Abstract. Soil moisture is an important variable linking the
atmosphere and terrestrial ecosystems. However, long-term satellite
monitoring of surface soil moisture at the global scale needs improvement.
In this study, we conducted data calibration and data fusion of 11
well-acknowledged microwave remote-sensing soil moisture products since 2003
through a neural network approach, with Soil Moisture Active Passive (SMAP)
soil moisture data applied as the primary training target. The training
efficiency was high (R2=0.95) due to the selection of nine quality
impact factors of microwave soil moisture products and the complicated
organizational structure of multiple neural networks (five rounds of iterative
simulations, eight substeps, 67 independent neural networks, and more than 1
million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering
2003–2018 at 0.1∘ resolution. The temporal
resolution is approximately 10 d, meaning that three data records are
obtained within a month, for days 1–10, 11–20,
and from the 21st to the last day of that month. RSSSM is proven comparable to the
in situ surface soil moisture measurements of the International Soil
Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing
popular similar products are usually within the ranges of
0.31–0.41 and 0.095–0.142 m3 m−3),
respectively. RSSSM generally presents advantages over other products in
arid and relatively cold areas, which is probably because of the difficulty
in simulating the impacts of thawing and transient precipitation on soil
moisture, and during the growing seasons. Moreover, the persistent high
quality during 2003–2018 as well as the complete spatial
coverage ensure the applicability of RSSSM to studies on both the spatial
and temporal patterns (e.g. long-term trend). RSSSM data suggest an
increase in the global mean surface soil moisture. Moreover, without
considering the deserts and rainforests, the surface soil moisture loss on
consecutive rainless days is highest in summer over the low latitudes
(30∘ S–30∘ N) but mostly in winter over
the mid-latitudes (30–60∘ N,
30–60∘ S). Notably, the error
propagation is well controlled with the extension of the simulation period
to the past, indicating that the data fusion algorithm proposed here will be
more meaningful in the future when more advanced microwave sensors become
operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).
Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals
