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

An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018. Chen, Y., Feng, X., & Fu, B. Earth System Science Data, 13(1):1–31, January, 2021.

Paper doi abstract bibtex

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).

@article{chen_improved_2021,
	title = {An improved global remote-sensing-based surface soil moisture ({RSSSM}) dataset covering 2003–2018},
	volume = {13},
	issn = {1866-3516},
	url = {https://essd.copernicus.org/articles/13/1/2021/},
	doi = {10.5194/essd-13-1-2021},
	abstract = {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).},
	language = {en},
	number = {1},
	urldate = {2022-10-26},
	journal = {Earth System Science Data},
	author = {Chen, Yongzhe and Feng, Xiaoming and Fu, Bojie},
	month = jan,
	year = {2021},
	pages = {1--31},
}

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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. 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However, long-term satellite\nmonitoring of surface soil moisture at the global scale needs improvement.\nIn this study, we conducted data calibration and data fusion of 11\nwell-acknowledged microwave remote-sensing soil moisture products since 2003\nthrough a neural network approach, with Soil Moisture Active Passive (SMAP)\nsoil moisture data applied as the primary training target. The training\nefficiency was high (R2=0.95) due to the selection of nine quality\nimpact factors of microwave soil moisture products and the complicated\norganizational structure of multiple neural networks (five rounds of iterative\nsimulations, eight substeps, 67 independent neural networks, and more than 1\nmillion localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering\n2003–2018 at 0.1∘ resolution. The temporal\nresolution is approximately 10 d, meaning that three data records are\nobtained within a month, for days 1–10, 11–20,\nand from the 21st to the last day of that month. RSSSM is proven comparable to the\nin situ surface soil moisture measurements of the International Soil\nMoisture 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\npopular similar products are usually within the ranges of\n0.31–0.41 and 0.095–0.142 m3 m−3),\nrespectively. RSSSM generally presents advantages over other products in\narid and relatively cold areas, which is probably because of the difficulty\nin simulating the impacts of thawing and transient precipitation on soil\nmoisture, and during the growing seasons. Moreover, the persistent high\nquality during 2003–2018 as well as the complete spatial\ncoverage ensure the applicability of RSSSM to studies on both the spatial\nand temporal patterns (e.g. long-term trend). RSSSM data suggest an\nincrease in the global mean surface soil moisture. Moreover, without\nconsidering the deserts and rainforests, the surface soil moisture loss on\nconsecutive rainless days is highest in summer over the low latitudes\n(30∘ S–30∘ N) but mostly in winter over\nthe mid-latitudes (30–60∘ N,\n30–60∘ S). Notably, the error\npropagation is well controlled with the extension of the simulation period\nto the past, indicating that the data fusion algorithm proposed here will be\nmore meaningful in the future when more advanced microwave sensors become\noperational. 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