Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms

Yuqi Liu, Yang Wang, Yanling Liao, Renkuan Liao and Jirka Šimůnek

Agricultural Water Management, 2025, vol. 308, issue C

Abstract: Conducting field investigations of farmland irrigation patterns on a large scale is a time-consuming and labor-intensive task. The traditional approach of employing satellite remote sensing for large-scale visual assessments is impractical for identifying irrigation patterns due to interference caused by vegetation cover. To address this, we utilized the Google Earth Engine (GEE) platform, integrating environmental covariates and machine learning algorithms, to generate distribution maps of irrigation patterns (micro-irrigation and surface irrigation) at a 30-meter resolution for the Turpan-Hami Basin. Results demonstrate that the Classification and Regression Tree (CART) model achieved a classification accuracy of 0.81, effectively distinguishing between different irrigation patterns. The analysis of feature importance determined NDVI (i.e., Normalized Difference Vegetation Index), EVI (i.e., Enhanced Vegetation Index), MSI (i.e., Moisture Stress Index), Ec (i.e., Transpiration), and NDWI (i.e., Normalized Difference Water Index) as the key indicators linked to irrigation patterns. Regional mapping findings reveal an increase in the proportion of micro-irrigation from 40.2 % in 2015 to 47.0 % in 2023, underscoring the successful implementation of water-saving practices in the Turpan-Hami Basin. Additionally, we developed a GEE-based interactive interface, which enables users to generate corresponding distribution maps of irrigation patterns by selecting a specific year, offering uesful data support for policymakers and farmers to better manage agricultural water resources.

Keywords: Agricultural water use; Remote sensing; Irrigation management; Supervised classification; Google Earth Engine (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377425000162
Full text for ScienceDirect subscribers only

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:eee:agiwat:v:308:y:2025:i:c:s0378377425000162

DOI: 10.1016/j.agwat.2025.109302

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().