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

Author

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

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.

Suggested Citation

  • Liu, Yuqi & Wang, Yang & Liao, Yanling & Liao, Renkuan & Šimůnek, Jirka, 2025. "Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms," Agricultural Water Management, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:agiwat:v:308:y:2025:i:c:s0378377425000162
    DOI: 10.1016/j.agwat.2025.109302
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    1. Junxia Wang & Abudukeyimu Abulizi & Yusuyunjiang Mamitimin & Kerim Mamat & Le Yuan & Shaojie Bai & Tingting Yu & Adila Akbar & Xiaofen Zhang & Fang Shen, 2024. "Spatiotemporal Evolution of Habitat Quality and Scenario Modeling Prediction in the Tuha Region," Land, MDPI, vol. 13(7), pages 1-20, July.
    2. Li, Xiaolin & Tong, Ling & Niu, Jun & Kang, Shaozhong & Du, Taisheng & Li, Sien & Ding, Risheng, 2017. "Spatio-temporal distribution of irrigation water productivity and its driving factors for cereal crops in Hexi Corridor, Northwest China," Agricultural Water Management, Elsevier, vol. 179(C), pages 55-63.
    3. van der Kooij, Saskia & Zwarteveen, Margreet & Boesveld, Harm & Kuper, Marcel, 2013. "The efficiency of drip irrigation unpacked," Agricultural Water Management, Elsevier, vol. 123(C), pages 103-110.
    4. Mason, Brooke & Rufí-Salís, Martí & Parada, Felipe & Gabarrell, Xavier & Gruden, Cyndee, 2019. "Intelligent urban irrigation systems: Saving water and maintaining crop yields," Agricultural Water Management, Elsevier, vol. 226(C).
    5. Liu, Yuqi & Wang, Aiwen & Li, Bo & Šimůnek, Jirka & Liao, Renkuan, 2024. "Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize," Agricultural Water Management, Elsevier, vol. 303(C).
    6. George P. Petropoulos & Prashant K. Srivastava & Maria Piles & Simon Pearson, 2018. "Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management," Sustainability, MDPI, vol. 10(1), pages 1-20, January.
    7. Ren, Shoujia & Guo, Bin & Wang, Zhijun & Wang, Juan & Fang, Quanxiao & Wang, Jianlin, 2022. "Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress," Agricultural Water Management, Elsevier, vol. 261(C).
    8. Yang, Danni & Li, Sien & Kang, Shaozhong & Du, Taisheng & Guo, Ping & Mao, Xiaomin & Tong, Ling & Hao, Xinmei & Ding, Risheng & Niu, Jun, 2020. "Effect of drip irrigation on wheat evapotranspiration, soil evaporation and transpiration in Northwest China," Agricultural Water Management, Elsevier, vol. 232(C).
    9. Wang, Yahui & Li, Sien & Qin, Shujing & Guo, Hui & Yang, Danni & Lam, Hon-Ming, 2020. "How can drip irrigation save water and reduce evapotranspiration compared to border irrigation in arid regions in northwest China," Agricultural Water Management, Elsevier, vol. 239(C).
    10. C. Dionisio Pérez-Blanco & Arthur Hrast-Essenfelder & Chris Perry, 2020. "Irrigation Technology and Water Conservation: A Review of the Theory and Evidence," Review of Environmental Economics and Policy, University of Chicago Press, vol. 14(2), pages 216-239.
    11. Guan, Changkun & Zhu, Xueqin & Heerink, Nico & Ma, Xianlei & Shi, Xiaoping, 2024. "Why do farmers stop using collective drip irrigation technology? Evidence from rural Xinjiang, China," Agricultural Water Management, Elsevier, vol. 302(C).
    12. Zou, Xiaoxia & Li, Yu’e & Cremades, Roger & Gao, Qingzhu & Wan, Yunfan & Qin, Xiaobo, 2013. "Cost-effectiveness analysis of water-saving irrigation technologies based on climate change response: A case study of China," Agricultural Water Management, Elsevier, vol. 129(C), pages 9-20.
    13. Shilong Piao & Philippe Ciais & Yao Huang & Zehao Shen & Shushi Peng & Junsheng Li & Liping Zhou & Hongyan Liu & Yuecun Ma & Yihui Ding & Pierre Friedlingstein & Chunzhen Liu & Kun Tan & Yongqiang Yu , 2010. "The impacts of climate change on water resources and agriculture in China," Nature, Nature, vol. 467(7311), pages 43-51, September.
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