Advancing sustainable agricultural: A novel framework for mapping annual 30-m resolution national-level irrigated croplands

• New high accuracy sample extraction method via dynamic threshold and data integration. • Comprehensive multi-feature set highly sensitive to irrigation’s spatial variability. • Enhanced irrigated cropland dataset with spatiotemporal continuity and consistency. • Analyses of spatiotemporal patterns and drivers of irrigated croplands. With regard to climate change and population growth, irrigated croplands need to be accurately delineated for sustainable water resource management. Owing to the lack of extensive training samples and the limitations of coarse spatiotemporal resolution data in complex agricultural regions, China’s irrigated croplands are difficult to map with a unified spatiotemporal framework. This study presents an innovative method for mapping irrigated and rainfed croplands in mainland China with a local adaptive random forest classifier on the Google Earth Engine platform. Based on the dynamic threshold extraction of multiple peak vegetation index values and a rigorous multi-dataset integration strategy, the annual sample sets of irrigated and rainfed croplands are generated automatically. After constructing 147 multi-feature variables sensitive to irrigation activities, China’s annual irrigated croplands dataset (CAICD) is developed, with 30-m spatial resolution for the 1990–2022 period. The results show the following:(1) CAICD has higher accuracy and a more realistic spatial distribution of irrigated croplands compared with existing datasets, with an average overall accuracy of 0.80. (2) The most sensitive classification features for irrigation signals are spectral indices and original bands, with regional differences influenced by climate characteristics (precipitation and evapotranspiration) and terrain features. (3) Over the past three decades, China’s irrigated croplands have expanded overall and Xinjiang has exhibited the most significant increase and the highest growth rate of irrigated area in mainland China, with an annual expansion of 103 thousand hectares. The results exhibit significant implications for the balance between food security and water resource security, providing valuable insights and contributions for future global monitoring of irrigated croplands.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-05-15
DOI
https://doi.org/10.1016/j.jag.2025.104589
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72

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article

Advancing sustainable agricultural: A novel framework for mapping annual 30-m resolution national-level irrigated croplands

Enyu Du, Lei Wang, Huicong Jia, Jinwei Dong et al.
1 citations
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing in Agriculture
1.72
article

Advancing sustainable agricultural: A novel framework for mapping annual 30-m resolution national-level irrigated croplands

Enyu Du, Lei Wang, Huicong Jia, Jinwei Dong, Yu Chen
article en
1 citations

Abstract

• New high accuracy sample extraction method via dynamic threshold and data integration. • Comprehensive multi-feature set highly sensitive to irrigation’s spatial variability. • Enhanced irrigated cropland dataset with spatiotemporal continuity and consistency. • Analyses of spatiotemporal patterns and drivers of irrigated croplands. With regard to climate change and population growth, irrigated croplands need to be accurately delineated for sustainable water resource management. Owing to the lack of extensive training samples and the limitations of coarse spatiotemporal resolution data in complex agricultural regions, China’s irrigated croplands are difficult to map with a unified spatiotemporal framework. This study presents an innovative method for mapping irrigated and rainfed croplands in mainland China with a local adaptive random forest classifier on the Google Earth Engine platform. Based on the dynamic threshold extraction of multiple peak vegetation index values and a rigorous multi-dataset integration strategy, the annual sample sets of irrigated and rainfed croplands are generated automatically. After constructing 147 multi-feature variables sensitive to irrigation activities, China’s annual irrigated croplands dataset (CAICD) is developed, with 30-m spatial resolution for the 1990–2022 period. The results show the following:(1) CAICD has higher accuracy and a more realistic spatial distribution of irrigated croplands compared with existing datasets, with an average overall accuracy of 0.80. (2) The most sensitive classification features for irrigation signals are spectral indices and original bands, with regional differences influenced by climate characteristics (precipitation and evapotranspiration) and terrain features. (3) Over the past three decades, China’s irrigated croplands have expanded overall and Xinjiang has exhibited the most significant increase and the highest growth rate of irrigated area in mainland China, with an annual expansion of 103 thousand hectares. The results exhibit significant implications for the balance between food security and water resource security, providing valuable insights and contributions for future global monitoring of irrigated croplands.

International Journal of Applied Earth Observation and GeoinformationVol. 140
Chinese Academy of Sciences (CN), Beijing Institute of Big Data Research (CN), Aerospace Information Research Institute (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Ministry of Science and Technology of the People's Republic of China, National Key Research and Development Program of China
Zero hunger
Openalex Percentile: Top 17%
Remote Sensing in Agriculture
1.72
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