<p class="first" id="P1">A Neural Network (NN) algorithm was developed to estimate
global surface soil moisture
for April 2015 to March 2017 with a 2–3 day repeat frequency using passive microwave
observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil
temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5)
land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation
water content. The NN was trained on GEOS-5 soil moisture target data, making the
NN estimates consistent with the GEOS-5 climatology, such that they may ultimately
be assimilated into this model without further bias correction. Evaluated against
in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE),
correlation and anomaly correlation of the NN retrievals were 0.037 m
<sup>3</sup>m
<sup>−3</sup>, 0.70 and 0.66, respectively, against SMAP core validation site measurements
and
0.026 m
<sup>3</sup>m
<sup>−3</sup>, 0.58 and 0.48, respectively, against International Soil Moisture Network
(ISMN)
measurements. At the core validation sites, the NN retrievals have a significantly
higher skill than the GEOS-5 model estimates and a slightly lower correlation skill
than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was
reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill
when ancillary parameters in physically-based retrievals were uncertain. Against ISMN
measurements, the skill of the two retrieval products was more comparable. A triple
collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and
Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P
retrieval errors have a similar spatial distribution, but the NN retrieval errors
are generally lower in densely vegetated regions and transition zones.
</p>