics indicate that SSS_Argo is more similar to 
SSS_NN than it is to SSS_Aquarius.  
 
4.2 Comparison of SSS_NN and SSS_Aquarius with other 
ocean datasets over one month of data 
 
Once the NN for the three beams are defined, they can be 
used to routinely retrieve SSS from Aquarius measurements. 
The results (SSS_NN) are compared with SSS_Aquarius and 
with two other SSS datasets: the model HYCOM (HYbrid 
Coordinate Ocean Model) [12]  and the climatology from the 
Scripps Institution of Oceanography [13] . Figure 3(a and b) 
show that, compared to the SSS from HYCOM and from 
Scripps, the SSS_Aquarius are noisier and with a negative 
bias near regions with strong RFI and near the transition with 
sea ice. On the other hand, the differences between SSS_NN 
and the SSS from HYCOM and Scripps, shown in Figure 3(c 
and d), have a lot less variability, meaning that SSS_NN is 
more consistently similar to both datasets. The regions where 
SSS_NN is most different from those datasets are near the 
outflow of the Uruguay river and off the western coast of 
Canada. These are dynamic regions of the ocean [14] , [15]  
and they correspond also to the largest differences between 
the two models, so that it is difficult to assess whether the 
SSS_NN is accurate there. 
Also note that the Scripps dataset only provides SSS for 
|latitude| < 65˚, so in Figure 3(c, d and e) there is no data at 
the highest latitudes.  
 
5. CONCLUSIONS 
 
While the SSS retrieved from Aquarius are already very close 
to in-situ measurements (in this case, Argo), the level of 
accuracy varies depending on the region and on the state of 
the ocean. To improve the accuracy between Aquarius and 
Argo, a different approach, based on a NN, was developed. 
The results show that, at |latitude| > 30˚, the similarity 
between Aquarius and Argo SSS decreases, and using an 
empirical model such as NN can be a useful alternative. 
 
6. REFERENCES 
 
[1]  D. M. Le Vine, E. P. Dinnat, T. Meissner, F. J. Wentz, H.-
Y. Kao, G. Lagerloef, and T. Lee, “Status of Aquarius and Salinity 
Continuity,” Remote Sensing, vol. 10, no. 10, p. 1585, Oct. 2018. 
 
[2]  Aquarius Project: Algorithm Theoretical Basis 
Document (ATBD), End of mission version, 2017. Available 
online at ftp://podaac-ftp.jpl.nasa.gov/allData/aquarius/docs/ 
v5/AQ-014-PS-0017_Aquarius_ATBD-EndOfMission.pdf. 
Accessed on Decemeber 2018. 
 
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 (a) 
 
(b) 
 
(c) 
 
(d) 
 
(e) 
Figure 3: Difference maps for Jan 2016 (all three beams) 
between: (a) SSS_Aquarius and SSS_HYCOM; (b) 
SSS_Aquarius and SSS_Scripps; (c) SSS_NN and 
SSS_HYCOM; (d) SSS_NN and SSS_Scripps; (e) 
SSS_Scripps and SSS_HYCOM.  
 
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Sea Surface Salinity Retrievals from Aquarius Using Neural Networks

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