自然资源遥感2026，Vol.38Issue(2)：41-49,9.DOI:10.6046/zrzyyg.2025050
稀疏样本下融合GHMM与高光谱遥感反演的高锰酸盐指数监测
A method for monitoring the permanganate index(CODMn)by integrating the Gaussian hidden Markov model and inversion of hyperspectral remote sensing data under sparse sample conditions
摘要
Abstract
This study aims to address the challenges in monitoring the permanganate index(CODMn)under conditions of sparsely distributed samples across a wide area.Hence,an integrated analytical method combining remote sensing images and time-series data was proposed,in which the inversion technique of remote sensing data and the Gaussian hidden Markov model(GHMM)are used for data analysis.Specifically,based on the high-accuracy GHMM prediction results,the current fluctuation trends of CODMn values are captured using the inversion technique,enabling the correction of the basic prediction results,thereby enhancing the overall accuracy.The experimental results show that compared to existing methods,the proposed method delivered higher accuracy and a higher coefficient of determination(R2).The CODMn inversion results exhibited a mean relative error of 0.177 mg·L-1,a root mean square error of 0.272 mg·L-1,a mean absolute percentage error of 7.276%,and a R2 value of 0.954.Therefore,the proposed method can achieve large-scale,multi-point,and high-accuracy monitoring of CODMn under the sparse distribution of initial water quality samples,providing scientific support and technical guidance for inland water body monitoring and pollution warning.关键词
高斯隐马尔可夫模型/遥感反演/水质监测/高锰酸盐指数Key words
Gaussian hidden Markov model(GHMM)/inversion of remote sensing data/water quality monitoring/permanganate index(CODMn)分类
信息技术与安全科学引用本文复制引用
温建华,曹里,蒋勇康,郭晶,李龙,钟述,邹阳..稀疏样本下融合GHMM与高光谱遥感反演的高锰酸盐指数监测[J].自然资源遥感,2026,38(2):41-49,9.基金项目
湖南省自然科学基金区域联合基金项目"大通湖营养状态参数多尺度高光谱遥感定量反演模型研究"(编号:2023JJ50357)和湖南省自然科学基金部门联合基金项目"天-空-地协同观察下的洞庭湖土壤高光谱遥感多尺度监测"(编号:2024JJ8353)共同资助. （编号:2023JJ50357）
