Diagnosing water resource pressure from cropland expansion using multisource remote sensing in Xinjiang, China
Cropland expansion in arid oases can increase pressure on limited water resources. However, conventional land-cover analyses cannot show whether newly cultivated land develops an irrigation-associated surface response or whether the expansion takes place under strong water constraints. Here, we developed a multisource remote-sensing framework to diagnose the water-resource pressure associated with cropland expansion in Xinjiang, China, from 2015 to 2024. We first used annual cropland trajectories to identify persistent new cropland. We then combined Landsat-derived changes in the oasis cooling effect (OCE) with post-conversion cropland persistence to construct an Irrigation-related Diagnostic Index (IDI), and we evaluated its spatial consistency with the independent IrriMap_CN irrigation dataset. Finally, we combined the new-cropland proportion, IDI and hydroclimatic water constraint into a 1 km Cropland Expansion Water-Resource Indicator (CEWRI). Cropland area increased from 58,823.5 km2 in 2015 to 78,184.2 km2 in 2024, a net increase of 32.91%, and most new cropland was located along the margins of existing oases. After conversion, the OCE of newly cultivated land increased by 2.93°C on average. IDI was positively correlated with independently mapped irrigation: Spearman’s ρ increased from 0.463 at the 1 km scale to 0.786 after aggregation to 20 km blocks. Its ability to distinguish irrigated from non-irrigated cells in 2019 was moderate (area under the receiver operating characteristic curve, AUC = 0.701). The high and very high CEWRI classes contained 57.64% of the persistent new-cropland area. This result indicates that expansion-related water-resource pressure was concentrated where cropland expansion, irrigation-associated thermal response and water constraint occurred together. The framework is a regional screening tool: it identifies priority areas where irrigation withdrawals and hydrological risk should be assessed in more detail.
Authors
- XinLong Feng
- Weiming Cheng (ORCID: https://orcid.org/0000-0003-1580-4979)
- Ruibo Wang
Institutions
- Chinese Academy of Sciences (CN)
- Xinjiang University (CN)
Publication Details
- Journal
- International Journal of Remote Sensing
- Published
- 2026-11-04
- DOI
- https://doi.org/10.1080/01431161.2026.2758254
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00