CAJ | 학술논문

为研究中原黄泛区土壤盐分空间变异，以河南省封丘县为研究区，综合考虑引起土壤盐渍化的土壤盐分、地形、地下水位及矿化度、植被情况及其他影响因素，基于遥感影像和磁感式探测获得的土壤表观电导率等多源数据建立了区域土壤盐分综合评估模型，并对研究区分层土壤盐分空间变异进行评估。结果表明：对于0～60 cm土层利用多源数据进行模型构建中土壤表观电导率与光谱指数占主要比例，模型对于各层土壤盐分的评价精度0～60 cm土层优于≥60～120 cm土层。土壤盐分含量随着深度的增加而增大，变异系数在0.22～0.28之间，属中等变异强度。土壤盐分主要集中分布在研究区北部与东南部，尤其是东南角黄河沿线区域，且随着土壤剖面显示出从表现到深层逐渐增加的趋势。利用多源数据建立的分层土壤盐分综合评估模型对于区域土壤盐分解析具有较高精度。该研究为中原黄泛区土壤盐化消减与土壤质量提升提供了可靠新方法。
위연구중원황범구토양염분공간변이，이하남성봉구현위연구구，종합고필인기토양염지화적토양염분、지형、지하수위급광화도、식피정황급기타영향인소，기우요감영상화자감식탐측획득적토양표관전도솔등다원수거건립료구역토양염분종합평고모형，병대연구구분층토양염분공간변이진행평고。결과표명：대우0～60 cm토층이용다원수거진행모형구건중토양표관전도솔여광보지수점주요비례，모형대우각층토양염분적평개정도0～60 cm토층우우≥60～120 cm토층。토양염분함량수착심도적증가이증대，변이계수재0.22～0.28지간，속중등변이강도。토양염분주요집중분포재연구구북부여동남부，우기시동남각황하연선구역，차수착토양부면현시출종표현도심층축점증가적추세。이용다원수거건립적분층토양염분종합평고모형대우구역토양염분해석구유교고정도。해연구위중원황범구토양염화소감여토양질량제승제공료가고신방법。
Salinization and alkalinization are two of important land degradation processes in flood area of the Yellow River in central China. A synthesized model for assessment of regional soil salinity was established based on multi-source data including soil salinity, topographical variable, the groundwater level and mineralization degree, vegetation and other factors to the soil salinization. A total of 101 soil columns were sampled from the study area using grid sampling method, and then analyzed for soil electrical conductivity (ECe) and other soil properties. Auxiliary data used in this study to interpret variability of soil salinization were Landsat 5 TM data, apparent electrical conconductivity (ECa) measured using an electromagnetic induction instrument (EM38), altitude derived from topographic map, the groundwater table and mineralization degree and soil pH. The spatial variability of soil salinity was assessed in Fengqiu County, Henan Province, China. Classification and regression tree was applied to obtain the relationships between ECe (0-120 cm) and the auxiliary data. The results showed that ECa accounted for the major proportion of model prediction from multi-source data in classification and regression tree model of total soil layer. Generally, ECaH (apparent soil electrical conductivity from EM38 horizontal mode) and spectral index (dvi: difference vegetation index, bi:soil index, int2: intentity, int1: intentity, ndvi: normalized difference vegetation index, si2: soil index and si1: soil index) were common variable for 0-60cm soil layer. For the 0-30 cm depth, plant index (ndvi and dvi), soil index (si1, si2 and bi) and intentity (int1 and int2) had the highest influence on the model prediction followed by ECa. Plant index (dvi) accounted of more than 50% for 0-60 cm soil layer used in the model. Meanwhile, for≥60-120 cm, ECaV (apparent soil electrical conductivity from EM38 vertical mode) was the most important variable used in regression tree model. Validation of the predictive models at each depth resulted in determination coefficient (R2 values) ranging from 0.52 to 0.65. The root mean square error (RMSE) value ranged between 0.72 to 1.27 dS/m. The model for the evaluation of the soil salinity of 0-60 cm was better than that of soil layer of≥60-120 cm. The mean of soil salinity varied from 1.26 to 1.61 dS/m from top to the bottom of soil profiles, and the soil salinity at the bottom was the highest in total soil profile, which indicated soil salinity accumulation at the bottom. ECe varied from 0.79 to 3.68 dS/m from top to the bottom of soil profiles. Coefficient of variation of soil salinity at each soil 1ayer was from 0.22 to 0.28 and exhibited the moderate spatial variability. The groundwater table varied from -14.0 m to -0.2 m. Coefficient of variation of groundwater table was 0.7 which exhibited the moderate spatial variability. The mean of groundwater electrical conductivity was 1.44 dS/m, which was similar to soil electrical conductivity. The mean of pH value varied from 8.88 to 9.28 and increased with increasing soil depth. Digital maps of ECa (horizontal and vertical modes) along with other environmental variables were used to predict the spatial distribution of ECe. The high values of soil salinity were mainly distributed in the northern and southern area along the Yellow river in the study area, especially the southeast region. The synthesized model based on multi-source data had high precision for assessment of regional soil salinity. Thus, the application of this technique provides a new method to improve soil salinization and soil quality in the flood area of the Yellow River in central China.