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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diagnosing water resource pressure from cropland expansion using multisource remote sensing in Xinjiang, China

XinLong Feng, Weiming Cheng, Ruibo Wang
International Journal of Remote Sensing
Remote Sensing in Agriculture
article

Diagnosing water resource pressure from cropland expansion using multisource remote sensing in Xinjiang, China

XinLong Feng, Weiming Cheng, Ruibo Wang
article en

Abstract

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.

International Journal of Remote Sensing
Chinese Academy of Sciences (CN), Xinjiang University (CN)
Openalex Percentile: Top 19%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon