首页 | 官方网站   微博 | 高级检索  
     

基于不同卫星光谱模拟的土壤电导率估算研究
引用本文:曹肖奕,丁建丽,葛翔宇,梁静,陈文倩,陈香月,唐普恩.基于不同卫星光谱模拟的土壤电导率估算研究[J].干旱区地理,2020,43(1):172-181.
作者姓名:曹肖奕  丁建丽  葛翔宇  梁静  陈文倩  陈香月  唐普恩
作者单位:1新疆大学资源与环境科学学院,新疆乌鲁木齐830046; 2新疆大学绿洲生态教育部重点实验室,新疆乌鲁木齐830046; 3新疆大学智慧城市与环境建模自治区普通高校重点实验室,新疆乌鲁木齐830046
基金项目:黄河水沙变化基础数据仓库与挖掘分析(2016YFC0402409-03);国家自然科学基金项目(41771470)
摘    要:土壤电导率 (Electrical conductivity, EC)是评价土壤盐渍化的重要指标。通过实测新疆艾比湖湿地自然保护区土壤EC及可见光—近红外光谱数据,利用波谱响应技术模拟Landsat 8 OLI、Sentinel 2、Sentinel 3卫星的宽波段数据。构建宽波段模拟数据及其5种预处理后的三维光谱指数 (Three-dimensional spectral index, TDSI),采用梯度提升回归树算法 (Gradient boosting regression tree, GBRT) 建立3种卫星土壤EC估算模型,并比对加入TDSI后模型精度的变化。结果表明:在不同土壤EC条件下,3种卫星具有相似的光谱趋势,均在红、近红外波段附近反射率较高;TDSI与土壤EC相关性基本均在0.4以上,最大程度保留了与土壤EC敏感度高的红、绿、蓝、近红外、短波红外波段信息;GBRT对于土壤EC估算能力表现突出,3种卫星对土壤EC的最佳预测精度R2分别为0.831、0.847、0.903,在加入TDSI后,R2分别提高至0.835、0.857、0.935,综合分析发现,Sentinel 3对土壤EC估算效果最佳 (R2=0.935,均方根误差RMSE=2.986 mS·cm-1,赤池信息准则AIC=57.500)。通过利用波谱响应技术结合TDSI深度挖掘波段间的协同信息,采用GBRT验证了不同卫星对土壤R2的估算效果,二者相结合可以有效提升模型预测精度,为干旱区土壤盐渍化定量监测与防控提供有利指导。

关 键 词:波谱响应    土壤电导率    光谱预处理    三维光谱指数    梯度提升回归树  
收稿时间:2019-05-06

Estimation of soil conductivity based on spectral simulation of different satellites
CAO Xiao-yi,DING Jian-li,GE Xiang-yu,LIANG Jing,CHEN Wen-qian,CHEN Xiang-yue,TANG Pu-en.Estimation of soil conductivity based on spectral simulation of different satellites[J].Arid Land Geography,2020,43(1):172-181.
Authors:CAO Xiao-yi  DING Jian-li  GE Xiang-yu  LIANG Jing  CHEN Wen-qian  CHEN Xiang-yue  TANG Pu-en
Affiliation:1 College of Resources and Environmental Science,Xinjiang University,Urumqi 830046,Xinjiang,China; 2 Key Laboratory of Oasis Ecology under Ministry of Education, Xinjiang University, Urumqi 830046,Xinjiang,China; 3 Key Laboratory of Smart City and Environment Modelling of Higher Education Institute,Xinjiang University, Urumqi 800046,Xinjiang,China
Abstract:As one of the important environmental problems in the global arid region, soil salinization not only restricts agricultural production, but also causes serious land degradation. This problem has attracted wide attention from many scholars at home and abroad. In the recent stage, the monitoring and evaluation of soil salinization is mainly based on the use of different remote sensing data combined with different model inversion methods, and few research was seen on the integration of ground measured spectra and satellite remotely sensed data to obtain soil salinization information. Therefore, this study explores the effect of different satellite broadband simulations on soil conductivity and reflects soil salinization. In this study, the Ebinur Lake Wetland Nature Reserve in Xinjiang Uygur Autonomous Region, China was taken as an example.40 soil samples were collected in the field, and the soil conductivity (EC) and visible-near-infrared (VIS-NIR) spectral data were measured. The broadband data of the Landsat 8 OLI, Sentinel 2, and Sentinel 3 satellites were simulated by spectral response techniques. The three-dimensional spectral index was constructed by simulating wide-band data and its six spectral forms after five pretreatments: first-order differential (FD),second-order differential (SD),continuum removal (CR),absorbance conversion (ABS),and multivariate scattering correction (MSC).Three satellite soil EC estimation models were established by combining the gradient boosting regression tree (GBRT) algorithm, and the accuracy of the model estimation was compared after adding the three-dimensional spectral index. Then the soil estimation potential of the three satellites was analyzed. The results show that: Landsat 8 OLI, Sentinel 2, and Sentinel 3 have similar spectral trends under different soil EC conditions. With the increase of soil EC, the reflectance of each band of the three satellites is correspondingly increased, and both have high reflectance in red and near infrared. The correlation between TDSI and soil EC was basically above 0.4, and Sentinel 3 had the highest correlation based on SD pretreatment, which was-0.72. TDSI retains the information of red, green, blue, near-infrared and short-wave infrared bands sensitive to soil EC, which indicates that TDSI increases the effective information of soil EC, while taking into account the remote sensing mechanism, which has certain scientific significance. GBRT algorithm has outstanding performance for soil EC, estimation. Landsat 8 OLI, Sentinel 2, Sentinel 3 based on the estimation model of broadband simulation data produced the best prediction accuracy R2 as 0.831, 0.847 and 0.903 respectively. After adding the TDSI, the estimation accuracy of different spectrum forms of the three satellites has been significantly improved. The corresponding prediction accuracy R2 of the three satellites was increased to 0.83 5,0.857 and 0.935,respectively.The comprehensive analysis found that Sentinel 3 had the best estimation effect for soil EC(R2=0.935,RMSE=2.986mS·cm-1, AIC=57.500),followed by Sentinel 2(R2=0.857,RMSE=4.596 mS·cm-1, AIC=45.247),and finally Landsat 8 OLI (R2=0.835,RMSE=4.348 mS·cm-1, AIC=32.765).In this study, the spectral response technique combined with GBRT algorithm has been verified that Landsat 8 OLI, Sentinel 2 and Sentinel 3 satellites have a good estimation effect on soil EC in arid area. TDSI can deeply mine the synergy information between bands and improve the prediction accuracy of the model. The combination of the two methods could provide favorable guidance for quantitative monitoring and prevention of soil salinization in arid areas.
Keywords:spectral response  soil conductivity  spectral pretreatment  three-dimensional spectral index  gradient boosting regression tree  
点击此处可从《干旱区地理》浏览原始摘要信息
点击此处可从《干旱区地理》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号