Modeling and analysis of spectral characteristic of soil water content in the salinized soil of Ebinur Lake Watershed
ZHANG Haiwei, ZHANG Fei, LI Zhe, JING Yunqing
1. College of Resources & Environmental Science, Xinjiang University, 830046, Urumqi, China; 2. Key Laboratory of Oasis Ecology, 830046, Urumqi, China; 3. General Institutes of Higher Learning Key Laboratory of Smart City and Environmental Modeling, Xinjiang University, 830046, Urumqi, China
摘要盐渍土表层盐分累积与土壤含水量有着直接的关系。为了建立干旱区盐渍土含水量高光谱遥感监测模型，本文以艾比湖流域不同含水量的盐渍土为研究对象，采用光谱反射率变换和多元统计分析（MSAM）方法，对土壤含水量的光谱特征进行分析和建模。结果表明：随着土壤含水量的增加，土壤反射率呈下降趋势；在一定范围内， 波长越长，土壤光谱反射率与含水量的相关性越高，其中1 937 nm处的土壤光谱反射率与含水量具有最高的相关性（r=-0.636）。对土壤光谱反射率进行8种光谱反射率变换后，在此基础上利用多元统计方法分析盐渍土的不同含水量与光谱之间的相关性，筛选敏感波段，建立关系模型。得出对数一阶微分（Logarithm First Order Differential）在波长2 024和2 357 nm建立的模型以及均方根一阶微分（Root Mean Square First Order Differential）在波长1 972和2 357 nm建立的模型最优，相关系数r分别为0.894和0.865。基于上述模型作者构建了一种耦合模型，其相关性r=0.926比对数一阶微分模型提高了0.032，比均方根一阶模型提高了0.061；因此，所构建的盐渍土含水量估算模型是可行的，可以为遥感反演提供理论参考，对高光谱遥感反演具有一定意义。
Abstract：[Background] With the development of spatial information science, the hyperspectral remote sensing becomes more and more important in nowadays. Studying on spectral characteristic of soil water content is an important work, for it is the base of monitoring remote sensing. This study aims to investigate the spectral characteristic of soil water content and the relationship between the hyperspectral data and the soil water content, to explore a rapid and accurate method for estimating soil water contents, and to establish the hyperspectral remote sensing monitoring model for saline soil water in the arid area of the Ebinur Lake Watershed. [Methods] This study took saline soil with different water contents in Ebinur Lake Watershed as the research object, used the spectral reflectance transformation and multivariate statistical analysis methods (MSAM) to analyze the spectral characteristic of soil water content, and built models. [Results] The result showed that with the increase of soil water content, the reflectance of soil declined. In a certain range, the longer the wavelength, the higher the correlation between the soil spectral reflectance and soil water content; the soil spectral reflectance at the wavelength of 1 937 nm (r=-0.636) had the highest correlation with water content. After 8 transformation of soil spectral reflectance, the correlation of logarithmic first order differential sensitive band of 2 357 nm was the best (r=-0.808 6). Subsequently, MSAM were applied to analyze the correlation between spectrum and salinized soil with different water contents, then the spectral sensitive band of the soil was screened, and the correlation model was established. The result indicated that the model set up by Logarithm First Order Differential at the wavelength of 2 024 nm and 2 357 nm and the Root Mean Square First Order Differential at the wavelength of 1 972 nm and 2 357 nm was the best, the correlation coefficient r was 0.894 and 0.865 respectively. Based on the above established models, the authors constructed a new model, and the correlation r of which was 0.926, increased 0.032 against the Logarithm First Order Differential model, and increased 0.061 compared to the Root Mean Square First Order Differential model. [Conclusions] Therefore, the estimation model of soil water content is feasible. In addition, this study provides a new model for the indoor hyperspectral estimation of soil water content in Ebinur Lake Watershed, and it also provides a theoretical and technical reference for the hyperspectral quantitative estimation of soil water content, which has certain guiding significance for hyperspectral remote sensing.
张海威1,2, 张飞1,2,3, 李哲1,2, 井云清1,2. 艾比湖流域盐渍土含水量光谱特征分析与建模[J]. 中国水土保持科学, 2017, 15(1): 8-14.
ZHANG Haiwei, ZHANG Fei, LI Zhe, JING Yunqing. Modeling and analysis of spectral characteristic of soil water content in the salinized soil of Ebinur Lake Watershed. SSWC, 2017, 15(1): 8-14.
李美婷,武红旗,蒋平安,等.利用土壤的近红外光谱特征测定土壤含水量[J].光谱学与光谱分析,2012,32(8):2117. LI Meiting, WU Hongqi, JIANG Pingan, et al. Measuring soil water content by using near infrared spectral characteristics of soil[J].Spectroscopy and Spectral Analysis, 2012,32(8): 2117.
[2]
中国土壤学会.中国土壤学在前进[M].北京:中国农业科技出版社,1995:9. Soil Science Society of China. Progress of soil science in China [M].Beijing: China's Agricultural Science and Technology Press, 1995:9.
[3]
ROBINOVE C J, CHAVEZ P S, GEHRING D, et al. Arid land monitoring using Landsat albedo difference images[J].Remote Sensing of Environment, 1981, 11: 133.
[4]
HENRICKSEN B L. Reflections on drought: Ethiopia 1983-1984[J]. International Journal of Remote Sensing, 1986, 7(11): 1447.
[5]
刘志明,张柏,晏明,等.土壤水分与干旱遥感研究的进展与趋势[J].地球科学进展, 2003, 18(4): 576. LIU Zhiming, ZHANG Bai, YAN Ming, et al. Some research advances and trends on soil moisture and drought monitoring by remote sensing [J]. Advance in Earth Sciences, 2003, 18(4):576.
[6]
LIU Huanjun, ZHANG Yuanzhi, ZHANG Bai. Novel hyper spectral reflectance models for estimating black-soil organic matter in Northeast China[J]. Environmental Monitoring and Assessment, 2009, 154(1/2/3/4): 147.
[7]
LI Y, DEMETRIADES-SHAH T H, KANEMASU E T, et al. Use of second derivatives of canopy reflectance for monitoring prairie vegetation over different soil backgrounds[J]. Remote Sensing of Environment, 1993, 44(1): 81.
[8]
LU Ning, ZHANG Zhi, GAO Yang. Recognition and mapping of soil salinization in arid environment with hyper spectral data[C].Proceedings. 2005 IEEE International Geo science and Remote Sensing Symposium, 2005. IGArSS'05. IEEE, 2005, 6: 4520.
[9]
孙小艳,常学礼,张宁,等.不同取样单元对干旱区绿洲小麦地上生物量光谱估算模型的影响[J]. 中国沙漠,2012,32(2): 568. SUN Xiaoyan, CHANG Xueli, ZHANG Ning, et al. Impact of different sampling units on ground spectral models for estimating wheat aboveground biomass in oases of arid zone[J].Journal of Desert Research, 2012, 32(2):568.
[10]
夏学齐, 季峻峰, 陈骏, 等. 土壤理化参数的反射光谱分析[J]. 地学前缘, 2009, 16(4): 354. XIA Xueqi, JI Junfeng, CHEN Jun, et al. Analysis of soil physical and chemical properties by reflectance spectroscopy [J]. Earth Science Frontiers, 2009, 16(4):354.
[11]
吴亚坤,杨劲松,李晓明.基于光谱指数与EM38 的土壤盐分空间变异性研究[J].光谱学与光谱分析,2009(4):1023. WU Yakun, YANG Jinsong, LI Xiaoming. Study on spatial variability of soil salinity based on spectral indices and EM38 readings[J]. Spectroscopy and Spectral Analysis, 2009,29(4):1023.
[12]
刘焕军,张柏,宋开山,等.黑土土壤水分光谱响应特征与模型[J].中国科学院研究生院学报, 2008, 25(4): 503. LIU Huanjun, ZHANG Bai, SONG Kaishan, et al. Soil moisture spectral analysis and its spectral model[J]. Journal of the Graduate School of the Chinese Academy of Sciences, 2008,25(4):503.
[13]
何挺,王静,程烨,等.土壤水分光谱特征研究[J].土壤学报,2006,43(6):1027. HE Ting, WANG Jing, CHENG Ye, et al. Soil water spectrum characteristic research[J].Acta Pedologica Sinica, 2006,43(6):1027.
[14]
翁永玲,戚浩平,方洪宾,等.基于PLSR方法的青海茶卡-共和盆地土壤盐分高光谱遥感反演[J].土壤学报,2010,47(6):1255. WENG Yongling, QIE Haoping, FANG Hongbin, et al. PLSR based hyper spectral remote sensing retrieval of soil salinity of Chaka·Gonghe basin in Qinghai province[J]. Acta Pedologica Sinica, 2010,47(6):1255.
[15]
姜红涛,特依拜,克里木,等.艾比湖流域NDWI变化及其与降水、温度的关系[J].中国沙漠,2014,34(6):1678. JIANG Hongtao, TIYIP, KELIMU, et al. Responses of NDVI to the variation of precipitation and temperature the Ebinur Lake Basin[J]. Journal of Desert Research, 2014, 34(6):1678.
[16]
张小龙. 艾比湖流域气候变化及其径流响应[J]. 盐湖研究,2011,19(2):11. ZHANG Xiaolong Climatic variation and runoff response in Ebinur Lake Catchment[J]. Journal of Salt Lake ResearcH,2011,19(2):11.
[17]
王昌佐,王纪华,王锦地,等.裸土表层含水量高光谱遥感的最佳波段选择[J]. 遥感信息, 2003(4): 33. WANG Changzuo, WANG Jihua, WANG Jindi, et al. The choice of best detecting band for hyper spectral remote sensing on surface water content of bare soil[J]. Remote Sensing Information, 2003. (4):33.
[18]
关红,贾科利,张至椅,等.盐渍化土壤光谱特征分析与建模[J]. 国土资源遥感, 2015, 27(2)100. GUAN Hong, JIA Keli, ZHANG Zhinan. Research on remote sensing monitoring model of soil salinization based on spectrum characteristic analysis[J]. Remote Sensing for Land and resources, 2015, 27(2):100.
[19]
赵振亮,特依拜,丁建丽,等.新疆典型绿洲土壤电导率和PH值的光谱响应特征[J].中国沙漠,2013,33(5):1413. ZHAO Zhenliang, TIYIP, DING Jianli, et al. Characteristics of spectral responding to soil electrical conductivity PH in the typical Oasis of Xinjiang[J]. Journal of Desert Research, 2013, 33(5):1413.
[20]
顾燕, 张鹰, 李欢. 基于实测光谱的潮滩土壤含水量遥感反演模型研究[J]. 湿地科学, 2013, 11(2): 167. GU Yan, ZHANG Ying, LI Huan, et al. Remote sensing retrieval model on soil moisture content of tidal flat based on measured spectra[J]. Wetland Science, 2012, 11(2):168.
[21]
刘曾媛,贡璐,张雪妮,等.克里雅河流域于田绿洲土壤酶活性c理化因子相关性分析[J].中国土壤与肥料, 2014,4(7):35. LIU Zeng yuan, GONG Lu, ZHANG Xueni, et al. Soil enzyme activities and their relationship with physicochemical factors in Yutian oasis of the Keriya river watershed[J]. Soil and Fertilizer Sciences in China,2014, 4(7):35.
2017, Vol. 15 
