IEEE Geoscience and Remote Sensing Magazine - December 2022 - 262

ECMWF, with a 30-km resolution]; the red and near-infrared
bands of multispectral images or the normalized difference
vegetation index from, e.g., the NASA Moderate Resolution
Imaging Spectroradiometer, at 9- and 36-km spatial resolutions,
aggregating to an original 1-km spatial resolution
product; and the FMPL-2 LHCP L-band BTs from the nadirlooking
antenna. The ANN was trained with 60% of the data
to avoid overfitting, using the 36-km NASA Soil Moisture Active
Passive mission SM product, and the training was interrupted
after 20 epochs.
Figure 3(a) provides a sample SM map produced over a week.
Figure 3(b) displays the scatter plot of the soil SMAP moisture
values and the FMPL-2/ANN-retrieved ones. The R2 coefficient
was 0.8, and the standard deviation of the error with respect to
the SMAP product was 9.2%, which was +2-2.5 times larger
than the +4% accuracy quoted for SMOS SM products. The interested
reader is referred to [17] for further details.
SEA ICE CONCENTRATION/EXTENT
Sea ice concentration (SIC) maps were generated using
L-band microwave radiometry and/or GNSS-R data. Two
regression-fit ANNs were used for each data source, with
three hidden layers of five, ten, and five neurons each and
five and eight input variables, respectively. The L-band
microwave radiometry ANN was trained using OSI SAF
[18] sea ice cover or the National Snow and Ice Data Center
ground truth as a target output, and FMPL-2 BTs, the
along-track standard deviation and gradient of the BTs,
the ERA 5 skin temperature from ECMWF, and the land
cover fraction (as a percentage) were used as input variables.
For the GNSS-R, the inputs were the FMPL-2 BTs,
averaged DDM, reflectivity, standard deviation of the reflectivity,
DDM signal-to-noise ratio, elevation angle of
the reflected signal, land cover fraction within an area
larger than the antenna footprint, and ERA 5 skin temperature.
All variables were bilinearly interpolated to the
GNSS-R specular reflection points, and the FMPL-2 BTs
were needed to improve the performance of the network.
Both networks were trained using +15,000 random samples
for each pole, and they were validated using +5,000
random samples for each pole.
SIE maps were generated using a binary classification
ANN with only one hidden layer with 10 neurons.
Figure 4 gives sample SIE and SIC maps for the
(a)
(b)
(c)
(d)
FIGURE 1. The (a) FMPL-2 combined microwave radiometer and GNSS-R, (b) 3Cat-5/A (NORAD ID: 46292) and dual-band nadir-looking
antenna, (c) HyperScout-2, and (d) 3Cat-5/B (NORAD ID: 46293), with the HyperScout-2 baffle at the bottom-right corner.
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