岩溶区石灰土全氮含量高光谱反演研究

何文; 李艳琼; 余玲; 王金叶; 倪隆康; 李宁

doi:10.11932/karst20240505

岩溶区石灰土全氮含量高光谱反演研究

doi: 10.11932/karst20240505

何文^{1, 2,},
李艳琼³,
余玲⁴,
王金叶^{1, 5, ,},
倪隆康²,
李宁⁵

1.
桂林理工大学环境科学与工程学院, 广西桂林 541006
2.
广西喀斯特植物保育与恢复生态学重点实验室, 广西壮族自治区中国科学院广西植物研究所, 广西桂林 541006
3.
中国科学院华南植物园退化生态系统植被恢复与管理重点实验室, 广东广州 510650
4.
桂林航天工业学院, 广西桂林 541004
5.
南宁理工学院, 广西南宁 530100

基金项目: 广西自然科学基金项目（2024GXNSFAA010318）；广西重点研发计划项目（AB22035060）；国家自然科学基金（32060369）；广西植物研究所基本业务费（桂植业23005）；广西喀斯特植物保育与恢复生态学重点实验室资助（22-035-26）

详细信息

作者简介:
何文(1988 －)，男，助理研究员，博士研究生，主要从事生态环境遥感方面研究。E-mail：hewen0320@126.com

通讯作者:
王金叶(1965－)，男，教授，博士，主要从事生态经济、森林生态水文方面研究。E-mail：wangjinye66@163.com。

中图分类号: S153.6；S127
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出版历程
- 收稿日期: 2023-07-23
- 网络出版日期: 2024-12-30
- 刊出日期: 2024-10-25

Hyperspectral inversion of total nitrogen content in calcareous soil in karst areas

HE Wen^{1, 2
,},
LI Yanqiong³,
YU Ling⁴,
WANG Jinye^{1, 5
, ,},
NI Longkang²,
LI Ning⁵

1.
College of Environmental Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541006, China
2.
Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin, Guangxi 541006, China
3.
Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, Guangdong 510650, China
4.
Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China
5.
Nanning College of Technology, Nanning, Guangxi 530100, China

摘要

摘要: 石灰土是岩溶地区主要的土壤类型之一，准确快速估测石灰土全氮(TN)含量是科学评价岩溶区土壤环境质量的重要保障。文章以广西岩溶区石灰土为研究对象，对土壤光谱数据进行5种数学变换，对比分析偏最小二乘回归(PLSR)、广义神经网络(GRNN)以及二者组合(PLSR_GRNN)三种模型对土壤TN含量的高光谱反演能力。结果表明：(1)石灰土TN对光谱600 nm、1300 nm、1600 nm、1900 nm以及2300 nm附近波段反射率较为敏感；(2)对土壤原始光谱做微分变换、倒数对数变换以及包络线去除变换均在一定程度上能够提高光谱对石灰土TN含量的反演能力，并以微分变换效果最佳；(3)建立的PLSR_GRNN高光谱反演模型能够综合PLSR模型和GRNN模型的优点，反演精度较高，并以二阶微分变换（SDR）建立的反演模型效果最好，模型验证决定系数高达0.90，均方根误差仅为0.51，适合于岩溶区石灰土TN含量高光谱反演。基于高光谱模型能够对岩溶区石灰土TN含量进行快速、高精度反演，研究结果可为区域土壤修复和开发利用提供科学依据。
- 土壤全氮 /
- 石灰土 /
- 高光谱 /
- 偏最小二乘回归 /
- 广义回归神经网络
Abstract: Nitrogen is a component of many important compounds in plants, such as proteins, nucleic acids and enzymes, and hence is indispensable for the growth of plants. The nitrogen content in soil is one of the key indicators of soil fertility. Calcareous soil is one of the main soil types in karst areas. A rapid and accurate estimation of total nitrogen (TN) content in calcareous soil is an important guarantee for the scientific evaluation of soil environmental quality in karst areas. In recent years, the rapid development of hyperspectral remote sensing technology has brought new opportunities for a quick assessment of soil physical and chemical properties. However, it is still extremely challenging to rapidly assess soil nitrogen content in karst areas by hyperspectral remote sensing due to the weak optical signal of soil nitrogen and the interference of factors such as the complex ecological environment and the strong spatial heterogeneity of soil TN content in karst areas.Karst areas are extensively distributed in China, where calcareous soil is one of the main soil types, exerting a great influence on ecological protection and agricultural development. Therefore, it is of great theoretical and practical significance for us to develop hyperspectral inversion models suitable for the TN content of calcareous soil. Karst landforms are distributed across 70 counties/cities in Guangxi, with an area of 97,700 km², accounting for 41% of the total area of Guangxi and 10.8% of the total area of karst landforms in China. Taking calcareous soil in the karst areas of Guangxi as the research object, this study performed five mathematical transformations on soil spectra to improve the detection ability of spectral signals while eliminating spectral noise. Meanwhile, given the coexistence of linear and nonlinear relationships between soil TN content and spectra, the hyperspectral inversion capability of three models, namely partial least squares regression (PLSR), generalized neural network (GRNN) and PLSR_GRNN (a combined model of PLSR and GRNN), for soil TN content was compared and analyzed to establish a high-precision and rapid inversion model suitable for the TN content of calcareous soils in karst areas.The results showed as follows. (1) The TN content in calcareous soil was significantly correlated with various spectral bands from 400 to 2,500 nm. Among them, the TN content was more sensitive to the reflectance of the spectral bands near 600 nm, 1,300 nm, 1,600 nm, 1,900 nm and 2,300 nm. (2) The first-order differential transform (FDR), second-order differential transform (SDR), reciprocal logarithmic transform (lg(1/R)), reciprocal logarithmic first-order differential transform ((lg(1/R))') and envelope removal transform (CR) of the original soil spectra can improve the capability of inversion of TN content in calcareous soil to some extent. The transformation effects were roughly ordered by (lg(1/R))' > SDR > CR > FDR > lg(1/R). Overall, the spectral differential transform is superior to the envelope transform as well as the reciprocal logarithmic transform, and can better exploit the detection capability of the spectral signal for soil TN. (3) The PLSR algorithm had excellent predictive ability for the variation of TN content in calcareous soil. In the SDR transform case, the model had the highest accuracy and better model robustness without overfitting, with a coefficient of determination (R2) of 0.84 and root mean square error (RMSE) of 0.55 in the modeling set and R2 of 0.82 and RMSE of only 0.64 in the validation set. Compared with the PLSR algorithm, the GRNN model had greater prediction ability. However, the robustness of GRNN model was worse and the overfitting phenomenon was obvious. In the same SDR transformation case, the modeling set R2 of the GRNN model could reach 0.92, but the validation set R2 was only 0.59, so the overall performance was inferior to that of the PLSR model. (4) The PLSR_GRNN model can integrate the advantages of PLSR and GRNN model, maintaining the high predictability of GRNN model and avoiding the overfitting phenomenon. Among them, the best inversion model was established by SDR, with R2 of 0.92 and 0.90 for the modeling set and validation set, and RMSE of 0.43 and 0.51, respectively, which were suitable for hyperspectral inversion of TN content in calcareous soil in karst areas. In addition, the FDR, (lg(1/R))' and CR transformations also had excellent performance, with R2 above 0.80 for the modeling set and R2 above 0.75 for the validation set.Although the prediction accuracy of the GRNN model cannot be improved by combining the PLSR model with the GRNN model, the overfitting phenomenon can be effectively controlled. This modeling approach, which combines linear and nonlinear models, is more widely applicable than the PLSR model or GRNN model alone, and is more adaptable to more heterogeneous soil types, and will be more widely used. Rapid and high-precision prediction of TN content in calcareous soil in karst areas can be performed based on hyperspectral models. The results can provide a basis for regional soil remediation and utilization.
- soil total nitrogen /
- calcareous soil /
- hyper-spectrum /
- partial least squares regression /
- generalized regression neural network

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图 1 采样点分布示意图

注：底图来源于自然资源部标准地图服务。

Figure 1. Distribution of sampling points

Note: Base map was derived from the standard mapping service of Ministry of Natural Resources.

下载: 全尺寸图片幻灯片

图 2 不同TN含量的土壤光谱反射率

Figure 2. Soil spectral reflectance for different TN contents

下载: 全尺寸图片幻灯片

图 3 不同变换形式土壤光谱反射率与TN含量的相关系数

Figure 3. Correlation coefficient between TN content and soil spectral reflectance in different transformation forms

下载: 全尺寸图片幻灯片

图 4 基于PLSR_GRNN模型的土壤TN含量实测值与预测值比较（SDR）

Figure 4. Comparison of measured and predicted values of soil TN content with the PLSR_GRNN model (SDR)

下载: 全尺寸图片幻灯片

表 1 土壤氮元素计量特征

Table 1. Measurement characteristics of soil total nitrogen

最大值	最小值	均值	标准差	变异系数	峰度	偏度	K-S检验
6.80	1.05	3.34	1.33	40%	−0.08	0.65	0.20

下载: 导出CSV

表 2 土壤TN含量PLSR模型

Table 2. PLSR model for soil TN content

光谱指标	入选波段个数	主成分个数	建模集(n=40)		验证集(n=10)
光谱指标	入选波段个数	主成分个数	R²	RMSE	R²	RMSE	P
R	1128	2	0.24	1.23	0.17	1.34	0.15
FDR	705	6	0.79	0.65	0.81	0.65	0.00**
SDR	645	3	0.84	0.55	0.82	0.64	0.00**
lg (1/R)	408	2	0.37	1.12	0.24	1.29	0.09
(lg (1/R))′	584	6	0.77	0.67	0.82	0.67	0.00**
CR	559	4	0.63	0.86	0.57	1.03	0.00**
注：* 表示达到显著水平(P≤0.05)；** 表示达到极显著水平(P≤0.01)。 Note: * represents the significant level (P≤0.05); ** represents the extremely significant level (P≤0.01).

下载: 导出CSV

表 3 土壤TN含量GRNN模型

Table 3. GRNN model for soil TN content

光谱变换	入选波段个数	平滑因子	建模集(n=40)		验证集(n=10)
光谱变换	入选波段个数	平滑因子	R²	RMSE	R²	RMSE	P
R	1128	2.1	0.48	1.10	0.15	1.49	0.27
FDR	705	1.6	0.91	0.70	0.78	0.75	0.00**
SDR	645	3.5	0.92	0.62	0.59	1.03	0.01**
lg(1/R)	408	2.6	0.62	0.88	0.26	1.32	0.13
(lg(1/R))′	584	5.6	0.75	0.74	0.59	1.02	0.01**
CR	559	3.6	0.76	0.73	0.31	1.21	0.09
注：* 表示达到显著水平（P≤0.05）；** 表示达到极显著水平（P≤0.01）。 Note: * represents the significant level (P≤0.05); ** represents the extremely significant level (P≤0.01).

下载: 导出CSV

表 4 土壤TN含量PLSR_GRNN模型

Table 4. PLSR_GRNN model for soil TN content

光谱变换	主成分数	平滑因子	建模集(n=40)		验证集(n=10)
光谱变换	主成分数	平滑因子	R²	RMSE	R²	RMSE	P
R	2	0.3	0.47	1.05	0.32	1.25	0.09
FDR	6	1.0	0.91	0.74	0.85	0.83	0.00**
SDR	3	0.2	0.92	0.43	0.90	0.51	0.00**
lg(1/R)	2	0.2	0.60	0.92	0.55	1.04	0.01**
(lg(1/R))′	6	0.8	0.86	0.62	0.75	0.80	0.01**
CR	4	0.4	0.80	0.77	0.76	0.86	0.00**
注：* 表示达到显著水平（P≤0.05）；** 表示达到极显著水平（P≤0.01）。 Note: * represents the significant level (P≤0.05); ** represents the extremely significant level (P≤0.01).

下载: 导出CSV

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