分数阶微分数据变换在滨海盐渍土盐分反演中的适用性  

Applicability of fractional-order differential transformation for salinization monitoring in coastal saline soils

作　　者：潘昊 陈诗扬 李祎森 李映祥 曹怀堂 刘佳[1] PAN Hao;CHEN Shiyang;LI Yisen;LI Yingxiang;CAO Huaitang;LIU Jia(Institute of Agricultural Resource and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China)

机构地区：[1]中国农业科学院农业资源与农业区划研究所,北京100081

出　　处：《农业工程学报》2025年第3期73-82,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家科技重大专项——高分辨率对地观测系统重大专项(30-Y60B01-9003-22/23,09-Y30F01-9001-20/22)。

摘　　要：滨海地区是中国重要的农业生态功能区,土壤盐渍化已成为该区域土地生产力退化的主要因素。为探索分数阶微分(fractional-order differentiation,FOD)数据变换在滨海盐碱地盐渍化监测中的应用潜力,该研究以中国北方典型滨海盐渍化区域—河北省黄骅市为研究区,利用环境减灾二号卫星(HJ-2B)高光谱影像,进行了阶数范围为0~2.0、步长为0.1的FOD数据变换。通过分析不同阶数下3类土壤(非盐渍化、轻度盐渍化、重度盐渍化)的光谱特征及其反射率与土壤含盐量的相关性,筛选出对土壤盐分敏感的波段作为模型输入,进而基于梯度提升机(gradient boosting machine,GBM)实现土壤盐分反演。结果表明:1)在0.9阶微分光谱下,3类土壤的光谱差异最为显著且与土壤含盐量的相关性最高,相关系数达到0.58;2)在FOD数据变换的基础上,结合皮尔逊相关性分析,计算了各波段在0~2.0阶范围内的反射率与土壤含盐量的相关性均值。结果显示,960、1 630、1 975、975和2 140 nm波段与土壤含盐量具有较高相关性,适合作为模型输入变量,以提升滨海盐碱地盐渍化监测的精度;3)根据光谱特征分离度和相关性排序,筛选出0、0.5、0.9、1.0、1.1和1.5共6个FOD变换阶数用于土壤盐分反演。其中,0.9阶影像反演精度最高,优于原始光谱和整数阶光谱,决定系数达0.78,均方根误差为1.0 g/kg。总体而言,FOD数据变换能更有效地揭示土壤含盐量与光谱信息的非线性关系,研究结果可为滨海盐碱地及其他区域的高光谱遥感土壤盐渍化监测提供参考。Soil salinization has become a major limiting factor for land productivity in coastal areas,a key agricultural ecological zone in China.In recent years,fractional-order differentiation(FOD) data transformation has gained attention for its applications in hyperspectral remote sensing-based salinization monitoring.However,the effectiveness of FOD varies across spatial and temporal scales due to differences in soil formation environments,and its applicability in humid to semi-humid coastal salinized areas remains unexplored.To address this gap,this study focuses on Huanghua City in Hebei Province,a representative coastal salinized region in northern China,to evaluate the potential of FOD for monitoring soil salinization.Using hyperspectral imagery from the HJ-2B satellite,the study employed the Grünwald-Letnikov(G-L) fractional-order differentiation method to transform spectral data across fractional orders from 0 to 2.0,with a step size of 0.1.This method aimed to reduce baseline drift and noise while enhancing spectral feature variations.Spectral characteristics of three soil types(non-salinized,mildly salinized,and heavily salinized) were analyzed,along with the correlation between spectral reflectance and soil salinity.Data from 60 field-collected soil samples were used to calculate Pearson correlation coefficients.Key spectral bands most sensitive to soil salinity were identified and utilized as input variables to develop a soil salinity inversion model based on a gradient boosting machine(GBM).The results demonstrated the following:FOD Enhances Spectral Correlation with Soil Salinity.FOD significantly improved the correlation between spectral data and soil salinity.The spectral differences among the three soil types were most pronounced under 0.9 order differentiation,achieving the highest correlation with soil salinity(maximum correlation coefficient of 0.58).This represents improvements of 25%,2.5%,and 50% compared to the original spectrum,first-order differentiation,and second-order differentiation,respect

关 键 词：遥感 土壤含盐量 高光谱影像 分数阶微分数据变换 敏感波段 梯度提升机 

分 类 号：S126[农业科学—农业基础科学]