滨海盐土土壤水分的高光谱参数及估测模型  被引量:18

Hyperspectral parameters and prediction model of soil moisture in coastal saline

在线阅读下载全文

作  者:李晨[1] 张国伟[2] 周治国[1] 赵文青[1] 孟亚利[1] 陈兵林[1] 王友华[1] 

机构地区:[1]南京农业大学/农业部作物生理生态与生产管理重点实验室/江苏省现代作物生产协同创新中心,南京210095 [2]江苏省农业科学院经济作物研究所,南京210014

出  处:《应用生态学报》2016年第2期525-531,共7页Chinese Journal of Applied Ecology

基  金:江苏省水利科技攻关项目(2011073;2012020;2013038)资助~~

摘  要:基于滨海盐土5个试验点的土壤含水量和室内土壤表面高光谱反射率,综合分析了350~2500 nm波段范围内土壤含水量与土壤光谱之间的关系,并基于比值光谱指数(RSI)、归一化光谱指数(NDSI)和差值光谱指数(DI)确定了光谱参数,进而构建土壤含水量估测定量模型.结果表明:滨海盐土原始光谱反射率与土壤含水量呈显著负相关关系,且最大负相关出现在1930 nm(r=0.86)附近.对RSI、NDSI和DI的直线回归方程、幂函数回归方程进行对比,以RSI(R_(1407),R_(1459))为自变量构建的土壤含水量指数函数线性回归方程决定系数最大(0.780),标准误较小(0.016),拟合方程为y=0.00001e^(9.72053 x).估测模型能够更好地监测滨海盐土土壤水分状况.基于RSI(R_(1407),R_(1459))构建的模型可实现对江苏省滨海盐土土壤水分的精确监测.Based on the data of soil moisture content and indoor soil surface spectral reflectance from five sampling sites of coastal saline soil, this paper analyzed the relationship between soil moisture content and soil spectrum in wavelength 350-2500 nm. We determined spectral parameters under ratio spectral index (RSI) , normalized difference spectral index (NDSI) and difference spectral index (DI) , and established the quantitative model of soil moisture content. The results showed significant negative correlation between spectral reflectance and soil moisture content, and the maximum negative correlation was near 1930 nm (r= 0.86). By comparison of the regression equation of RSI, NDSI and DI, it was found that the regression equation of exponential function (y= 0.00001e^972053x) built by soil moisture content based on RSI (R1407, R1459 ) presented the maximum R2 (0.780) and the minimum SE (0.016). The established model based on RSI (R1407, R1459) could be used to monitor soil moisture content accurately in Jiangsu coastal saline soils.

关 键 词:滨海盐土 高光谱参数 土壤水分 高光谱遥感 

分 类 号:S152.7[农业科学—土壤学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象