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

Abstract

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.