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干旱区地理 ›› 2026, Vol. 49 ›› Issue (2): 287-300.doi: 10.12118/j.issn.1000-6060.2025.064 cstr: 32274.14.ALG2025064

• 生态与环境 • 上一篇    下一篇

基于哨兵数据与特征空间模型的新疆渭库绿洲土壤盐渍化遥感反演

尼格拉·塔什甫拉提1,2(), 马莹轩3, 阿不都外力·热合曼1,2, 杨磊1,2   

  1. 1.新疆大学地理与遥感科学学院,新疆 乌鲁木齐 830046
    2.新疆绿洲生态重点实验室,新疆 乌鲁木齐 830046
    3.中国气象局气象干部培训学院新疆分院,新疆 乌鲁木齐 830013
  • 收稿日期:2025-02-13 修回日期:2025-03-19 出版日期:2026-02-25 发布日期:2026-02-27
  • 作者简介:尼格拉·塔什甫拉提(1985-),女,博士,副教授,研究生导师,主要从事干旱区资源环境遥感研究. E-mail: ngr.t@xju.edu.cn
  • 基金资助:
    国家自然科学基金项目(41761077);中国气象局气象干部培训学院科研项目(2024CMATCQN14)

Remote sensing inversion of soil salinization in the Weiku Oasis of Xinjiang using Sentinel data and a feature space model

Nigara TASHPOLAT1,2(), MA Yingxuan3, Abuduwaili REHEMAN1,2, YANG Lei1,2   

  1. 1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China
    2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, Xinjiang, China
    3. Xinjiang Branch China Meteorological Adminstration Training Centre, Urumqi 830013, Xinjiang, China
  • Received:2025-02-13 Revised:2025-03-19 Published:2026-02-25 Online:2026-02-27

摘要:

新疆作为中国土壤盐渍化典型区域,及时准确地掌握其动态信息对盐渍土治理与可持续土地利用具有重要意义。以新疆渭干河-库车河三角洲绿洲(简称渭库绿洲)为研究区,基于2022年7月的Sentinel-1雷达影像与Sentinel-2光学影像,结合同期野外实测土壤含盐量数据,提取并优选与土壤盐分显著相关的特征参数;通过构建Sentinel-1极化组合指数[V2-H]-[H]、[V2-H]-[(V2+H2)/V]、[V2-H]-[V-H]与Sentinel-2光谱指数CRSI-COSRI、CRSI-NDWI、CRSI-GARI共6种特征空间模型,对比分析各模型的反演精度,并利用最优模型实现渭库绿洲典型盐渍区土壤盐渍化空间分异制图。结果表明:(1) Sentinel-2光谱指数CRSI-COSRI构建的特征空间模型反演效果最佳,其相关系数达0.639,决定系数为0.670。(2) 研究区整体盐渍化程度较高,空间分异明显,盐渍化程度自西向东呈递增趋势。研究结果验证了特征空间模型在干旱区土壤盐渍化遥感监测中的有效性,为区域盐渍土精准监测与治理提供了方法与数据支撑。

关键词: 土壤盐渍化, Sentinel-1数据, Sentinel-2数据, 特征空间模型, 渭干河-库车河三角洲绿洲

Abstract:

Xinjiang requires timely and accurate monitoring of soil salinization dynamics to support effective management and sustainable land use. This study examines the Weigan-Kuqa River Delta Oasis (Weiku Oasis) in Xinjiang. Based on Sentinel-1 synthetic aperture radar imagery and Sentinel-2 optical imagery from July 2022, characteristic parameters that were significantly correlated with measured soil salinity values were extracted and optimized. Six feature space models were constructed, including three polarization combination models from Sentinel-1 ([V2-H]-[H], [V2-H]-[(V2+H2)/V], and [V2-H]-[V-H]) and three spectral index models from Sentinel-2 (CRSI-COSRI, CRSI-NDWI, and CRSI-GARI). Using the optimal model, soil salinity in the study area was inversely estimated, revealing its spatial distribution patterns and enabling precise monitoring of typical salinized areas within the Weigan-Kuqa River Delta Oasis. The results indicate that (1) The Sentinel-2-based CRSI-COSRI model achieved the best inversion performance, with a correlation coefficient (r) of 0.639 and a coefficient of determination (R2) of 0.670, significantly outperforming all other models. (2) The simulated spatial distribution of soil salinization indicated that the overall degree of soil salinization in the study area is relatively high, with an increasing trend from west to east. This study verifies the effectiveness of feature space models in the remote sensing-based inversion of soil salinization in arid regions, providing reliable data support and methodological references for regional salinized soil monitoring and management.

Key words: soil salinization, Sentinel-1 data, Sentinel-2 data, feature space model, Weigan-Kuqa River Delta Oasis