An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

• Main Authors: Y. Chen, X. Feng, B. Fu
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-01-01
• Series: Earth System Science Data
• Online Access: https://essd.copernicus.org/articles/13/1/2021/essd-13-1-2021.pdf

<p>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 (<span class="inline-formula"><i>R</i>²=0.95</span>) 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<span class="inline-formula">^∘</span> 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 <span class="inline-formula"><i>R</i>²</span> and RMSE values of 0.42 and 0.087 <span class="inline-formula">m³ m⁻³</span>), while the overall <span class="inline-formula"><i>R</i>²</span> and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 <span class="inline-formula">m³ m⁻³</span>), 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<span class="inline-formula">^∘</span> S–30<span class="inline-formula">^∘</span> N) but mostly in winter over the mid-latitudes (30–60<span class="inline-formula">^∘</span> N, 30–60<span class="inline-formula">^∘</span> 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 <a href="https://doi.org/10.1594/PANGAEA.912597">https://doi.org/10.1594/PANGAEA.912597</a> (Chen, 2020).</p>