河套灌区土壤盐渍化微波雷达反演
目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重,动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而有潜力的方法。快速获取大范围地表土壤盐渍化的空间分布是一个迫切急需解决的科学难题。该文目的是试验与评价C波段RADARSAT-2 SAR（synthetic aperture radar）数据反演土壤盐渍化的性能。以受盐渍化影响较严重的内蒙古河套灌区解放闸灌域为试验区,基于SAR后向散射系数和土壤盐分实测值,利用多元线性回归（multiple linear regress,MLR）、地理加权回归（geographically weig...
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| Published in | 农业工程学报 Vol. 32; no. 16; pp. 109 - 114 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
内蒙古农业大学水利与土木建筑工程学院测绘工程系,呼和浩特,010018%加拿大约克大学地球空间科学与工程系，加拿大 安大略省 多伦多 M3J1P3
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2016.16.016 |
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| Abstract | 目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重,动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而有潜力的方法。快速获取大范围地表土壤盐渍化的空间分布是一个迫切急需解决的科学难题。该文目的是试验与评价C波段RADARSAT-2 SAR（synthetic aperture radar）数据反演土壤盐渍化的性能。以受盐渍化影响较严重的内蒙古河套灌区解放闸灌域为试验区,基于SAR后向散射系数和土壤盐分实测值,利用多元线性回归（multiple linear regress,MLR）、地理加权回归（geographically weighted regression,GWR）和BP人工神经网络（back propagation artificial neural networks,BP ANN）方法建立土壤含盐量的定量反演模型,重点构建了8∶140∶1结构的3层BP ANN模型,经模型验证发现MLR、GWR模型均偏向于弱相关,其标准误差SE分别为0.55、0.47 mg/g,而ANN（BP）模型的内部、外部检验标准误差SE分别为0.24、0.33 mg/g,优于前2种模型,其反演的盐渍化面积占比65.4%,与地面验证结果基本一致。该文建立的考虑土壤水分影响、组合雷达后向散射系数反演土壤盐分的人工智能模型,无需复杂的介电常数模型,能够在一定程度上满足土壤盐渍化监测的需要,可促进微波遥感在土壤盐渍化监测中的开拓应用。 |
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| AbstractList | S156.4; 目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重，动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而有潜力的方法。快速获取大范围地表土壤盐渍化的空间分布是一个迫切急需解决的科学难题。该文目的是试验与评价 C 波段 RADARSAT-2 SAR（synthetic aperture radar）数据反演土壤盐渍化的性能。以受盐渍化影响较严重的内蒙古河套灌区解放闸灌域为试验区，基于 SAR 后向散射系数和土壤盐分实测值，利用多元线性回归（multiple linear regress，MLR）、地理加权回归（geographically weighted regression，GWR）和 BP 人工神经网络（back propagation artificial neural networks，BP ANN）方法建立土壤含盐量的定量反演模型，重点构建了8∶140∶1结构的3层 BP ANN 模型，经模型验证发现 MLR、GWR 模型均偏向于弱相关，其标准误差 SE 分别为0.55、0.47 mg/g，而 ANN（BP）模型的内部、外部检验标准误差 SE 分别为0.24、0.33 mg/g，优于前2种模型，其反演的盐渍化面积占比65.4%，与地面验证结果基本一致。该文建立的考虑土壤水分影响、组合雷达后向散射系数反演土壤盐分的人工智能模型，无需复杂的介电常数模型，能够在一定程度上满足土壤盐渍化监测的需要，可促进微波遥感在土壤盐渍化监测中的开拓应用。 目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重,动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而有潜力的方法。快速获取大范围地表土壤盐渍化的空间分布是一个迫切急需解决的科学难题。该文目的是试验与评价C波段RADARSAT-2 SAR（synthetic aperture radar）数据反演土壤盐渍化的性能。以受盐渍化影响较严重的内蒙古河套灌区解放闸灌域为试验区,基于SAR后向散射系数和土壤盐分实测值,利用多元线性回归（multiple linear regress,MLR）、地理加权回归（geographically weighted regression,GWR）和BP人工神经网络（back propagation artificial neural networks,BP ANN）方法建立土壤含盐量的定量反演模型,重点构建了8∶140∶1结构的3层BP ANN模型,经模型验证发现MLR、GWR模型均偏向于弱相关,其标准误差SE分别为0.55、0.47 mg/g,而ANN（BP）模型的内部、外部检验标准误差SE分别为0.24、0.33 mg/g,优于前2种模型,其反演的盐渍化面积占比65.4%,与地面验证结果基本一致。该文建立的考虑土壤水分影响、组合雷达后向散射系数反演土壤盐分的人工智能模型,无需复杂的介电常数模型,能够在一定程度上满足土壤盐渍化监测的需要,可促进微波遥感在土壤盐渍化监测中的开拓应用。 |
| Abstract_FL | The expanding trend of soil salinization has become more and more severe especially in arid and semi-arid areas in the northwest China. Therefore it is specially important to dynamically monitor the soil salinization in the arid and semi-arid areas scientifically, accurately and rapidly. Microwave remote sensing technique has become a promising method to detect and monitor the soil salinity due to its many advantages. The aim of this study was to investigate the capability of C-band RADARSAT-2 SAR (synthetic aperture radar) data in soil salinity estimation over agricultural fields. In this study, Jiefangzha zone of Hetao irrigation district, Inner Mongolia, China was selected as the study area. Based on the back-scatter coefficient value and soil salt content, this paper used 3 kinds of methods including the multiple linear regression (MLR), geographically weighted regression (GWR) and back propagation artificial neural network (BP ANN) to establish the quantitative inversion models of soil salt content. Soil salinity information was extracted from the RADARSAT-2 SAR data, which had a kind of fine four-polarization SLC (single look complex) format and were bought in 2013, and covered an area of 25 km × 25 km with 8 m ground resolution. Taking the spatial unevenness distribution of the saline soil into account, 69 sampling points were designed in the study area, and field digging depth of soil was 10 cm. Hand-held GPS (global position system) receiver was used to record the coordinates of sampling points, and the soil total soluble salt content was measured in the indoor. Mainly use the SAR Scape module of ENVI software to perform the radar image processing, including radiometric calibration, geometric correction, slant range turning and filtering. The four-polarization back-scatter coefficient values corresponding to the sampling points were extracted based on the previous results by the spatial analysis module of ArcGIS. Total salt content was took as dependent variable, four-polarization back-scatter coefficient values and soil moisture as independent variables, and the MLR and GWR salinity prediction models were established. Use the MLR module of SPSS and the GWR module of GWR4 software to deal with data respectively. The correlation of GWR model was higher than the MLR, but these 2 statistical models were not significant, and the inversion results were difficult to reflect the distribution of severe salinity and saline-alkali soil. Therefore the emphasis was focused on the building of the 8:140:1 structure of three-layer BP ANN model. Training set contained the data of 49 sampling points in the test area, the input layer was made up of sampling point coordinates, soil moisture, four-polarization back-scattering coefficients and their combined value, and the number of net neurons was 8; the output layer was a neuron corresponding to the total salt content of sample point. After a lot of tentatively computation, the optimal number of neurons in the hidden layer was selected as 140. The hidden layer of the three-layer BP ANN model used the hyperbolic tangent sigmoid activation function, the output layer utilized the linear activation function, the traingda function was selected to perform network learning and training, and the fitting target error was set as 0.1 mg/g. Then the BP model was applied to the RADARSAT-2 remote sensing images to achieve the high-precision quantitative inversion of soil salinity when the BP model inversion accuracy met the requirements of salinity prediction. Whole calculation process was implemented by the MATLAB neural network toolbox. It was found that the correlation of the MLR and GWR models was weak and their standard error (SE) was 0.55 and 0.47 mg/g respectively, but the SE of internal and external inspection using the ANN (BP) model was only 0.24 and 0.33 mg/g respectively, better than the other 2 models, whose salinity area accounted for about 65.4%, and was basically consistent with ground validation. The artificial intelligence inversion model of soil salt considered the moisture’s influence, was directly based on the polarization radar scattering coefficients and their composition, and had no complex dielectric constant model. So the ANN (BP) model can reduce the smoothing effect compared with the 2 traditional models and improve the accuracy and reliability of model predictions, which meets the needs of soil salinity monitoring to a certain extent, and can promote and develop the application of microwave remote sensing in the soil salinity monitoring. |
| Author | 刘全明 成秋明 王学 李相君 |
| AuthorAffiliation | 内蒙古农业大学水利与土木建筑工程学院测绘工程系,呼和浩特010018 加拿大约克大学地球空间科学与工程系,加拿大安大略省多伦多M3J1P3 |
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| Author_FL | Wang Xue Li Xiangjun Cheng Qiuming Liu Quanming |
| Author_FL_xml | – sequence: 1 fullname: Liu Quanming – sequence: 2 fullname: Cheng Qiuming – sequence: 3 fullname: Wang Xue – sequence: 4 fullname: Li Xiangjun |
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| Keywords | 土壤盐渍化 radar inversion 雷达 neural networks 神经网络 soils 反演 土壤 soil salinity |
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| Notes | 11-2047/S soils; neural networks; radar; soil salinity; inversion The expanding trend of soil salinization has become more and more severe especially in arid and semi-arid areas in the northwest China. Therefore it is specially important to dynamically monitor the soil salinization in the arid and semi-arid areas scientifically, accurately and rapidly. Microwave remote sensing technique has become a promising method to detect and monitor the soil salinity due to its many advantages. The aim of this study was to investigate the capability of C-band RADARSAT-2 SAR（synthetic aperture radar） data in soil salinity estimation over agricultural fields. In this study, Jiefangzha zone of Hetao irrigation district, Inner Mongolia, China was selected as the study area. Based on the back-scatter coefficient value and soil salt content, this paper used 3 kinds of methods including the multiple linear regression（MLR）, geographically weighted regression（GWR） and back propagation artificial neural network（BP ANN） to establish |
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| Publisher | 内蒙古农业大学水利与土木建筑工程学院测绘工程系,呼和浩特,010018%加拿大约克大学地球空间科学与工程系，加拿大 安大略省 多伦多 M3J1P3 |
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| Snippet | 目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重,动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而... S156.4; 目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重，动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布... |
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| SubjectTerms | 反演 土壤 土壤盐渍化 神经网络 雷达 |
| Title | 河套灌区土壤盐渍化微波雷达反演 |
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