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Downscaling soil moisture using multi-source data in China

Downscaling soil moisture using multi-source data in China
Downscaling soil moisture using multi-source data in China
Soil moisture plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most soil moisture data from remote sensing ranges from ten to several tens of kilometres whilst those observed in situ and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed soil moisture data needs to be down-scaled. In this paper, a universal soil moisture downscaling model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, soil and topographic parameters from high resolution data, and active/passive microwave remotely sensed soil moisture data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 soil moisture maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The in situ observations were used to validate the down-scaled ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the down-scaled ECV_SM and original one. In general, the down-scaled soil moisture for different land cover and land use types is consistent with the in situ observations. The accuracy is relatively high in autumn and winter. The validation results show that downscaled soil moisture can be improved not only on spatial resolution, but also on estimation accuracy
An, Ru
b1a1658a-20f7-49e1-8da5-3b4e321ce595
Wang, Huilin
862eea88-3d41-49e6-96f9-0ed4629fc919
You, Jiajun
083fc43a-65be-45bd-b073-c1a498571dd2
Wang, Ying
9f99be6c-c16d-4204-9ae8-36343e2349dc
Shen, Xiaoji
7ae97d85-07c4-4016-b2ff-e1ee727982e3
Gao, Wei
106856b9-e61d-4a84-ba0c-9088344260f4
Wang, Yinan
b2e37753-64d9-44ec-9b83-a2018e5f1a96
Zhang, Yu
5be0fec0-2a86-4d01-8014-3466eeb339ff
Wang, Zhe
cd5ba2f9-2c1b-48c7-bd9d-30b8708fd7de
Quaye-Ballardd, Jonathan Arthur
cbe3b095-89ae-4262-87f5-5c081f9d5804
Chen, Yuehong
827c7a53-1f3c-4baa-bf6b-ee4a57a3ee7d
An, Ru, Wang, Huilin, You, Jiajun, Wang, Ying, Shen, Xiaoji, Gao, Wei, Wang, Yinan, Zhang, Yu, Wang, Zhe, Quaye-Ballardd, Jonathan Arthur and Chen, Yuehong (2017) Downscaling soil moisture using multi-source data in China In Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100041Z. 14 pp. (doi:10.1117/12.2241247).

An, Ru, Wang, Huilin, You, Jiajun, Wang, Ying, Shen, Xiaoji, Gao, Wei, Wang, Yinan, Zhang, Yu, Wang, Zhe, Quaye-Ballardd, Jonathan Arthur and Chen, Yuehong (2017) Downscaling soil moisture using multi-source data in China In Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100041Z. 14 pp. (doi:10.1117/12.2241247).

Record type: Conference or Workshop Item (Paper)

Abstract

Soil moisture plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most soil moisture data from remote sensing ranges from ten to several tens of kilometres whilst those observed in situ and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed soil moisture data needs to be down-scaled. In this paper, a universal soil moisture downscaling model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, soil and topographic parameters from high resolution data, and active/passive microwave remotely sensed soil moisture data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 soil moisture maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The in situ observations were used to validate the down-scaled ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the down-scaled ECV_SM and original one. In general, the down-scaled soil moisture for different land cover and land use types is consistent with the in situ observations. The accuracy is relatively high in autumn and winter. The validation results show that downscaled soil moisture can be improved not only on spatial resolution, but also on estimation accuracy

PDF Downscaling soil moisture using multisource data in China - Accepted Manuscript
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Accepted/In Press date: 18 October 2016
e-pub ahead of print date: 18 October 2016
Published date: 6 January 2017
Organisations: Geography & Environment

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Local EPrints ID: 407904
URI: http://eprints.soton.ac.uk/id/eprint/407904
PURE UUID: 42f45364-5091-4370-9f6f-4578a3efdbef

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Date deposited: 28 Apr 2017 01:07
Last modified: 17 Jul 2017 13:53

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Contributors

Author: Ru An
Author: Huilin Wang
Author: Jiajun You
Author: Ying Wang
Author: Xiaoji Shen
Author: Wei Gao
Author: Yinan Wang
Author: Yu Zhang
Author: Zhe Wang
Author: Jonathan Arthur Quaye-Ballardd
Author: Yuehong Chen

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