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利用谷歌地球引擎研究土壤盐分的时空变化:以中国西部的渭干河-库车绿洲为例。

Investigation of the spatial and temporal variation of soil salinity using Google Earth Engine: a case study at Werigan-Kuqa Oasis, West China.

机构信息

College of Geography and Remote Sensing Sciences, Xinjiang University, No. 777 Huarui Street, Xinjiang, 830017, Urumqi, China.

Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, 830017, Urumqi, China.

出版信息

Sci Rep. 2023 Feb 16;13(1):2754. doi: 10.1038/s41598-023-27760-8.

Abstract

Large-scale soil salinity surveys are time-costly and labor-intensive, and it is also more difficult to investigate historical salinity, while in arid and semi-arid regions, the investigation of the spatial and temporal characteristics of salinity can provide a scientific basis for the scientific prevention of salinity, With this objective, this study uses multi-source data combined with ensemble learning and Google Earth Engine to build a monitoring model to observe the evolution of salinization in the Werigan-Kuqa River Oasis from 1996 to 2021 and to analyze the driving factors. In this experiment, three ensemble learning models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were established using data collected in the field for different years and some environmental variables, After the accuracy validation of the model, XGBoost had the highest accuracy of salinity prediction in this study area, with RMSE of 17.62 dS m, R of 0.73 and RPIQ of 2.45 in the test set. In this experiment, after Spearman correlation analysis of soil Electrical Conductivity (EC) with environmental variables, we found that the near-infrared band in the original band, the DEM in the topographic factor, the vegetation index based on remote sensing, and the salinity index soil EC had a strong correlation. The spatial distribution of salinization is generally characterized by good in the west and north and severe in the east and south. Non-salinization, light salinization, and moderate salinization gradually expanded southward and eastward from the interior of the western oasis over 25 years. Severe and very severe salinization gradually shifted from the northern edge of the oasis to the eastern and southeastern desert areas during the 25 years. The saline soils with the highest salinity class were distributed in most of the desert areas in the eastern part of the Werigan-Kuqa Oasis study area as well as in smaller areas in the west in 1996, shrinking in size and characterized by a discontinuous distribution by 2021. In terms of area change, the non-salinized area increased from 198.25 in 1996 to 1682.47 km in 2021. The area of saline soil with the highest salinization level decreased from 5708.77 in 1996 to 2246.87 km in 2021. overall, the overall salinization of the Werigan-Kuqa Oasis improved.

摘要

大规模土壤盐度调查既耗时又费力,而且更难调查历史盐度,而在干旱和半干旱地区,调查盐度的时空特征可为科学防治盐度提供科学依据。有鉴于此,本研究使用多源数据结合集成学习和谷歌地球引擎构建监测模型,观察 1996 年至 2021 年蔚岗-库车河绿洲盐渍化的演变,并分析驱动因素。在本实验中,使用不同年份采集的野外数据和一些环境变量,建立了三个集成学习模型,即随机森林(RF)、极端梯度提升(XGBoost)和轻梯度提升机(LightGBM)。在模型精度验证后,XGBoost 在本研究区的盐度预测精度最高,在测试集中 RMSE 为 17.62 dS m,R 为 0.73,RPIQ 为 2.45。在本实验中,对土壤电导率(EC)与环境变量进行 Spearman 相关分析后,发现原始波段中的近红外波段、地形因子中的 DEM、基于遥感的植被指数和盐度指数土壤 EC 之间具有很强的相关性。盐渍化的空间分布一般以西、北方向较好,东、南方向较严重。25 年来,非盐渍化、轻度盐渍化和中度盐渍化逐渐从西部绿洲内部向南、向东扩展。25 年来,重度和极重度盐渍化逐渐从绿洲北部边缘向东部和东南部沙漠地区转移。在 1996 年,最高盐度等级的盐渍土主要分布在蔚岗-库车河绿洲研究区东部大部分沙漠地区以及西部较小区域,到 2021 年,面积缩小,分布呈不连续状态。在面积变化方面,非盐渍化面积从 1996 年的 198.25km 增加到 2021 年的 1682.47km。最高盐度等级的盐渍土面积从 1996 年的 5708.77km 减少到 2021 年的 2246.87km。总体而言,蔚岗-库车河绿洲的整体盐渍化程度有所改善。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd16/9935516/f6fbade83414/41598_2023_27760_Fig1_HTML.jpg

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