Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm

Lixiran Yu, Hong Xie, Yan Xu, Qiao Li, Youwei Jiang, Hongfei Tao () and Mahemujiang Aihemaiti
Additional contact information
Lixiran Yu: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Hong Xie: Changji Water Conservancy Management Station (Santunhe River Basin Management Office), Changji 831100, China
Yan Xu: Xinjiang Cold and Arid Zone Water Resources and Ecological Hydraulic Engineering Research Center (Academician Expert Workstation), Urumqi 830052, China
Qiao Li: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Youwei Jiang: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Hongfei Tao: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Mahemujiang Aihemaiti: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2024, vol. 14, issue 10, 1-23

Abstract: Accurate monitoring of irrigation areas is of great significance to ensure national food security and rational utilization of water resources. The low resolution of the Moderate Resolution Imaging Spectroradiometer and Landsat data makes the monitoring accuracy insufficient for actual demand. Thus, this paper proposes a method of extracting the irrigated area in arid regions based on Sentinel-2 long time-series imagery to realize the accurate monitoring of irrigation areas. In this paper, a typical irrigation area in the arid region of Northwest China–Xinjiang Santun River is selected as the study area. The long time series Sentinel-2 remote sensing data are used to classify the land use of the irrigation area. The random forest, CART decision tree, and support vector machine algorithms are used to combine the field collection of the typical irrigation point and non-irrigated sample points. The irrigation area is extracted by calculating the Normalized Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) time series data as the classification parameters. The results show that (1) the irrigated area of the dryland irrigation region can be effectively extracted using the SAVI time-series data through an object-oriented approach combined with the random forest algorithm. (2) The extracted irrigated areas were 44,417, 42,915, 43,411, 48,908, and 47,900 hm 2 from 2019 to 2023, and the overall accuracies of the confusion matrix validation were 94.34%, 90.22%, 92.03%, 93.23%, and 94.63%, with kappa coefficients of 0.9011, 0.8887, 0.8967, 0.9009, and 0.9265, respectively. The errors of the irrigated area compared with the statistical data were all within 5%, which demonstrated the effectiveness of the method in extracting the irrigated area. This method provides a reference for extracting irrigated areas in arid zones.

Keywords: Sentinel-2A; random forest; land use classification; object-oriented; irrigated area (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/10/1693/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/10/1693/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:10:p:1693-:d:1487012

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().