干旱区生态环境知识资源中心(Arid): 干旱区绿洲土壤有机碳高光谱估测及其影响因子分析

Arid

	干旱区绿洲土壤有机碳高光谱估测及其影响因子分析
其他题名	Hyperspectral estimation of soil organic carbon and its influencing factors in arid oasis
	周倩倩; 丁建丽; 黄帅
来源期刊	干旱地区农业研究
ISSN	1000-7601
出版年	2018
卷号	36 期号:5 页码:200-206,214
中文摘要	为了揭示干旱区绿洲土壤有机碳的分布格局及环境因子的影响,以新疆渭干河-库车河三角洲绿洲为研究区,通过实测土壤高光谱数据以及化学分析获取的剖面土壤有机碳数据,提出了一种土壤光谱和偏最小二乘回归预测模型相结合的土壤有机碳估算方法,并分析了表层土壤有机碳含量与环境因子之间的相关性。结果表明:土壤反射率二阶微分变换优于一阶微分,经二阶微分变换后的光谱可以较好地预测土壤有机碳含量,预测模型的决定系数R2均大于0.8,RPD均大于1.5,模型具有较高的精度和较好的稳定性。绿洲剖面土壤有机碳含量介于0.172~ 17.376 g·kg~(-1)之间,且主要集中在0~ 60 cm;土壤有机碳的变异系数在35%~ 53%;各层土壤有机碳分布格局相似,绿洲内部明显高于绿洲外围,并均有向表层聚集的趋势。相关分析表明土壤含盐量是影响土壤有机碳含量最重要的环境因子。
英文摘要	Soil organic carbon (SOC) concentration is a useful soil property which can guide agricultural applications of chemical inputs.Soil organic carbon is also an important factor affecting regional carbon budget.It is impending to develope more time and cost-efficient methodologies for SOC analysis.To reveal the distribution pattern of SOC and its influencing factors in arid area,the experiment was conducted in the delta oasis between the Weigan- Kuqa River Oasis.The SOC content was predicted by VNIR-PLSR model using kriging. The results showed that the SOC value in the oasis varies from 0.172 to 17.376 g·kg~(-1) among the soil samples. The value of the coefficient of variation was between 35%~53%.The most important wavelengths for SOC prediction were 550nm and 650 nm in the visible,and 780nm,818nm,866nm,1423nm,1733nm,2005 nm and 2172 nm in the near-infrared region. Second-order differential transformation was better than the first-order differential transformation. VNIR-PLSR regression model determination coefficient (R2) was higher than 0.853 and all residual prediction deviation (RPD) was higher than 1.5.The distribution pattern of SOC in each layer was similar. The SOC accumulation in top soil and the content in the oasis was significantly higher than which in the oasis periphery. Soil salt content was the most important environmental factor for the content of SOC. Quantitative spectral analysis of SOC by vis-NIR reflectance spectroscopy was feasible. The spectrum transformed with second-order differential could accurately predict SOC content.The model with partial least squares regression model had higher prediction accuracy and better robustness.
中文关键词	土壤有机碳 ; 高光谱 ; 空间分布预测 ; 环境因子
英文关键词	soil organic carbon hyperspectral spatial distribution prediction environmental factors
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6349961
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/237566
作者单位	新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 新疆 830046, 中国
推荐引用方式 GB/T 7714	周倩倩,丁建丽,黄帅. 干旱区绿洲土壤有机碳高光谱估测及其影响因子分析[J]. 新疆大学,2018,36(5):200-206,214.
APA	周倩倩,丁建丽,&黄帅.(2018).干旱区绿洲土壤有机碳高光谱估测及其影响因子分析.干旱地区农业研究,36(5),200-206,214.
MLA	周倩倩,et al."干旱区绿洲土壤有机碳高光谱估测及其影响因子分析".干旱地区农业研究 36.5(2018):200-206,214.