Quick Search:       Advanced Search
引用本文:
【打印本页】   【HTML】   【下载PDF全文】   View/Add Comment  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 301次   下载 349 本文二维码信息
码上扫一扫!
分享到: 微信 更多
半干旱草原型流域表层土壤饱和导水率传递函数及遥感反演研究
黎明扬,刘廷玺,罗艳云,段利民,张俊怡,周亚军,BUREN Scharaw
内蒙古农业大学水利与土木建筑工程学院,内蒙古农业大学水利与土木建筑工程学院,内蒙古农业大学水利与土木建筑工程学院,内蒙古农业大学水利与土木建筑工程学院,内蒙古农业大学水利与土木建筑工程学院,内蒙古农业大学水利与土木建筑工程学院,Application Center for System Technologies, Fraunhofer IOSB
摘要:
为了进一步探究遥感与表层土壤导水率的联系,以内蒙古锡林河流域为研究区,对厚栗黄土、草甸沼泽土、荒漠风沙土、石灰性草甸砂土、淡黑土5种土壤类型下的表层土壤进行采样,测定其粒径组成、容重、有机质、饱和导水率等理化特征。使用Saxton、Cosby、Wosten三种土壤饱和导水率传递函数及非线性多元经验回归模型对点尺度上0~30 cm表层土壤每10 cm土层的饱和导水率进行拟合,并利用这些点的土壤参数均值进行验证,结果显示Saxton模型效果最好,拟合及验证结果与实测值的拟合系数R2分别为0.985、0.988。在此基础上,利用Radarsat-2四极化后向散射数据将模型中的表层土壤参数均值进行了8 m精度的面尺度扩展,并对遥感影像研究区范围内表层土壤饱和导水率进行预测。研究表明,土壤传递函数可以很好地刻画半干旱草原型流域表层土壤的饱和导水率,且土壤传递函数的结果与模型中包含的参数个数并不存在直接联系,人类活动对研究区表层土壤的饱和导水率影响极大。
关键词:  锡林河流域  土壤传递函数  饱和导水率  遥感反演  Radarsat-2
DOI:DOI:10.11766/trxb201803200104
分类号:
基金项目:国家国际科技合作专项(2015DFA00530),国家自然科学基金青年与面上基金项目(51509131,51479086)
Pedo-Transfer Function and Remote-Sensing-Based Inversion Saturated Hydraulic Conductivity of Surface Soil Layer in Xilin River Basin
LI Mingyang,LIU Tingxi,LUO Yanyun,DUAN Limin,ZHANG Junyi,ZHOU Yajun and Buren Scharaw
Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Application Center for System Technologies, Fraunhofer IOSB
Abstract:
【Objective】Remote sensing technology has successfully been applied to monitoring and inversion of soil parameters. In order to further promote application of the Synthetic Aperture Radar (SAR) technology to less-destructive soil monitoring and environmental management, based on the field survey and sampling, laboratory experiments and interpretation of microwave radar images, this study explored possibilities of inverting and predicting soil hydraulic parameters of semi-arid grassland basins on the premise of no large scaled damage.【Method】In this paper, the Xilin River Basin, Inner Mongolia was set as subject for the study. The study area had 5 major types of soils, i.e. Thick chestnut soil, Meadow swamp soil, Desert aeolian soil, Limy meadow sandy soil, and Pale black soil. From the surface layers of the soils, soil samples were collected for analysis of particle size composition, bulk density, organic matter content, saturated hydraulic conductivity ( Ks) and some other physical and chemical properties. First of all, distributions of soil parameters in soil layers, 10 cm each, of the five types of soils were characterized. Then Saxton, Cosby, and Wosten models, three saturated soil hydraulic conductivity pedo-transfer functions (PTFs) and nonlinear multivariate empirical regression models, were used for fitting of Ksin soil layers, 10cm each, within the 0~30 cm soil layers of the 32 sampling sites for modeling. Based on the averages of the soil parameters of these 32 sampling sites for modeling and backscattering of quadrupolarized Radarsat-2, a multivariate linear equation was established, using the radar data of the 10 sampling sites for validation to validate the fitting of Ks.【Result】Results show that the study area is extremely high in soil sand content, almost nil in clay content and low in organic matter content. The parameters do not vary much with soil depth from layer to layer. In terms of PTFs, the four models reach 0.778, 0.985, 0.958, and 0.966 in modeling accuracy separately. Among them, Saxton model is the highest, with RMSE being 0.262 and layer average validation accuracy reaching 0.989. In terms of inversion of surface soil parameters based on back scattering coefficient of quadrupolarized RADARSAT-2, the inversions of bulk density and sand content are the best. In using the backscattering coefficient of the 10 validating sampling sites to validate PTFs, Saxton model is superior in fitting, with simulation coefficient reaching as high as 0.964. Consequently, this study has finally chosen Saxton model to predict surface soil saturated hydraulic conductivity on an 8 meter precision extended scale based on remote-sensing images of the study area, by combining Radarsat-2 radar data.【Conclusion】All the findings indicate that compared with inversion directly using SAR to predict surface soil Ksin a large-scale, PTFs may better depict saturated conductivity of the surface soil layer in semi-arid grassland watersheds. However, the effect of PTFs are not directly related to the number of parameters contained in the model. Surface soil Ks in the degraded grassland varies generally in the range of 4~8 m•d-1. Under natural conditions, the region lacks vegetation and high in sand content, which are the main reasons for rapid water transfer. In developed areas, like irrigation zones or urbanized regions, surface soil Ksdeclines by a large margin, indicating that human activity is one of the main influencing factors of its change. The use of remote sensing to predict surface soil KS over a region is still not fully developed, so more efforts should be done to perfect and validate it.
Key words:  Xilin River Basin  Pedo-transfer function  Saturated hydraulic conductivity  Remote sensing inversion  RADARSAT-2