中国农业科学 ›› 2019, Vol. 52 ›› Issue (10): 1698-1709.doi: 10.3864/j.issn.0578-1752.2019.10.004
王丹阳1,陈红艳1(),王桂峰2,丛津桥3,王向锋4,魏学文2
收稿日期:
2018-12-28
接受日期:
2019-03-05
出版日期:
2019-05-16
发布日期:
2019-05-23
通讯作者:
陈红艳
作者简介:
王丹阳,E-mail: 基金资助:
WANG DanYang1,CHEN HongYan1(),WANG GuiFeng2,CONG JinQiao3,WANG XiangFeng4,WEI XueWen2
Received:
2018-12-28
Accepted:
2019-03-05
Online:
2019-05-16
Published:
2019-05-23
Contact:
HongYan CHEN
摘要:
【目的】为提高土壤盐分信息定量遥感提取精度,准确掌握土壤盐渍化程度与分布。【方法】选择垦利区黄河口镇集中连片的重度盐渍土区域为试验区,于2018年4月26日—28日采用搭载Sequoia多光谱相机的无人机进行试验区近地遥感图像采集,并进行图像拼接、辐射校正、正射校正和几何校正等预处理;然后基于相关性分析、灰色关联度分析筛选土壤盐分的敏感波段,构建并筛选光谱参量;进而分别采用多元线性回归(multivariable linear regression,MLR)、支持向量机(support vector machine,SVM)及偏最小二乘(partial least square,PLS)方法构建土壤盐分定量反演模型,并进行验证与评价;最后基于最佳模型进行试验区土壤盐分的分布反演与分析,并与反距离加权插值结果进行精度比较。【结果】相较相关性分析,通过灰色关联度分析的反演模型精度及显著性均有所提高;对比3种建模方法,SVM模型精度最高,PLS模型次之,MLR模型最低,最佳模型为基于灰色关联度分析筛选变量的支持向量机模型,其建模R 2、RMSE分别为0.820、3.626,验证R 2、RMSE、RPD分别为0.773、4.960、2.200;据此模型反演得到该区域土壤盐分含量为0.323—21.210 g·kg -1,平均值为6.871 g·kg -1,重度盐渍土占58.094%,与实地调查结果较为一致;反演结果与反距离加权插值结果的误差80%控制在样本盐分含量平均值的20%以内,亦较为相近。 【结论】基于无人机多光谱可实现重度盐渍土盐分信息的准确提取。
王丹阳,陈红艳,王桂峰,丛津桥,王向锋,魏学文. 无人机多光谱反演黄河口重度盐渍土盐分的研究[J]. 中国农业科学, 2019, 52(10): 1698-1709.
WANG DanYang,CHEN HongYan,WANG GuiFeng,CONG JinQiao,WANG XiangFeng,WEI XueWen. Salinity Inversion of Severe Saline Soil in the Yellow River Estuary Based on UAV Multi-Spectra[J]. Scientia Agricultura Sinica, 2019, 52(10): 1698-1709.
表2
光谱指数分析"
序号 Number | 光谱指数 Spcetral index | 相关系数 Correlation coefficient | 灰色关联度指数 Grey correlation index |
---|---|---|---|
1 | r+nir | -0.645** | -0.720** |
2 | R×nir | -0.600* | -0.708** |
3 | r/g | -0.185 | -0.756** |
4 | R+nir+g | -0.641** | -0.665** |
5 | $\sqrt{r^{2}+reg^{2}}$ | -0.601** | -0.668** |
6 | g×nir | -0.562* | -0.658* |
7 | g+reg | -0.603* | -0.671* |
8 | (r+g)/(r-g) | -0.015 | -0.471* |
9 | g×r | -0.145 | -0.701* |
10 | $\sqrt{r^{2}+g^{2}}$ | -0.640** | -0.700** |
11 | r+nir+reg | -0.631** | -0.709** |
12 | r×reg | -0.591* | -0.649* |
13 | r+reg | -0.634** | -0.691** |
14 | (reg-r)/( reg+r) | 0.252 | -0.183 |
15 | g×nir×r | -0.556* | -0.603* |
16 | g+r+reg | -0.632* | -0.678* |
17 | g×r×reg | -0.037 | -0.576* |
18 | g×reg | -0.124 | -0.609* |
19 | (reg-g)/( reg+g) | 0.148 | -0.354 |
20 | r-g | -0.21 | -0.206 |
21 | reg-r | 0.048 | -0.079 |
22 | reg/r | -0.17 | -0.709* |
23 | $\sqrt{g^{2}+reg^{2}+r^{2}}$ | -0.601* | -0.668* |
24 | $\sqrt{r\times reg}$ | -0.634* | -0.689* |
25 | $\sqrt{r\times r}$ | -0.643* | -0.702* |
26 | $\sqrt{g^{2}+reg^{2}}$ | -0.605* | -0.669* |
27 | (reg+g)/(g-reg) | 0.117 | -0.238 |
28 | (r+g)/(r-g) | -0.015 | -0.471* |
29 | $\sqrt{reg\times g}$ | -0.631* | -0.679* |
30 | $\sqrt{reg \times r \times g}$ | -0.613* | -0.689* |
31 | r+nir+g+reg | -0.631* | -0.660* |
32 | r+nir+g | -0.641* | -0.665* |
33 | nir+g+reg | -0.606* | -0.652* |
表3
土壤盐分反演模型"
分析方法 Analytical methods | 建模方法 Modeling methods | 建模精度Calibration accuracy | 验证精度Verification accuracy | |||
---|---|---|---|---|---|---|
决定系数 R2 | 均方根误差 RMSE | 决定系数 R2 | 均方根误差 RMSE | 相对分析误差 RPD | ||
相关性分析 Correlation analysis | MLR | 0.645 | 5.217 | 0.564 | 5.157 | 1.261 |
SVM | 0.730 | 5.363 | 0.782 | 5.596 | 2.083 | |
PLS | 0.711 | 5.545 | 0.707 | 5.362 | 1.870 | |
灰色关联度 Grey correlation index | MLR | 0.691 | 4.291 | 0.687 | 5.013 | 1.731 |
SVM | 0.820 | 3.626 | 0.773 | 4.960 | 2.203 | |
PLS | 0.722 | 4.677 | 0.724 | 4.731 | 2.210 |
表4
试验区土壤盐分等级及其比例"
等级 Grade | 反演图Inversion map | 插值图 Interpolation map | ||
---|---|---|---|---|
像元数 Pixel count | 所占比例 Percentage (%) | 像元数 Pixel count | 所占比例 Percentage (%) | |
非盐渍土Non-saline soil (<2.0 g·kg-1) | 3027962 | 7.640 | 1025934 | 2.476 |
轻度盐渍土Mild saline soil (≥2.0-4.0 g·kg-1) | 1928771 | 4.871 | 7327621 | 17.691 |
中度盐渍土Moderate saline soil (≥4.0-6.0g·kg-1) | 10091297 | 25.473 | 11092173 | 26.779 |
重度盐渍土Severe saline soil (≥6.0-10.0 g·kg-1) | 23018069 | 58.094 | 20015014 | 48.321 |
盐土Solonchak (≥10.0 g·kg-1) | 1558977 | 3.932 | 1959849 | 4.731 |
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