Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy

Determining high-resolution irrigation water use (IWU) is essential for agricultural water management. However, current remote sensing and statistics-based approaches suffer from either a challenge of coarse spatial resolution or insufficient irrigation details. Given that IWU estimation is fundamentally dependent on soil moisture (SM) dynamics in accordance with the water balance principle, we have proposed a framework to determine the IWU amount using multiple SM observations at kilometer scale across the entire irrigated farmlands throughout China. This framework specifically focuses on investigating SM dynamics at which depth is optimal for estimating IWU, considering that irrigation water infiltration leads to asynchronous SM variations across soil column. Moreover, an optimal fusion strategy was implemented to mitigate the possible error of a single SM data on the determination of the individual IWU amount estimation. The proposed framework exhibits the best IWU estimates when considering SM dynamics at the top layer (0–10 cm), with a correlation coefficient (R) of 0.79 and an unbiased root mean square error (ubRMSE) of 4.14 km 3 /year compared with statistics collected from the China Water Resources Bulletin over a period of 10 years (2010–2019). This result demonstrates the feasibility of using satellite SM products to estimate the IWU amount. An optimal fusion strategy incorporating multiple satellite-based fine resolution SM products shows improved IWU estimates with an ubRMSE decrease of approximately 10 % (3.72 km 3 /year), indicating the effectiveness of multiple satellite SM datasets in reducing the uncertainty associated with IWU estimation. • A framework was proposed to determine irrigation water use from multiple soil moisture data fusion. • Surface soil moisture dynamics can be used to estimate irrigation water use. • An assessment covering 10 years in China yields an ubRMSE of 3.72 km 3 /year in irrigation estimates.

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Publication Details

Journal
Agricultural Water Management
Published
2026-02-19
DOI
https://doi.org/10.1016/j.agwat.2026.110232
Primary Topic
Soil Moisture and Remote Sensing
Type
article
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article

Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy

Yun-Jing Geng, Shixian Zhai, Qiu-Yu Yan, Abba Aliyu Kasim et al.
Agricultural Water Management
Soil Moisture and Remote Sensing
article

Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy

Yun-Jing Geng, Shixian Zhai, Qiu-Yu Yan, Abba Aliyu Kasim, Jian-Wei Ma, Seokhyeon Kim, Yayong Sun, Chunfeng Ma, Pei Leng, Zhao-Liang Li, Shiyuan Fu, Xiaoning Song, Tian Ma, Qian-Yu Liao, Muhannad Adnan Siddique
article en

Abstract

Determining high-resolution irrigation water use (IWU) is essential for agricultural water management. However, current remote sensing and statistics-based approaches suffer from either a challenge of coarse spatial resolution or insufficient irrigation details. Given that IWU estimation is fundamentally dependent on soil moisture (SM) dynamics in accordance with the water balance principle, we have proposed a framework to determine the IWU amount using multiple SM observations at kilometer scale across the entire irrigated farmlands throughout China. This framework specifically focuses on investigating SM dynamics at which depth is optimal for estimating IWU, considering that irrigation water infiltration leads to asynchronous SM variations across soil column. Moreover, an optimal fusion strategy was implemented to mitigate the possible error of a single SM data on the determination of the individual IWU amount estimation. The proposed framework exhibits the best IWU estimates when considering SM dynamics at the top layer (0–10 cm), with a correlation coefficient (R) of 0.79 and an unbiased root mean square error (ubRMSE) of 4.14 km 3 /year compared with statistics collected from the China Water Resources Bulletin over a period of 10 years (2010–2019). This result demonstrates the feasibility of using satellite SM products to estimate the IWU amount. An optimal fusion strategy incorporating multiple satellite-based fine resolution SM products shows improved IWU estimates with an ubRMSE decrease of approximately 10 % (3.72 km 3 /year), indicating the effectiveness of multiple satellite SM datasets in reducing the uncertainty associated with IWU estimation. • A framework was proposed to determine irrigation water use from multiple soil moisture data fusion. • Surface soil moisture dynamics can be used to estimate irrigation water use. • An assessment covering 10 years in China yields an ubRMSE of 3.72 km 3 /year in irrigation estimates.

Agricultural Water ManagementVol. 326
Federal University of Agriculture, Abeokuta (NG), Chinese Academy of Sciences (CN), Korea Institute of Civil Engineering and Building Technology (KR), China Institute of Water Resources and Hydropower Research (CN), Northwest Institute of Eco-Environment and Resources (CN), Institute of Agricultural Resources and Regional Planning (CN), Center For Remote Sensing (United States) (US), University of Chinese Academy of Sciences (CN), Istanbul Technical University (TR), Korea Institute of Science and Technology (KR)
Clean water and sanitation
Openalex Percentile: Top 58%
Soil Moisture and Remote Sensing
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