weight of the
BP
neural network and provides a considerably
effective method to obtain surface soil moisture. Comparing
with other inversion methods, the
MPSO
-
BP
method produces
the highest accuracy with
R
2
of 72.2% and
RMSE
of 0.033 cm
3
/
cm
3
. It is shown that the
MPSO
algorithm is feasible for
BP
neu-
ral network optimization, which can effectively establish the
relationship between backscatter coefficient and soil moisture
content. In addition, the
MPSO
-
BP
algorithm uses optical and
radar data as inputs to collaboratively retrieve the surface soil
moisture under vegetation-covered area. The result is com-
pared with the methods only using a single data source of op-
tical data or radar data as input. The accuracy of soil moisture
retrieval using optical and radar data based on
MPSO
-
BP
algo-
rithm is much higher than that using only a single data source
as input. The
R
2
is 0.827 and the
RMSE
is 0.029 cm
3
/cm
3
.
In this study, some errors in soil moisture retrieval include
measuring uncertainty of ground data (especially surface
roughness), matching and processing error of radar and opti-
cal images. It is necessary to introduce variables that can
separate the scattering information of bare soil and vegeta-
tion. The subsequent work needs to obtain multitemporal and
multisource data to further verify the algorithm.
Acknowledgments
This work is supported by National Key Research and De-
velopment Program of China (2017YFA0603701), Chinese
Natural Science Foundation Project (41901278) and Science
and Technology Project of Jiangsu Province (BK20180798).
It is also supported by the State Scholarship Fund of China.
Thanks to the authors and reviewers who contributed to the
manuscript.
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