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Global Soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm

Mohseni, Farzane LU ; Jamali, Sadegh LU orcid ; Ghorbanian, Arsalan LU and Mokhtarzade, Mehdi (2023) In Global and Planetary Change 231.
Abstract
The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global... (More)
The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and nonlinear trends were observed in the global land areas that have experienced statistically significant changes. The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20% agreement, demonstrating the robustness and reliability of the observed trends. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Global and Planetary Change
volume
231
article number
104310
pages
12 pages
publisher
Elsevier
external identifiers
  • scopus:85178656352
ISSN
1872-6364
DOI
10.1016/j.gloplacha.2023.104310
language
English
LU publication?
yes
id
12a8b77b-ec2b-4cf9-810f-1a5e71b0311a
date added to LUP
2023-11-27 13:18:57
date last changed
2024-01-04 10:37:33
@article{12a8b77b-ec2b-4cf9-810f-1a5e71b0311a,
  abstract     = {{The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and nonlinear trends were observed in the global land areas that have experienced statistically significant changes. The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20% agreement, demonstrating the robustness and reliability of the observed trends.}},
  author       = {{Mohseni, Farzane and Jamali, Sadegh and Ghorbanian, Arsalan and Mokhtarzade, Mehdi}},
  issn         = {{1872-6364}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Global and Planetary Change}},
  title        = {{Global Soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm}},
  url          = {{http://dx.doi.org/10.1016/j.gloplacha.2023.104310}},
  doi          = {{10.1016/j.gloplacha.2023.104310}},
  volume       = {{231}},
  year         = {{2023}},
}