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Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. / Pradhan , Ankita; Nair, Akhilesh S.; Indu, J.; Makarieva, Olga ; Nesterova , Nataliia .

In: Remote Sensing, Vol. 15, No. 6, 1532, 10.03.2023.

Research output: Contribution to journal › Article › peer-review

Harvard

Pradhan , A, Nair, AS, Indu, J, Makarieva, O & Nesterova , N 2023, 'Leveraging Soil Moisture Assimilation in Permafrost Affected Regions', Remote Sensing, vol. 15, no. 6, 1532. https://doi.org/10.3390/rs15061532

APA

Pradhan , A., Nair, A. S., Indu, J., Makarieva, O., & Nesterova , N. (2023). Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sensing, 15(6), [1532]. https://doi.org/10.3390/rs15061532

Vancouver

Pradhan A, Nair AS, Indu J, Makarieva O, Nesterova N. Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sensing. 2023 Mar 10;15(6). 1532. https://doi.org/10.3390/rs15061532

Author

Pradhan , Ankita ; Nair, Akhilesh S. ; Indu, J. ; Makarieva, Olga ; Nesterova , Nataliia . / Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. In: Remote Sensing. 2023 ; Vol. 15, No. 6.

BibTeX

@article{9cb37aa19c9e4c82b9ff28e69cdd1e8f,
title = "Leveraging Soil Moisture Assimilation in Permafrost Affected Regions",
abstract = "The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study{\textquoteright}s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.",
keywords = "permafrost, soil moisture, precipitation, streamflow, assimilation, Noah LSM",
author = "Ankita Pradhan and Nair, {Akhilesh S.} and J. Indu and Olga Makarieva and Nataliia Nesterova",
note = "Pradhan, A.; Nair, A.S.; Indu, J.; Makarieva, O.; Nesterova, N. Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sens. 2023, 15, 1532. https://doi.org/10.3390/rs15061532",
year = "2023",
month = mar,
day = "10",
doi = "10.3390/rs15061532",
language = "English",
volume = "15",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI AG",
number = "6",

}

RIS

TY - JOUR

T1 - Leveraging Soil Moisture Assimilation in Permafrost Affected Regions

AU - Pradhan , Ankita

AU - Nair, Akhilesh S.

AU - Indu, J.

AU - Makarieva, Olga

AU - Nesterova , Nataliia

N1 - Pradhan, A.; Nair, A.S.; Indu, J.; Makarieva, O.; Nesterova, N. Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sens. 2023, 15, 1532. https://doi.org/10.3390/rs15061532

PY - 2023/3/10

Y1 - 2023/3/10

N2 - The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.

AB - The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.

KW - permafrost

KW - soil moisture

KW - precipitation

KW - streamflow

KW - assimilation

KW - Noah LSM

UR - https://www.mendeley.com/catalogue/3010e7fb-289a-3834-bab4-99f8752e4753/

U2 - 10.3390/rs15061532

DO - 10.3390/rs15061532

M3 - Article

VL - 15

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 6

M1 - 1532

ER -

ID: 104879140