Articles | Volume 13, issue 1
https://doi.org/10.5194/essd-13-1-2021
https://doi.org/10.5194/essd-13-1-2021
Data description paper
 | 
05 Jan 2021
Data description paper |  | 05 Jan 2021

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

Yongzhe Chen, Xiaoming Feng, and Bojie Fu

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Cited articles

Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008. 
Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., and Wagner, W.: Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations, Remote Sens. Environ., 118, 215–226, https://doi.org/10.1016/j.rse.2011.11.017, 2012. 
Albergel, C., Dorigo, W., Reichle, R. H., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., and Wagner, W.: Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing, J. Hydrometeorol., 14, 1259–1277, https://doi.org/10.1175/JHM-D-12-0161.1, 2013. 
Al-Yaari, A., Wigneron, J. P., Ducharne, A., Kerr, Y. H., Wagner, W., De Lannoy, G., Reichle, R., Al Bitar, A., Dorigo, W., Richaume, P., and Mialon, A.: Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land), Remote Sens. Environ., 152, 614–626, https://doi.org/10.1016/j.rse.2014.07.013, 2014. 
Al-Yaari, A., Wigneron, J. P., Kerr, Y., de Jeu, R., Rodriguez-Fernandez, N., van der Schalie, R., Al Bitar, A., Mialon, A., Richaume, P., Dolman, A., and Ducharne, A.: Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations, Remote Sens. Environ., 180, 453–464, https://doi.org/10.1016/j.rse.2015.11.022, 2016. 
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Short summary
Soil moisture can greatly influence the ecosystem but is hard to monitor at the global scale. By calibrating and combining 11 different products derived from satellite observation, we developed a new global surface soil moisture dataset spanning from 2003 to 2018 with high accuracy. Using this new dataset, not only can the global long-term trends be derived, but also the seasonal variation and spatial distribution of surface soil moisture at different latitudes can be better studied.
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