A 25-year assessment of soil salinity in an arid oasis: Spatiotemporal validation and SHAP attribution
Year: 2026
Type: article
Abstract: Long-term soil salinity mapping in arid oases is vulnerable to spatial dependence and interannual shifts, yet transfer to unsampled areas and years is rarely tested explicitly. We combined 1107 quality-controlled topsoil observations from the Yanqi Basin, China (2000, 2005, 2015, 2020, and 2025), with 30 environmental covariates. Ten algorithms and five input strategies were compared using site-grouped five-fold, 15-km spatial-block five-fold, and leave-one-year-out (LOYO) validation. Extremely Randomized Trees (ERT) provided the most stable overall performance (R 2 = 0.602, 0.597, and 0.501), whereas Transformer performed best under LOYO (R 2 = 0.523), showing that algorithm rankings depended on the transfer scenario. PCC, VIP, GRA, and GBM feature selection produced no consistent gains; ERT fitted with all 30 predictors was therefore used for mapping. Within the common valid support, the area with SSC ≥ 6 g kg −1 increased from 23.65% in 2000 to 36.41% in 2005, declined to 22.40% in 2015, and rose to 33.88% in 2025, indicating an expansion–relief–reintensification pattern. ERT-SHAP identified SI3, SI1, and Int1 as the leading predictive indicators, while groundwater depth added little independent information after the other covariates were included. ERT systematically underestimated SSC ≥ 10 g kg −1 (spatial-block bias, −4.426 g kg −1 ), requiring caution at the upper end of the observed range. The framework supports reproducible regional comparison and potential hotspot screening by combining spatiotemporal transfer tests, high-value diagnostics, attribution, and long-term mapping.
Source: Ecological Indicators
Institution: Xinjiang Agricultural University
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $3,650
OpenAlex ID: W7212046661