辽东湾北部碱蓬群落及滩涂土壤盐分遥感反演模型  

Soil salinity inversion of Suaeda salsa community and tidal flat in northern Liaodong Bay

作　　者：王文硕 李微 孙悦 孙涛 刘远 WANG Wenshuo;LI Wei;SUN Yue;SUN Tao;LIU Yuan(Department of Marine Science and Environment, Dalian Ocean University, Dalian 116023, Liaoning, China)

机构地区：[1]大连海洋大学海洋科技与环境学院,辽宁大连116023

出　　处：《华中师范大学学报（自然科学版）》2022年第3期532-540,共9页Journal of Central China Normal University：Natural Sciences

基　　金：国家自然科学基金项目(41706199);2018年辽宁省高等学校创新人才支持计划;大连海洋大学第二届“湛蓝学者工程”项目;大连海洋大学大学生创新创业项目(201710158000021).

摘　　要：土壤盐分是评价土壤质量的重要指标,也是影响辽河口滨海湿地盐地碱蓬生长的主要环境因素之一,提出一种实时、准确、大尺度监测碱蓬群落及周围滩涂土壤盐分的算法十分必要.为了减少大气对模型的影响,该文利用地面高光谱数据模拟Landsat 8 OLI卫星反射率,采用基于交叉验证的逐步回归分析方法构建土壤盐分反演模型.结果表明:1)碱蓬样本的土壤盐分明显低于裸滩,海南三区域土壤盐分在总体上低于鸳鸯沟和笔架岭区域,而植株高度和生物量普遍均高于鸳鸯沟和笔架岭区域,在一定程度上说明了土壤盐分对盐地碱蓬生长的影响;2)模拟卫星反射率构建的多光谱指数与土壤盐分的相关性相较于单波段在整体上有所提高,其中植被指数NDVI和RVI与土壤盐分的相关性较高,相关系数达到了-0.689和-0.683;3)利用基于交叉验证的逐步回归分析法构建土壤盐分反演模型,模型的自变量为RVI、SAVI和SI3,模型的建模集决定系数R^(2)为0.684,均方根误差(RMSE)为3.45,验证集RMSE为1.88,相对分析误差(RPD)为2.28,表明模型的反演精度和反演能力较好;为了进一步验证模型的精度,对比分析基于逐步回归分析法筛选的指数因子构建的多元线性回归反演模型,发现交叉验证的逐步回归模型的R^(2)、RMSE均优于多元线性回归反演模型,同时土壤盐分反演值和实测值散点图更接近1∶1线,为辽东湾北部碱蓬群落及裸滩土壤盐分因子的反演提供技术及数据支持.Soil salinity is an important index to evaluate soil quality,and it is also one of the main environmental factors affecting the growth of Suaeda salsa(S.salsa)in the coastal wetland of Liaohe Estuary.It is necessary to propose a real-time,accurate and large-scale monitoring method for the soil salinity of S.salsa community and surrounding tidal flats.In order to reduce the influence of the atmosphere on the model,ground hyperspectral data was used to simulate the reflectance of Landsat 8 OLI satellite and a stepwise regression analysis method based on cross-validation was introduced to construct a soil salt inversion model.The result are shown as follows.1)The soil salinity of S.salsa samples are significantly lower than that of tidal flats.The soil salinity in the region of Hainan San is lower than that in Yuanyang Gou and Bijialing regions,but the plant height and biomass are higher than those in Yuanyang Gou and Bijialing regions,which indicated the effect of soil salinity on the growth of S.salsa to a certain extent.2)The correlation between multispectral indices constructed by the simulated satellite reflectance and soil salinity is improved as a whole compared with the correlation between the single band and soil salinity.NDVI and RVI have the high correlation with soil salinity,the values reach—0.689 and—0.683.3)The stepwise regression analysis method based on cross-validation was used to construct a soil salinity inversion model.The independent variables of the model were RVI,SAVI and SI3.The fitting accuracy of modeling set R2 is 0.684,with the root mean square error(RMSE)of 3.45,the validation set RMSE of 1.88,and the relative analysis error(RPD)of 2.28,suggesting that the model has good inversion accuracy and inversion ability.In order to further verify the accuracy of the model,a multivariate regression inversion model based on the factors screened by stepwise regression analysis is compared and analyzed.The results indicate that the R2and RMSE of the cross-validation stepwise regression model are

关 键 词：盐地碱蓬 土壤盐分 逐步回归 交叉验证 模拟遥感反射率 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]