基于光谱指数优选的土壤盐分定量光谱估测

郭鹏; 李华; 陈红艳; 刘亚秋; 盖岳峰; 任涛

doi:10.13961/j.cnki.stbctb.2018.03.031

您当前的位置：

首页 >

文章列表页 >

基于光谱指数优选的土壤盐分定量光谱估测

试验研究

- 基于光谱指数优选的土壤盐分定量光谱估测
- Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices
- 水土保持通报 2018年38卷第3期页码：193-199
- 作者机构：
  
  1. 山东农业大学资源与环境学院,山东,泰安,271018
  2. 山东菏泽水利工程总公司,山东,菏泽,274000
  3. 山东凯文科技职业学院,山东,济南,250200
  4. 山东颐通土地房地产评估测绘有限公司,山东,济南,250000
  5. 山东省泰安市农业局,山东,泰安,271018
- 作者简介：
- 基金信息：
  
  国家科技支撑计划“华北平原小麦－玉米轮作区高效施肥技术研究与示范”（2015BAD23B02）;山东农业大学“双一流”奖补资金资助（SYL2017XTTD02）;山东省重点研发计划盐渍土快速改良与地力培肥产品的研发与应用（2017CXGC0306）
- DOI：10.13961/j.cnki.stbctb.2018.03.031
  中图分类号：
- 纸质出版：2018
- 稿件说明：
移动端阅览
郭鹏, 李华, 陈红艳, 等. 基于光谱指数优选的土壤盐分定量光谱估测[J]. 水土保持通报, 2018,38(3):193-199.

GUO Peng, LI Hua, CHEN Hongyan, et al. Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices[J]. Bulletin of Soiland Water Conservation, 2018, 38(3): 193-199.
郭鹏, 李华, 陈红艳, 等. 基于光谱指数优选的土壤盐分定量光谱估测[J]. 水土保持通报, 2018,38(3):193-199. DOI： 10.13961/j.cnki.stbctb.2018.03.031.

GUO Peng, LI Hua, CHEN Hongyan, et al. Quantitative Spectral Estimation of Soil Salinity Based on Optimum Spectral Indices[J]. Bulletin of Soiland Water Conservation, 2018, 38(3): 193-199. DOI： 10.13961/j.cnki.stbctb.2018.03.031.

摘要

[目的

]

探索基于光谱指数的盐渍土盐分估测的最佳技术路线，为研究区土壤盐分定量、快速遥感监测提供理论基础和技术参考。[方法

]

以山东省垦利县为研究区，野外采样，获取盐分及其主要离子（Cl

，Na

，Ca

）含量及高光谱数据；然后采用2种思路：①先选取敏感波段，进而构建常见的5种光谱指数；②先任意两波段组合构建光谱指数，进而筛选敏感光谱指数。最后皆采用随机森林方法（random forest，RF）构建土壤盐分及其主要离子的光谱模型。[结果

]

基于筛选的敏感亮度指数（1 750，1 620 nm）的RF模型精度最高，作为研究区土壤盐分的最佳估测模型，亮度指数作为最佳光谱指数；思路②明确的特征光谱范围涵盖思路①筛选的敏感波段，更有利于光谱特征分析；思路②建模的结果明显优于思路①；确定最佳技术路线为：任意波段两两组合构建光谱指数后，利用相关分析筛选土壤盐分及其主要离子的敏感光谱指数，进而构建其RF模型。[结论

]

该技术路线适用于黄河三角洲地区土壤盐渍化信息的有效提取。

Abstract

[Objective] To explore the best technical route for salt salinity estimation based on spectral indices in order to provide theoretical basis and technical reference for the quantitative calculation and rapid remote sensing monitoring of soil salinity in the study area.[Methods] Taking Kenli County of Shandong Province as the study area

samples were collected in the field

the content of soil salt and its main ions(Cl-

Na+

Ca2+)were measured

and the hyperspectra were obtained. Two different methods were used to select the sensitive spectral indices. The first one was to select the sensitive bands of salt and its major ions and then to build five spectral indices. The second one was to combine any two bands and to construct the five spectral indices

and the sensitive spectral indices were then filtered. The random forest(RF) method was used to build quantitative hyperspectral models of soil salinity and ions contents.[Results] The RF model of brightness spectral indices(1 750

1 620 nm)exhibited the best precision

thus it was the best estimation model of soil salinity in the study area

and the brightness spectral index was the best spectral index. The characteristic spectral range based on the second method covered the selected sensitive bands based on the first method

thus was more conducive to the spectral characteristics analysis. Meanwhile

the salt prediction model built based on the second method was better than that on the first one. Therefore

the best technical route was to construct the spectral indices by combination of any two bands firstly

then to select the sensitive spectral index of soil salinity and its main ions by correlation analysis

finally to build the RF model.[Conclusion] The technical route is suitable for the extraction of soil salinization information in the Yellow River delta.

关键词

Keywords

references

安德玉,赵庚星,常春艳,等.基于野外高光谱的黄河三角洲滨海盐渍土盐分含量估测研究[J].土壤通报,2015,6(4):843-850.

刘亚秋,陈红艳,王瑞燕,等.基于可见/近红外黄河口区土壤盐分及其主要离子的定量分析[J].中国农业科学,2016,49(10):1925-1935.

Weng Yongling, Gong Peng, Zhu Zhiling. A spectral index for estimating soil salinity in the Yellow River delta region of China using EO-1 hyperion data[J]. Pedosphere, 2010,20(3):378-388.

吕真真,刘光明,杨劲松.新疆玛纳斯河流域土壤盐分特征研究[J].土壤学报,2013,50(2):290-295.

Dehni A, Lounis M. Remote sensing techniques for salt affected soils mapping:Application to the Oran Region of Algeria[J]. Procedia Engineering, 2012,33(1):188-198.

代希君,彭杰,张艳丽,等.基于光谱分类的土壤盐分含量预测[J].土壤学报,2016,53(4):910-917.

王丽娜,朱西存,刘庆,等.黄河三角洲盐碱土盐分的高光谱定量估测研究[J].土壤通报,2013,44(5):1101-1106.

朱赟,申光荣,王紫君,等.土壤修复过程中盐含量及其光谱特征分析研究[J].光谱学与光谱分析,2017,37(5):1507-1513.

张同瑞,赵庚星,高明秀,等.基于近地面多光谱的黄河三角洲典型地区土壤含盐量估算研究[J].光谱学与光谱分析,2016,36(1):248-253.

蔡东全.潍坊海咸水入侵区土壤盐分高光谱估测研究[D].济南:山东师范大学,2016.

黄帅,丁建丽,李相,等.土壤盐渍化高光谱特征分析与建模[J].土壤通报,2016,47(5):1043-1047.

蒲智,于瑞德,尹昌应,等.干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究[J].水土保持通报,2012,32(6):130-133.

彭杰,王加强,向红英,等.土壤含盐量与电导率的高光谱反演精度对比研究[J].光谱学与光谱分析,2014,34(2):510-514.

贾永倩,王振锡,吴智乐,等.杏树落果前后叶绿素含量的高光谱估算模型[J].森林工程,2016,32(6):1-9.

陈红艳,赵庚星,李希灿,等.小波分析用于土壤速效钾含量高光谱估测研究[J].中国农业科学,2012,45(7):1425-1431.

王丽爱,马昌,周旭东,等.基于随机森林回归算法的小麦叶片SPAD值遥感估算[J].农业机械学报,2015,26(1):259-265.

Mashimbye Z E, Cho M A, Nell J P, et al. Model-based integrated methods for quantitative estimation of soil salinity from hyperspectral remote sensing data:A case study of selected South African soils[J]. 2012,22(5):640-649.

Lee S, Chol H, Cha K, et al. Random forest as a potential multivariate method for near-infrared (NIR) spectroscopic analysis of complex mixture samples:gasoline and naphtha[J]. Microchemical Journal, 2013,110:739-748.

马驰.基于HJ-1A高光谱影像的土壤盐碱化遥感研究[J].干旱区资源与环境,2014,28(2):180-184.

Bouaziz M,Matschullat J,Gloaguen R. Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil[J]. Comptes Rendus Geoscience, 2011,343(11):795-803.

史舟.土壤地面高光谱遥感原理与方法[M].北京:科学出版社,2014:46-47.

Minasny B, McBratney A B, Bellon-Maurel V, et al. Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon[J]. Geoderma, 2011,167:118-124

翁永玲,戚浩平,方洪宾,等.基于PLSR方法的青海-共和盆地土壤盐分高光谱遥感反演[J].土壤学报,2010,47(6):1256-1263.

关红,贾科利,张至楠.采用高光谱指数的龟裂碱土盐碱化信息提取与分析[J].红外与激光工程,2010,43(12):4154-4158.

浏览量

1152

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

2000—2023年吉林省黑土核心保护区撂荒耕地遥感监测及水土保持效应

基于不同评价单元的滑坡易发性对比分析

2013—2023年三峡库区植被覆盖度时空变化及其驱动力

新疆焉耆盆地土壤盐渍化特征分析

黄河三角洲土地利用及景观格局的动态分析