基于高光谱信息的宁夏引黄灌区中低产田土壤水分和有机质含量估算

doi:10.13287/j.1001-9332.202311.013

应用生态学报 ›› 2023, Vol. 34 ›› Issue (11): 3011-3020.doi: 10.13287/j.1001-9332.202311.013

基于高光谱信息的宁夏引黄灌区中低产田土壤水分和有机质含量估算

丁启东¹, 王怡婧⁴, 张俊华^1,2,3*, 陈睿华⁵, 贾科利⁴, 李小林¹

¹宁夏大学生态环境学院, 银川 750021;
²宁夏大学西北退化生态系统恢复与重建教育部重点实验室, 银川 750021;
³宁夏大学西部土地退化与生态恢复国家重点实验室培育基地, 银川 750021;
⁴宁夏大学地理科学与规划学院, 银川 750021;
⁵西安煤航遥感信息有限公司, 西安 710199

收稿日期:2023-07-02 修回日期:2023-09-25 出版日期:2023-11-15 发布日期:2024-05-15
通讯作者: *E-mail: zhangjunhua728@163.com
作者简介:丁启东, 男, 1995年生, 硕士研究生。主要从事精准农业与土壤质量提升研究。E-mail: 2535643120@qq.com
基金资助:
国家重点研发计划项目(2021YFD1900602)、国家自然科学基金项目(42067003,42061047)和宁夏科技创新领军人才项目(2022GKLRLX02)

Estimation of soil water and organic matter content in medium and low yield fields of Ningxia Yellow River Irrigation area based on hyperspectral information.

DING Qidong¹, WANG Yijing⁴, ZHANG Junhua^1,2,3*, CHEN Ruihua⁵, JIA Keli⁴, LI Xiaolin¹

¹College of Ecology and Environmental Science, Ningxia University, Yinchuan 750021, China;
²Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwestern China of Ministry of Education, Ningxia University, Yinchuan 750021, China;
³Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China;
⁴College of Geographical Sciences and Planning, Ningxia University, Yinchuan 750021, China;
⁵Xi’an Meihang Remote Sensing Information Co., Ltd., Xi’an 710199, China

Received:2023-07-02 Revised:2023-09-25 Online:2023-11-15 Published:2024-05-15

摘要/Abstract

摘要： 准确获取土壤水分和有机质含量对中低产田土壤质量提升具有重要意义。为探讨分数阶微分(FOD)联合不同光谱指数对土壤水分和有机质含量的估算效果,本研究以宁夏引黄灌区中低产田土壤为对象,对野外实测高光谱反射率进行均方根变换后,采用0～2阶FOD处理(步长0.25),构建差值指数(DI)、比值指数(RI)、乘积指数(PI)、加和指数(SI)、广义指数(GDI)和氮平面域指数(NPDI),基于6种光谱指数与水分和有机质含量的相关系数来筛选最优光谱指数,建立基于偏最小二乘回归(PLSR)和支持向量机(SVM)的水分和有机质含量估算模型。结果表明: 经FOD变换后,水分和有机质含量与光谱信息间的相关性较原始光谱均得到有效提升,最大分别提升0.1785和0.1713。水分含量敏感波段主要在400～630和1350～1940 nm;有机质含量敏感波段主要在460～850、1530～1910和2060～2310 nm。SVM模型精度明显高于PLSR,基于1.75阶NPDI-SVM的水分含量估算模型精度最佳,其验证决定系数(R_p²)为0.970,均方根误差(RMSE)为1.615,相对分析误差(RPD)为4.211;基于0.5阶DI-SVM的有机质含量估算模型效果最佳,其R_p²、RMSE和RPD分别为0.983、0.701和5.307。本研究可为相似地区中低产田土壤水肥监测、质量提升和制图提供数据与技术支撑。

关键词: 分数阶微分, 光谱指数, 偏最小二乘回归, 支持向量机, 反距离权重法

Abstract: Accurately obtaining soil water and organic matter content is of great significance for improving soil qua-lity in croplands with medium to low yield. We explored the estimation effect of fractional order differentiation (FOD) combined with different spectral indices on soil water and organic matter content in medium and low yield croplands of Ningxia Yellow River Irrigation Area. After root mean square transformation of field measured hyperspectral reflectance, we used 0-2 FOD (with a step length of 0.25) to construct difference index (DI), ratio index (RI), product index (PI), sum index (SI), generalized difference index (GDI), and nitrogen planar domain index (NPDI) and to select the optimal spectral index based on the correlation coefficients between six spectral indices with soil water and organic matter contents. We constructed a model for estimating soil water and organic matter content based on partial least squares regression (PLSR) and support vector machine (SVM). The results showed that the correlation between soil water and organic matter content and spectral information was effectively improved after FOD transformation compared with the original spectrum, with maximum increases of 0.1785 and 0.1713, respectively. The soil water content sensitive bands were mainly in the range of 400-630 and 1350-1940 nm, while the sensitive bands of organic matter content were mainly at 460-850, 1530-1910, and 2060-2310 nm. The accuracy of SVM model was significantly higher than that of PLSR, and the soil water content estimation model based on 1.75-order NPDI-SVM reached the highest precision, with a validation determination coefficient (R_p²) of 0.970, root mean square error (RMSE) of 1.615, and relative percent deviation (RPD) of 4.211. The organic matter content estimation model based on 0.5 order DI-SVM had the best performance, with R_p², RMSE and RPD of 0.983, 0.701 and 5.307, respectively. Our results could provide data and technological support for soil water and nutrient monitoring, quality improvement, and graphics creating in similar area with medium to low yield fields.

Key words: fractional order differentiation, spectral index, partial least squares regression, support vector machine, inverse distance weighting

丁启东, 王怡婧, 张俊华, 陈睿华, 贾科利, 李小林. 基于高光谱信息的宁夏引黄灌区中低产田土壤水分和有机质含量估算[J]. 应用生态学报, 2023, 34(11): 3011-3020.

DING Qidong, WANG Yijing, ZHANG Junhua, CHEN Ruihua, JIA Keli, LI Xiaolin. Estimation of soil water and organic matter content in medium and low yield fields of Ningxia Yellow River Irrigation area based on hyperspectral information.[J]. Chinese Journal of Applied Ecology, 2023, 34(11): 3011-3020.

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基于高光谱信息的宁夏引黄灌区中低产田土壤水分和有机质含量估算

Estimation of soil water and organic matter content in medium and low yield fields of Ningxia Yellow River Irrigation area based on hyperspectral information.

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