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

被引:0
|
作者
Shilong Ma
Baozhong He
Boqiang Xie
Xiangyu Ge
Lijing Han
机构
[1] Xinjiang University,College of Geography and Remote Sensing Sciences
[2] Xinjiang University,Xinjiang Key Laboratory of Oasis Ecology
[3] Xinjiang University,Key Laboratory of Smart City and Environment Modelling of Higher Education Institute
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
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−1, R2 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 km2 in 2021. The area of saline soil with the highest salinization level decreased from 5708.77 in 1996 to 2246.87 km2 in 2021. overall, the overall salinization of the Werigan–Kuqa Oasis improved.
引用
收藏
相关论文
共 50 条
  • [21] Assessing temporal snow cover variation in the Sutlej river basin using google earth engine and machine learning models
    Abhilash Gogineni
    Madhusudana Rao Chintalacheruvu
    Earth Science Informatics, 2024, 17 : 455 - 473
  • [22] Identifying the spatio-temporal dynamics of regional ecological risk based on Google Earth Engine: A case study from Loess Plateau, China
    Shen, Wencang
    Zhang, Jianjun
    Wang, Ke
    Zhang, Zhengfeng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 873
  • [23] Multi-Temporal Assessment of Soil Erosion After a Wildfire in Tuscany (Central Italy) Using Google Earth Engine
    Barbadori, Francesco
    Confuorto, Pierluigi
    Chouksey, Bhushan
    Moretti, Sandro
    Raspini, Federico
    LAND, 2024, 13 (11)
  • [24] Evaluation of Urban Ecological Environment Quality Based on Google Earth Engine: A Case Study in Xi'an, China
    Yang, Shuo
    Su, Hao
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2023, 32 (01): : 927 - 942
  • [25] Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China
    Li, Shuai
    Guo, Pu
    Sun, Fei
    Zhu, Jinlei
    Cao, Xiaoming
    Dong, Xue
    Lu, Qi
    LAND, 2024, 13 (06)
  • [26] Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China
    Wei, Yang
    Shi, Zhou
    Biswas, Asim
    Yang, Shengtian
    Ding, Jianli
    Wang, Fei
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 716 (716)
  • [27] Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine
    Li, Yuchen
    Liu, Chenli
    Zhang, Jun
    Zhang, Ping
    Xue, Yufei
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 9450 - 9461
  • [28] Expansion and Evolution of a Typical Resource-Based Mining City in Transition Using the Google Earth Engine: A Case Study of Datong, China
    Xue, Minghui
    Zhang, Xiaoxiang
    Sun, Xuan
    Sun, Tao
    Yang, Yanfei
    REMOTE SENSING, 2021, 13 (20)
  • [29] Spatial and Temporal Variation of Soil Salinity During Dry and Wet Seasons in the Southern Coastal Area of Laizhou Bay, China
    Liu Wenquan
    Lu Fang
    Xu Xingyong
    Chen Guangquan
    Fu Tengfei
    Su Qiao
    INDIAN JOURNAL OF GEO-MARINE SCIENCES, 2020, 49 (02) : 260 - 270
  • [30] Monitoring Interannual Variability in Spatial Distribution of Plastic-Mulched Farmland in Black Soil Areas Using Google Earth Engine
    Zhong, Jingfa
    Ji, Dongmei
    Chang, Lei
    Li, Yuefen
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 4347 - 4365