基于BP神经网络的不同人为干扰强度下盐渍土定量分析*

田安红, 付承彪, 熊黑钢, 赵俊三

田安红1,2, 付承彪1**, 熊黑钢3,4, 赵俊三2

(1. 曲靖师范学院信息工程学院 曲靖 655011; 2. 昆明理工大学国土资源工程学院 昆明 650093; 3. 北京联合大学应用文理学院 北京 100083; 4. 新疆大学资源与环境科学学院 乌鲁木齐 830046)

传统对土壤元素的反演模型经常采用线性的偏最小二乘模型(PLSR), 例如, 夏芳等[14]以浙江省36个县市的农田土壤为研究对象, 分析有机质与8种重金属的相关性, 并采用PLSR预测8种重金属的含量, 仿真结果表明PLSR对重金属Ni和Cr的预测效果较好, 其相对预测性能(RPD)值为1.8~2.0, 而剩余6种重金属预测模型的RPD值均为1.0~1.4。王文俊等[15]以山西的褐土为研究对象, 利用PLSR对20种高光谱变换后的预处理方法进行建模估算总氮含量, 仿真结果表明一阶导数预处理后建模能得到更好的预测结果, 且最佳的预处理方法为平均光谱曲线与标准差曲线的乘积, 其次为平均光谱曲线与平均光谱曲线的一阶导数、与标准差曲线的乘积, PLSR模型能对总氮进行有效的预测。然而, 土壤高光谱与土壤某元素间的关系表现为非线性, 传统线性PLSR对土壤元素的反演精度有限, 因此需要探索非线性的预测方法。

1 材料与方法

1.1 土壤样本采集

研究区位于新疆维吾尔自治区昌吉回族自治州境内, 87°44¢~88°46¢E, 43°29¢~45°45¢N, 距乌鲁木齐约70 km。该区域土壤盐渍化严重, 土壤表层的盐分含量为5.34~44.45 g×kg-1 [1], 夏季非常炎热, 降水稀少, 蒸发强烈, 年蒸发量高达2 000 mm。

图1 无人为干扰区(A)和人为干扰区(B)盐渍土采样点示意图

蓝色方框为水渠位置, 红色圆圈为农场位置, 黄色方框为无人为干扰区(A区), 绿色方框为人为干扰区(B区)。The blue box is the location of the canal, the red circle is the location of the farm, the yellow box is the undisturbed area (area A), and the green box is the human disturbing area (area B).

1.2 野外高光谱测量

55个样本点的野外高光谱采用FieldSpec®3 Hi- Res高精度地物光谱仪测量, 该仪器的波段范围300~2 500 nm。350~1 000 nm波段的采样间隔为1.4 nm, 1 000~2 500 nm波段的采样间隔为1 nm。野外测量时选择当地时间13:00—15:00, 且晴朗无风的天气进行。每次测量之前用白板进行光谱校正处理, 每个土壤样本点采用梅花桩采样法于5个方向重复采集10次高光谱, 测定高度为距离土壤表面15 cm。计算平均值为该样点的原始高光谱数据。同时, 因边缘波段(350~390 nm和2 401~2 500 nm)信噪比低及存在水分吸收带(1 355~1 410 nm和1 820~1 942 nm)的干扰, 删除这些波段范围的高光谱数据。

1.3 建立BP神经网络模型

1.4 模型精度评价方法

2 结果与分析

2.1 研究区盐渍土4种主要阴离子含量统计

表1 无人为干扰区和人为干扰区盐渍土4种阴离子含量描述性统计

2.2 不同含量盐渍土的高光谱曲线特征

图2 无人为干扰区(A区)和人为干扰区(B区)不同含量盐渍土壤样本的高光谱曲线图

2.3 不同人为干扰区盐渍土含量反演特征波段的选择

因此, 本研究将两种光谱变换在0阶、一阶和二阶微分中通过0.05检验的波段选择为特征波段, 研究区通过0.05显著性检验的波段数量个数如表2所示, 特征波段对应的高光谱值作为后续BP神经网络模型的输入变量。

图3 无人为干扰区(A区)和人为干扰区(B区)盐渍土高光谱与含量的相关系数

<|0.05|表示显著相关。<|0.05| indicates significant correlation.

表2 无人为干扰区和人为干扰区通过0.05显著性检验的盐渍土高光谱波段数量个数

R表示原始高光谱, LogR表示对数变换后的光谱。R is the original hyperspectral, LogR is logarithmic transformation of R.

2.4 不同人为干扰区盐渍土含量高光谱反演模型

表3 无人为干扰区和人为干扰区盐渍土含量高光谱反演模型的精度

RPD: 相对预测性能。RPD: relative prediction performance.

图4 无人为干扰区(a)和人为干扰区(b)盐渍土含量实测值和BP模型预测值的散点图

图中预测数据为高光谱对数二阶微分(LogR)的BP模型预测值。The predicted values are prediction results of BP model with spectral logarithmic transformation.

图5 无人为干扰区(a)和人为干扰区(b)盐渍土含量实测值与BP模型预测值的拟合效果

图中预测数据为高光谱对数二阶微分(LogR)的BP模型预测值。The predicted values are prediction results of BP model with spectral Logarithmic transformation.

3 结论

图6 无人为干扰区(a)和人为干扰区(b)盐渍土含量BP模型的训练过程

3)统计相关系数在0阶、一阶和二阶微分中通过0.05检验的波段数量, R变换在无人为干扰区分别为0个、38个和77个, 在人为干扰区分别为0个、39个和74个; LogR变换在无人为干扰区分别为1 822个、264个和121个, 在人为干扰区分别为1 659个、121个和86个。

4)无人为干扰区的最佳反演模型为二阶微分的LogR光谱变换对应的BP模型, 其RPD为3.309, 表明该模型的预测能力非常强。人为干扰区的最佳反演模型为一阶微分的LogR光谱变换对应的BP模型, 其RPD为2.234, 表明该模型的预测能力很好。

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Tian Anhong1,2, FU Chengbiao1**, XIONG Heigang3,4, ZHAO Junsan2

(1.College of Information Engineering, Qujing Normal University, Qujing 655011, China; 2. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China; 3. College of Applied Arts and Science, Beijing Union University, Beijing 100083, China; 4. College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China)

S151.9

10.13930/j.cnki.cjea.190700

* 国家自然科学基金项目(41901065, 41671198, 41761081)资助

付承彪, 主要从事遥感与地理信息系统的研究。E-mail: fucb305@163.com

田安红, 主要从事干旱区盐渍土的高光谱研究。E-mail: tianfucb@163.com

2019-09-26

2019-12-10

* This study was supported by the National Natural Science Foundation of China (41901065, 41671198, 41761081).

, E-mail: fucb305@163.com

Dec. 10, 2019

Sep. 26, 2019;