Estimation of soil salinity using satellite-based variables and machine learning methods

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-08-24 DOI:10.1007/s12145-024-01467-4
Wanli Wang, Jinguang Sun
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Abstract

Soil salinity is one of the significant environmental issues that can reduce crop growth and productivity, ultimately leading to land degradation. Therefore, accurate monitoring and mapping of soil salinity are essential for implementing effective measures to combat increasing salinity. This study aims to estimate the spatial distribution of soil salinity using machine learning methods in Huludao City, located in northeastern China. By meticulously collecting data, soil salinity was measured in 310 soil samples. Subsequently, environmental parameters were calculated using remote sensing data. In the next step, soil salinity was modeled using machine learning methods, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Additionally, to estimate uncertainty, the lower limit (5%) and upper limit (95%) prediction intervals were used. The results indicated that accurate maps for predicting soil salinity could be obtained using machine learning methods. By comparing the methods employed, it was determined that the RF model is the most accurate approach for estimating soil salinity (RMSE=0.03, AIC=-919, BIS=-891, and R2=0.84). Furthermore, the results from the prediction interval coverage probability (PICP) index, utilizing the uncertainty maps, demonstrated the high predictive accuracy of the methods employed in this study. Moreover, it was revealed that the environmental parameters, including NDVI, GNDVI, standh, and BI, are the main controllers of the spatial patterns of soil salinity in the study area. However, there remains a need to explore more precise methods for estimating soil salinity and identifying salinity patterns, as soil salinity has intensified with increased human activities, necessitating more detailed investigations.

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利用卫星变量和机器学习方法估算土壤盐度
土壤盐碱化是重大环境问题之一,会降低作物生长和生产力,最终导致土地退化。因此,准确监测和绘制土壤盐分分布图对于采取有效措施应对日益严重的盐分问题至关重要。本研究旨在利用机器学习方法估算位于中国东北部的葫芦岛市土壤盐分的空间分布。通过细致的数据采集,测量了 310 个土壤样本的土壤盐度。随后,利用遥感数据计算了环境参数。下一步,使用机器学习方法对土壤盐度进行建模,包括随机森林(RF)、支持向量机(SVM)和人工神经网络(ANN)。此外,为了估计不确定性,还使用了下限(5%)和上限(95%)预测区间。结果表明,使用机器学习方法可以获得准确的土壤盐度预测图。通过比较所采用的方法,确定 RF 模型是估算土壤盐度最准确的方法(RMSE=0.03,AIC=-919,BIS=-891,R2=0.84)。此外,利用不确定性地图得出的预测区间覆盖概率(PICP)指数结果表明,本研究采用的方法具有很高的预测准确性。此外,研究还发现,环境参数(包括 NDVI、GNDVI、standh 和 BI)是研究区域土壤盐渍化空间模式的主要控制因素。然而,随着人类活动的增加,土壤盐碱化程度加剧,仍需探索更精确的方法来估算土壤盐碱化程度并确定盐碱化模式,因此有必要进行更详细的调查。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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