Retrieval of non-optically active water quality parameters by hyperspectra for river network waters in the Pearl River estuary

Field spectra and water quality measurements were performed in Zhongshan City and its adjacent river network in the Guangdong-Hong Kong-Macao Greater Bay Area.The hyperspectral reflectance characteristic of the river network and its correlation with the concentration of two non-optical activity parameters Chemical Oxygen Demand (COD_(Cr)) and Total Phosphorus (TP) were analyzed.The results showed that the spectral reflectance of the river network was mainly affected by suspended particulate matter and chlorophyll-a.In the range of 500~680 nm,the spectral reflectance of the water had negative correlations with the concentrations of COD_(Cr) and TP.Compared with the single band,the reflectance ratio of the specific band had higher correlations with COD_(Cr) and TP concentrations.The reflectance ratio band combinations with the highest correlations with COD_(Cr) and TP concentrations are R_(675)/R_(794) and R_(690)/R_(815),respectively.The water quality retrieval models that depend on the band combination show good estimation accuracy,with 27.2% and 32.1% of the average relative errors,indicating that the hyperspectral technology has good potential application for non-optical activity parameters (COD_(Cr) and TP) retrieval in the river network of the Pearl River estuary

Using Hyperspectral Imagery and GA-PLS Algorithm to Estimate Chemical Oxygen Demand Concentration of Water in River Network

【Objective】The hyperspectral remote sensing has proven potential to monitor water quality, but issues such as data redundancy and susceptibility to environmental variation could affect its accuracy and reliability. The genetic algorithm-partial least squares (GA-PLS) algorithm with a function to select sensitive spectral variables could resolve these problems. The GA-PLS algorithm was mainly used in retrieval of the optically active parameters such as transparency, chlorophyll-a, suspended matter and turbidity in surface water bodies. The purpose of this paper is to combine it with hyperspectral retrieval model to estimate chemical oxygen demand (COD) concentration of water in the river network in the Pearl River estuary.【Method】Hyperspectral imageries and COD concentration of 146 samples taken from water bodies in the Pearl River estuary were collected, and the characteristic bands of the hyperspectral reflectance data were screened using the GA-PLS algorithm to retrieve the COD concentration. The differences in retrieval accuracy between different band combinations were compared.【Result】The COD concentration retrieved from the hyperspectral imageries based on the GA-PLS algorithm is more accurate than that calculated using the full-spectrum PLS model. The minimum RMSEP of the method was 4.887 mg/L, 11.4% less than that of the full-spectrum PLS model. Using 74 filtered bands, accounting for 2.9% of the full bands, the model was still stable and accurate. Some characteristic bands obtained by the GA-PLS algorithm have physical interpretation, indicating that the screening results were rational.【Conclusion】The GA-PLS algorithm can be used to screen characteristic bands from the hyperspectral imageries to reduce the number of data and simplify the model as a result. It can accurately estimate COD of water in river networks

基于GA-PLS算法的河网水体化学需氧量高光谱反演

【目的】建立河网水体化学需氧量(COD)高光谱反演模型,验证遗传-偏最小二乘(GA-PLS)算法对建模效果的改善作用。【方法】采集广东省中山市146个点位的水体高光谱数据和COD质量浓度实测数据,通过GA-PLS算法对高光谱反射率数据进行特征波段筛选后建立COD质量浓度反演模型,并比较输入变量为不同特征波段组合时模型反演效果差异。【结果】基于GA-PLS算法的COD质量浓度高光谱模型反演效果优于全谱段PLS模型,验证集RMSEP最小为4.887 mg/L,较全谱段PLS模型降低11.4%;以筛选得到的74个波段(占全波段数的2.9%)作为输入变量时,模型仍可保持良好的稳定性和反演精度;GA-PLS算法筛选得出的部分特征波段与水体中藻类、悬浮颗粒物的吸收特征波段一致,筛选结果具有合理性和指示意义。【结论】通过GA-PLS算法可对高光谱数据进行特征波段筛选,实现数据降维优化,进一步简化模型;在样本COD质量浓度主要分布范围内,GA-PLS算法模型有良好的反演精度和水质类别分类准确性。该方法在河流COD快速监测中具有良好的应用前景

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies