Please wait a minute...
img

官方微信

高级检索
中国沙漠  2018, Vol. 38 Issue (2): 345-351    DOI: 10.7522/j.issn.1000-694X.2016.00157
生物与土壤     
基于离子光谱特征波段反射率的土壤碱化指标反演模型
朱跃晨1, 熊黑钢2, 朱忠鹏1, 张芳1
1. 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, 新疆 乌鲁木齐 830046;
2. 北京联合大学应用文理学院, 北京 100083
Soil Alkalization Index Inversion Model Based on Spectral Reflectance of Ions Characteristics Band
Zhu Yuechen1, Xiong Heigang2, Zhu Zhongpeng1, Zhang Fang1
1. College of Resource and Environmental Science/Key Laboratory of Oasis Ecology under Ministry of Education, Xinjiang University, Urumqi 830046, China;
2. College of Arts and Sciences, Beijing Union University, Beijing 100083, China
 全文: PDF(2163 KB)  
摘要: 以新疆奇台地区碱化土壤为研究对象,通过分析碱化土壤实测光谱反射率曲线与八大离子、pH、碱化指标相互间的相关关系,建立基于离子光谱特征波段反射率的各碱化指标一元及多元光谱反演模型,并对其精度进行验证。结果显示:Na+、CO32-、HCO3-含量与光谱反射率正相关,最高点的相关系数分别为0.710、0.798、0.749,而Ca2+、Mg2+含量与光谱反射率负相关,相关系数最高均不超过-0.370,反映出前3类离子含量与光谱反射率关系更为密切。SAR(钠吸附比)和ESP(碱化度)与Na+相关系数同为0.954,TA(总碱度)、RSC(残余碳酸钠)、pH与CO32-的相关系数分别为0.946、0.949和0.953,总体上Na+和CO32-含量对各碱化指标的影响更大。各碱化指标与土壤光谱反射率的相关性TA > RSC > ESP > pH > SAR;其中TA与光谱反射率的相关系数达到0.863。碱化指标TA的离子光谱特征波段反射率反演模型精度最好,其R2为0.703,比利用实测光谱反射率建立的pH反演模型的R2高约14%,说明前者精度更高,能更好地反映研究区内土壤的碱化程度。利用离子光谱特征波段反射率实现对土壤碱化的预测会成为今后研究的重点。
关键词: 离子光谱特征波段反射率土壤碱化碱化指标反演模型    
Abstract: The thesis takes the alkalized soil in Xinjiang Qitai area as the research object, and through the analysis of correlation between the measured spectral reflectance curves of alkalized soil and eight ions, pH, alkalization index, the unitary or polynary spectral inversion models based on the reflectivity of ionic spectral waveband are established, and the accuracy is verified. The results shows that:Na+, CO32-, HCO3- content and spectral reflectance are positively correlated, and the correlation coefficients of the highest point are respectively 0.710, 0.798 and 0.749, while the Ca2+, Mg2+ content and spectral reflectance are negatively correlated with the highest correlation coefficient less than -0.370, which reflects that the relation between the ion concentration of first three kinds and spectral reflectivity is closer. The correlation coefficient between SAR (Sodium Adsorption Ratio), ESP (Exchange Sodium Percentage) and Na+ is 0.954, the correlation coefficients between TA (Total Alkalinity), RSC (Residual Sodium Carbonate), pH and CO32- are respectively 0.946, 0.949 and 0.953. Therefore, Na+ and CO32- have greater effect on alkalization index on the whole. The correlation between alkalization indexes and soil spectral reflectance is TA > RSC > ESP > pH > SAR, and the correlation coefficient between TA and spectral reflectance reaches 0.863. The inversion model of spectral reflectance of ion spectrum characteristic band of alkalization index is the best, and its R2 is 0.703, which is 14 percent higher than the R2 of the pH inversion model based on measured spectral reflectance, this shows that the former has higher accuracy and can better reflect the degree of soil alkalization in the research area. At the same time, the use of spectral reflectance of ion characteristic band to predict soil alkalization will become the focus of future research.
Key words: spectral reflectance of ions character band    soil alkalization    alkalization index    inversion model
收稿日期: 2016-09-01 出版日期: 2018-03-20
ZTFLH:  S15  
基金资助: 国家自然科学基金项目(41171165);北京市属高等学校高层次人才引进与培养计划项目(IDHT20130322)
通讯作者: 熊黑钢(E-mail:heigang@buu.edu.cn)     E-mail: heigang@buu.edu.cn
作者简介: 朱跃晨(1991-),男,新疆克拉玛依人,硕士研究生,主要研究方向为干旱区资源与环境研究。E-mail:ZYC_612@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
朱跃晨
熊黑钢
朱忠鹏
张芳

引用本文:

朱跃晨, 熊黑钢, 朱忠鹏, 张芳. 基于离子光谱特征波段反射率的土壤碱化指标反演模型[J]. 中国沙漠, 2018, 38(2): 345-351.

Zhu Yuechen, Xiong Heigang, Zhu Zhongpeng, Zhang Fang. Soil Alkalization Index Inversion Model Based on Spectral Reflectance of Ions Characteristics Band. JOURNAL OF DESERT RESEARCH, 2018, 38(2): 345-351.

链接本文:

http://www.desert.ac.cn/CN/10.7522/j.issn.1000-694X.2016.00157        http://www.desert.ac.cn/CN/Y2018/V38/I2/345

[1] 王遵亲. 中国盐渍土[M].北京:科学出版社,1993.
[2] Amezketa E.An integrated methodology for assessing soil salinization,a pre-condition for land desertification[J].Journal of Arid Environments,2006,67(4):594-606.
[3] 郗金标,张福锁,田长彦,等.新疆盐生植物[M].北京:科学出版社,2006.
[4] 关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,5(1):46-52,86.
[5] 理查兹.盐碱土的鉴别和改良[M].北京:科学出版社,1965.
[6] 田兆顺.皖北"花碱土"的形成及其利用改良[J].土壤,1961(9):17-28.
[7] 李彬,王志春,迟春明.吉林省大安市苏打盐碱土碱化参数与特征分析[J].生态与农村环境学报,2006,22(1):20-23,28.
[8] 李述刚,王周琼.荒漠碱土[M].乌鲁木齐:新疆人民出版社,1988.
[9] 张杰,陈立新,乔璐,等.大庆市不同土壤类型盐碱化特征及评价[J].东北林业大学学报,2010,38(7):119-122.
[10] 孙建军,毛玉凤.总碱度与碳酸盐硬度及pH间的关系[J].东北水利水电,2014,32(4):46-48.
[11] 迟春明,王志春.松嫩平原苏打盐渍土钠吸附比的间接推算[J].干旱地区农业研究,2013,31(6):198-202.
[12] 孙军娜,董陆康,徐刚,等.糠醛渣及其生物炭对盐渍土理化性质影响的比较研究[J].农业环境科学学报,2014,33(3):532-538.
[13] 王军,顿耀龙,郭义强,等.松嫩平原西部土地整理对盐渍化土壤的改良效果[J].农业工程学报,2014,30(18):266-275.
[14] 杨军,孙兆军,刘吉利,等.脱硫石膏糠醛渣对新垦龟裂碱土的改良洗盐效果[J].农业工程学报,2015,31(17):128-135.
[15] Ben-Dor E,Patkin K,Banin A,et al.Mapping of several soil properties using DAIS-7915 hyperspectral scanner data-a case study over clayey soils in Israel[J].International Journal of Remote Sensing,2002,23(6):1043-1106.
[16] 张芳,熊黑钢,栾福明,等.土壤碱化的实测光谱响应特征[J].红外与毫米波学报,2011,30(1):55-60.
[17] 赵振亮,塔西甫拉提·特依拜,丁建丽,等.新疆典型绿洲土壤电导率和pH的光谱响应特征[J].中国沙漠,2013,33(5):1413-1419.
[18] 张芳,熊黑钢,龙桃,等.实测反射率与影像反射率对土壤碱化预测的对比分析[J].光谱学与光谱分析,2011,31(1):227-232.
[19] 王凯龙,熊黑钢,张芳.应用数字照片估算土壤pH的研究[J].光谱学与光谱分析,2014,34(3):771-776.
[20] 贾科利,张俊华,秦君琴.典型龟裂碱土光谱特征分析及碱化程度预测[J].干旱地区农业研究,2013,31(4):187-192,199.
[21] 张芳,熊黑钢,丁建丽,等.碱化土壤的野外及实验室波谱响应特征及其转换[J].农业工程学报.2012,28(5):101-107.
[22] 张芳,熊黑钢,安放舟,等.基于盐(碱)生植被盖度的土壤碱化分级[J].土壤学报,2012,49(4):665-672.
[23] 王凯龙,熊黑钢,张芳.基于高光谱数据预测土壤碱化程度最佳模型及其影响因素的研究[J].土壤,2014,46(3):544-549.
[24] 赵秀芳,杨劲松,姚荣江.基于典范对应分析的苏北滩涂土壤春季盐渍化特征研究[J].土壤学报,2010,47(03):422-428.
[25] 薛薇.基于SPSS的数据分析[M].北京:中国人民大学出版社,2011.
[26] 施龙青,徐东晶,邱梅,等.基于多元回归分析法预测断层防隔水煤柱宽度[J].煤炭科学技术,2013,41(6):108-110.
[27] 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.
[28] 徐彬彬,季耿善,朱永豪.中国陆地背景和土壤光谱反射特性的地理分区的初步研究[J].环境遥感,1991,6(2):142-151.
[1] 栾福明, 熊黑钢, 王芳, 张芳. 荒漠-绿洲交错带土壤N、P、K含量的高光谱反演模型[J]. 中国沙漠, 2014, 34(5): 1320-1328.
[2] 栾福明, 张小雷, 熊黑钢, 王芳, 张芳. 基于TM影像的荒漠绿洲交错带土壤有机质含量反演模型[J]. 中国沙漠, 2014, 34(4): 1080-1086.