内蒙古河套灌区土壤盐分多源多指数估算模型  

Using Multi-source and Multi-index Method to Assess Soil Salinity in Hetao Irrigation District in Inner Mongolia

作　　者：王欢 李瑞平[1] 张寅 李正中[3] 魏美玲 WANG Huan;LI Ruiping;ZHANG Yin;LI Zhengzhong;WEI Meiling(College of Water Conservancy and Civil Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China;Department of Water Conservancy and Civil Engineering,Hetao University,Bayannur 015000,China;Liberation Gate Branch Center,Inner Mongolia Hetao Irrigation District Water Conservancy Development Center,Bayannur 015000,China)

机构地区：[1]内蒙古农业大学水利与土木建筑工程学院,呼和浩特010018 [2]河套学院水利与土木工程系,内蒙古巴彦淖尔015000 [3]内蒙古河套灌区水利发展中心解放闸分中心,内蒙古巴彦淖尔015000

出　　处：《灌溉排水学报》2023年第10期122-128,共7页Journal of Irrigation and Drainage

基　　金：国家自然科学基金项目(52269004,52069021);内蒙古自然科学基金项目(2022MS05044)。

摘　　要：【目的】探究河套灌区乌拉特灌域土壤盐分遥感反演的最优方法。【方法】针对单一数据源、单一指数、单一算法对土壤盐分反演精度不高的问题,分别以光谱指数、极化组合指数为建模因子,利用偏最小二乘回归(PLSR)、自适应增强回归(AdaBoost)、随机森林回归(RF)3种算法构建土壤盐分反演模型,筛选出最优的土壤盐分反演模型,并监测了2019—2021年10月乌拉特灌域土壤盐分的时空分布。【结果】对于PLSR和AdaBoost模型,光谱指数的预测效果优于极化组合指数,对于RF模型,极化组合指数的预测效果优于光谱指数。PLSR模型在反演10 cm深度处的土壤盐分时,光谱指数的反演效果最优,决定系数为0.70;AdaBoost模型在反演2cm深度处的土壤盐分时,光谱指数反演效果最优,决定系数为0.74;RF模型在反演2cm深度处的土壤盐分时,极化组合指数的反演效果最优,决定系数为0.64。乌拉特灌域土壤盐渍化程度较重的区域主要位于灌域东南部,而西北部和中部盐渍化程度较轻。【结论】应用AdaBoost算法并结合改进的光谱指数有望提升河套灌区乌拉特灌域表层土壤盐分的反演精度。【Objective】This study is to propose a method to improve the accuracy of remote sensing-based soil salinity inversion in the Urartian irrigation area within the Hetao irrigation region.【Method】To address the challenge of low accuracy associated with using single data source,indices and algorithms for soil salinity inversion,we propose to use spectral indices and polarimetric-combination indices as the variables.Partial least squares regression(PLSR),adaptive boosting(AdaBoost),and random forest regression(RF)models were used to construct soil salinity inversion model,and the optimal inversion model was identified through comprehensive evaluation based on the spatiotemporal variation of soil salinity measured from the Urartian irrigation from 2019 to October 2021.【Result】The PLSR and AdaBoost models worked better using spectral indices than using polarimetric-combination indices,while RF model was superior in using the polarimetric-combination indices than using the spectral indices.The PLSR model was most accurate for predicting soil salinity at the depth of 10 cm,with the coefficient of determination being 0.70.The AdaBoost model was the best for predicting soil salinity at the depth of 2cm,with the coefficient of determination being 0.74,while the RF model worked best when using the polarimetric combination indices to predict soil salinity at the depth of 2 cm,with the coefficient of determination being 0.64.Soil with high salinity was predominantly located in the southeast,while soil in the northwest and the center were only slightly salinized.【Conclusion】Combing the improved spectral index and the AdaBoost algorithm is more accurate in using the inversion model to predict the salinity of the surface soil in Urartian irrigation area in the Hetao irrigation district.

关 键 词：哨兵遥感数据 土壤盐分 光谱指数 极化组合指数 机器学习 

分 类 号：TP19[自动化与计算机技术—控制理论与控制工程] S156.4[自动化与计算机技术—控制科学与工程]