A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.

Nguyen, TT; Ngo, HH; Guo, W; Chang, SW; Nguyen, DD; Nguyen, CT; Zhang, J; Liang, S; Bui, XT; Hoang, NB

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.

Nguyen, TT Ngo, HH Guo, W

Chang, SW Nguyen, DD Nguyen, CT Zhang, J Liang, S Bui, XT Hoang, NB

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Sci Total Environ, 2022, 833, pp. 155066
Issue Date:: 2022-08-10

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Field	Value	Language
dc.contributor.author	Nguyen, TT
dc.contributor.author	Ngo, HH
dc.contributor.author	Guo, W https://orcid.org/0000-0001-5542-2858
dc.contributor.author	Chang, SW
dc.contributor.author	Nguyen, DD
dc.contributor.author	Nguyen, CT
dc.contributor.author	Zhang, J
dc.contributor.author	Liang, S
dc.contributor.author	Bui, XT
dc.contributor.author	Hoang, NB
dc.date.accessioned	2023-01-26T06:23:03Z
dc.date.available	2022-04-02
dc.date.available	2023-01-26T06:23:03Z
dc.date.issued	2022-08-10
dc.identifier.citation	Sci Total Environ, 2022, 833, pp. 155066
dc.identifier.issn	0048-9697
dc.identifier.issn	1879-1026
dc.identifier.uri	http://hdl.handle.net/10453/165478
dc.description.abstract	A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER
dc.relation.ispartof	Sci Total Environ
dc.relation.isbasedon	10.1016/j.scitotenv.2022.155066
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Environmental Sciences
dc.subject.mesh	Algorithms
dc.subject.mesh	Machine Learning
dc.subject.mesh	Radar
dc.subject.mesh	Soil
dc.subject.mesh	Water
dc.subject.mesh	Water
dc.subject.mesh	Soil
dc.subject.mesh	Algorithms
dc.subject.mesh	Radar
dc.subject.mesh	Machine Learning
dc.title	A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.
dc.type	Journal Article
utslib.citation.volume	833
utslib.location.activity	Netherlands
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-08-10T00:00:00+1000Z
dc.date.updated	2023-01-26T06:23:02Z
pubs.publication-status	Published
pubs.volume	833

Abstract:

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

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http://hdl.handle.net/10453/165478