太湖流域典型农用地面源磷流失的土壤主控因子光谱识别

熊俊峰; 林晨; 马荣华; 吴治澎; 闵敏

doi:10.13961/j.cnki.stbctb.2017.02.020

您当前的位置：

首页 >

文章列表页 >

太湖流域典型农用地面源磷流失的土壤主控因子光谱识别

试验研究

- 太湖流域典型农用地面源磷流失的土壤主控因子光谱识别
- Spectral Identification of Main Control Factors of Soil Phosphorus Loss from Typical Agricultural Land in Taihu Basin
- 水土保持通报 2017年37卷第2期页码：137-141
- 作者机构：
  
  1. 南京信息工程大学地理与遥感学院,江苏,南京,210044
  2. 中国科学院南京地理与湖泊研究所中国科学院流域地理学重点实验室,江苏,南京,210008
  3. 南京大学地理与海洋科学学院,江苏,南京,210046
  4. 河南大学黄河文明与可持续发展研究中心,河南,开封,475001
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年项目“典型林耕地转换模式下颗粒态磷流失的土壤主控因子光谱甄别”（41301227）
- DOI：10.13961/j.cnki.stbctb.2017.02.020
  中图分类号：
- 纸质出版：2017
- 稿件说明：
移动端阅览
熊俊峰, 林晨, 马荣华, 等. 太湖流域典型农用地面源磷流失的土壤主控因子光谱识别[J]. 水土保持通报, 2017,37(2):137-141.

XIONG Junfeng, LIN Chen, MA Ronghua, et al. Spectral Identification of Main Control Factors of Soil Phosphorus Loss from Typical Agricultural Land in Taihu Basin[J]. Bulletin of Soiland Water Conservation, 2017, 37(2): 137-141.
熊俊峰, 林晨, 马荣华, 等. 太湖流域典型农用地面源磷流失的土壤主控因子光谱识别[J]. 水土保持通报, 2017,37(2):137-141. DOI： 10.13961/j.cnki.stbctb.2017.02.020.

XIONG Junfeng, LIN Chen, MA Ronghua, et al. Spectral Identification of Main Control Factors of Soil Phosphorus Loss from Typical Agricultural Land in Taihu Basin[J]. Bulletin of Soiland Water Conservation, 2017, 37(2): 137-141. DOI： 10.13961/j.cnki.stbctb.2017.02.020.

摘要

[目的

]

通过光谱识别太湖流域农用地面源磷流失的土壤主控因子，为简化面源磷流失强度估算提供依据。[方法

]

通过分析梅梁湾流域耕地和园地中不同面源磷流失强度下的土壤光谱特征，确定影响面源磷流失强度的主要土壤理化性质。[结果

]

耕地面源磷流失强度的特征波段为650~670 nm，1 475 nm和1 680~1 695 nm，土壤主控因子是有机质，二者之间呈正相关；园地面源磷流失强度的特征波段为685~690 nm，710~720 nm，1 110~1 115 nm，1 150~1 155 nm和2 170 nm，主控因子是有机质、水分和Fe

，分别和面源磷流失强度呈负相关、正相关和负相关；有机质对耕地的面源磷流失强度的影响更加显著：耕地面源磷流失强度与光谱指数间的相关系数在1 685 nm处达到0.74，而园地条件下相关系数最高值在715 nm处仅为0.48。[结论

]

耕地面源磷流失主控因子为有机质，园地的主控因子为有机质、水分和Fe

。

Abstract

[Objective] This study was to simplify the estimation of phosphorus loss intensity

through identifying key soil properties affecting non-point source phosphorus loss by spectral analysis

in agricultural land of Taihu Basin. [Methods] To identify the key soil physical and chemical properties affecting non-point source phosphorus loss

the spectral characteristics of soil from arable land and orchard land at Meiliang Bay watershed

Taihu Basin were contrasted. [Results] The characteristic bands of non-point source phosphorus loss were 650~670 nm

1 475 nm and 1 680~1 695 nm in arable land

suggesting that soil organic matter was the main factor controlling non-point source phosphorus loss due to the positive correlation between soil organic matter content and non-point source phosphorus loss intensity. The characteristic bands of non-point source phosphorus loss were 685~690 nm

710~720 nm

1 110~1 115 nm

1 150~1 155 nm and 2 170 nm in orchard land

which suggested that soil organic matter

moisture and Fe2+ were the main controlling factors of non-point source phosphorus loss intensity. Non-point source phosphorus loss intensity was negatively correlated with soil organic matter content and Fe2+

while it was positively correlated with water content. The effect of the source phosphorus loss on the arable land was more significant. The correlation coefficient between the surface source phosphorus loss intensity and the spectral index reached 0.74 at 1 685 nm in arable land. The highest correlation coefficient was only 0.48 at 715 nm in orchard land. [Conclusion] The key soil property in arable land was soil organic matter

and the key soil properties in orchard land was soil organic matter

moisture and Fe2+.

关键词

Keywords

references

Lin Chen, Wu Zhipeng, Ma Ronghua, et al. Detection of sensitive soil properties related to non-point phosphorus pollution by integrated models of SEDD and PLOAD[J]. Ecological Indicators, 2016,60:483-494.

陈玲.香溪河流域典型坡耕地氮磷流失机理研究[D].湖北宜昌:三峡大学,2013.

郑光辉.江苏部分地区土壤属性高光谱定量估算研究[D].江苏南京:南京大学,2010.

燕守勋,张兵,赵永超,等.矿物与岩石的可见近红外光谱特性综述[J].遥感技术与应用,2003,18(4):191-201.

Weber B, Olehowski C, Knerr T, et al. A new approach for mapping biological soil crusts in semi-desert areas with hyperspectral imagery[J]. Remote Sensing of Environment, 2008,112(5):2187-2201.

Bartholomeus H M, Schaepman M E, Kooistra L, et al. Spectral reflectance based indices for soil organic carbon quantification[J]. Geoderma, 2008,145(1/2):28-36.

Ladoni M, Bahrami H A, Alavipanah S K, et al. Estimating soil organic carbon from soil reflectance:a review[J]. Precision Agriculture, 2010,11(1):82-99.

Reeves J B. Near-versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis:Where are we and what needs to be done[J]. Geoderma, 2010,158(1/2):3-14.

Rossel R A V, Adamchuk V I, Sudduth K A, et al.Proximal soil sensing:An effective approach for soil measurements in space and time[J]. Advances in Agronomy, 2011,113(113):243-291.

纪文君,史舟,周清,等.几种不同类型土壤的VIS_NIR光谱特性及有机质响应波段[J].红外与毫米波学报,2012,31(3):277-282.

彭杰,周清,张杨珠,等.有机质对土壤光谱特性的影响研究[J].土壤学报,2013,50(3):517-524.

郑光辉,王明江,焦彩霞,等.土壤有机质高光谱估算研究进展[J].南京信息工程大学学报,2013,5(6):481-486.

史舟,王乾龙,彭杰,等.中国主要土壤高光谱反射特性分类与有机质光谱预测模型[J].中国科学(D):地球科学,2014,44(5):978-988.

马创,申广荣,王紫君,等.不同粒径土壤的光谱特征差异分析[J].土壤通报,2015,46(2):292-298.

张娜,张栋良,李立新,等.基于高光谱的区域土壤质地预测模型建立与评价:以河套灌区解放闸灌域为例[J].干旱区资源与环境,2014,28(5):67-72.

Martin P D, Malley D F, Manning G, et al. Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy[J]. Canadian Journal of Soil Science, 2002,82(4):413-422.

Luce M S, Ziadi N, Zebarth B J, et al. Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy[J]. Geoderma, 2014,232:449-458.

Lin Chen, Ma Ronghua, Zhu Qing, et al. Using hyper-spectral indices to detect soil phosphorus concentration for various land use patterns[J]. Environ. Monit. Assess, 2015,187(1):4130-4140.

徐丽华,谢德体,魏朝富,等.紫色土土壤全氮和全磷含量的高光谱遥感预测[J].光谱学与光谱分析,2013,33(3):723-727.

USEPA. PLOAD 3.0 User's Manual[R]. January, 2001.

徐宪立,马克明,傅伯杰,等.植被与水土流失关系研究进展[J].生态学报,2006,26(9):3137-3143.

杨才敏.土壤有机质与水土流失的关系定量研究[J].水土保持研究,2008,15(5):177-179.

陈书琴,储昭升,胡社荣,等.巢湖周边表土中有机质、全氮和全磷空间分布及其相关性[J].环境科学,2012,33(9):3031-3037.

浏览量

1457

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

雨强和土地利用方式对豫西南山区有机质流失的影响

城乡融合背景下秦巴山区县域“三生”空间时空演变

基于CSLE模型的2024年福建省典型区域水土流失特征及其差异评估

2018—2022年陕西省关中地区土壤侵蚀时空分布

漓江东岸冠岩地下河流域果园石漠化分布特征及其演变规律