干旱区典型绿洲多深度土壤盐渍化反演及时空变化机制研究——以维库绿洲为例

IF 5.9 1区 农林科学 Q1 AGRONOMY
Jinming Zhang , Jianli Ding , Zihan Zhang , Jinjie Wang , Xu Zeng , Xiangyu Ge
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引用次数: 0

摘要

土壤盐渍化是干旱半干旱地区普遍存在的问题,严重威胁着农业生产力和环境的可持续性。然而,由于环境因素之间复杂的相互作用和有限的地面观测,准确地模拟和预测多个深度随时间的土壤盐度仍然是一个挑战。了解土壤盐分时空特征及其驱动因素对制定科学合理的灌溉策略和修复方法具有重要意义。以典型干旱区绿洲微库绿洲为例,以Landsat遥感影像为数据源,结合近10年土壤盐度野外实测数据,采用Bootstrap Soft Shrinkage(BOSS)算法选取特征变量,通过卷积神经网络和长短期记忆网络(CNN-LSTM)框架构建不同深度土壤盐度反演模型。分析了渭库绿洲土壤盐分的时空变化特征,并利用最优地理探测器探索了研究区土壤盐分变化的驱动机制。结果表明:(1)采用CNN-LSTM框架构建的多深度土壤盐度预测模型具有较好的预测效果,其中0-10 cm土壤盐度预测模型的预测精度最高,在测试集中的R²为0.7。10-20 cm、20-40 cm、40-60 cm、60-80 cm和80-100 cm深度土壤盐度预测模型测试集的R²值分别为0.5、0.57、0.53、0.5和0.52。(2)基于土壤盐分分类,各深度非盐渍化土壤面积均呈增加趋势,而绿洲内部非盐渍化土壤呈盐分积累趋势。(3)微库绿洲土壤盐分受多种驱动因子的影响,任意两种驱动因子的交互作用都增强了土壤盐分变化的解释力。未来的研究还可以关注更精细的跨季节土壤盐分制图,以更高的时空分辨率获得更全面的驱动数据,分析土壤盐分在不同土壤深度之间的转移机制,为盐渍化的科学管理提供理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis
Soil salinization is a widespread issue in arid and semi-arid regions, severely threatening agricultural productivity and environmental sustainability. However, accurately modeling and predicting soil salinity at multiple depths over time remains a challenge due to complex interactions among environmental factors and limited ground observations. Understanding the spatiotemporal characteristics of soil salinity and its driving factors is essential for formulating more scientific and rational irrigation strategies and remediation methods. Taking the Wei-Ku Oasis, a typical arid region oasis, as an example, this study uses Landsat remote sensing imagery as the data source, incorporating soil salinity field measurements over a decade, employing the Bootstrap Soft Shrinkage(BOSS) algorithm to select feature variables, and building soil salinity inversion models at various depths through a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) framework. The spatiotemporal variation of soil salinity in the Wei-Ku Oasis is analyzed, and the driving mechanisms of soil salinity change in the study area are explored using the optimal geographic detector. Results indicate that: (1) The multi-depth soil salinity inversion models built with the CNN-LSTM framework exhibit superior predictive performance, with the 0–10 cm soil salinity prediction model achieving the highest accuracy, attaining an R² of 0.7 in the test set. The R² values for the test set of soil salinity prediction models at 10–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm depths are 0.5, 0.57, 0.53, 0.5, and 0.52, respectively. (2) Based on soil salinity classification, the area of non-salinized soil at all depths shows an increasing trend, whereas non-salinized soil within the oasis exhibits salinity accumulation. (3) Soil salinity in the Wei-Ku Oasis is influenced by multiple types of driving factors, with the interaction of any two factors enhancing the explanatory power for salinity changes. Future research could also focus on more refined soil salinity mapping across seasons, obtaining more comprehensive driving data with higher temporal and spatial resolutions, analyzing the transfer mechanisms of soil salinity between different soil depths, and providing a theoretical basis for the scientific management of salinization.
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来源期刊
Agricultural Water Management
Agricultural Water Management 农林科学-农艺学
CiteScore
12.10
自引率
14.90%
发文量
648
审稿时长
4.9 months
期刊介绍: Agricultural Water Management publishes papers of international significance relating to the science, economics, and policy of agricultural water management. In all cases, manuscripts must address implications and provide insight regarding agricultural water management.
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