总磷(TP)、总氮(TN)是水质富营养化的重要指标,亦是水质监测的主要参数.具有快速高效、无二次污染等特点光谱法水质监测是当今水环境遥感分析研究的热点.针对水体TP、TN反演模型采用实验室标准液或野外全样本进行建模时,各种水质参数的相互影响及预测值超出建模样本值域的可能,使得实际的预测效果并不理想的情况.本文以白洋淀实验区的实际水体样本为反演模型的输入值,在确定最优相关波段和最佳反演模型的基础上,讨论了5种不同浓度范围场景下的样本建模对反演模型的影响,同时剖析了模型对超出建模浓度值样本的预测能力.结果表明:建模样本浓度覆盖预测样本时,反演模型决定系数R2＞0.6,TP、TN浓度预测值的平均偏离度ARE＜20％;建模浓度高于预测样本时,R2在0.6左右,对超过建模浓度范围12％以内的预测值,其ARE＜25％;建模浓度低于预测值时,R2介于0.4-0.5,预测值超过建模样本浓度一倍时,ARE≤30％;建模样本浓度位于预测值两侧时,R2可达0.8,ARE＜25％;建模样本浓度值介于预测值之间时,0.45＜R2＜0.55,ARE＞35％.通过本文的研究与讨论,可为水质参数监测的实际工程应用提供科学依据和参考.

Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water. The model consists of four main components: feature multi-level attention module, spatial graph convolution module, temporal-domain residual decomposition module, and feature fusion and output module. Through the organic combination of these four modules, FMA-STGRN can more effectively explore the complex spatio-temporal correlation relationships between water quality monitoring stations and more accurately integrate and utilize external influencing factors, thereby improving the prediction accuracy of ammonia nitrogen concentration in water. Experimental results show that the FMA-STGRN model outperforms other benchmark models such as RF, MART, MLP, LSTM, GRU, ST-GCN, and ST-GAT in various aspects. In addition, a series of feature ablation experiments were conducted to further reveal the key contributions of meteorological factors and point of interest data to the model performance. Overall, our research provides a powerful and practical tool for water quality monitoring and urban water management, with broad application prospects.

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH3N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH3N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.

Total suspended sediments (TSS) are an important water quality indicator. The spatiotemporal distribution patterns of TSS concentrations in summer in the Peace-Athabasca Delta (PAD) remain unclear between 2000 and 2020. Among empirical and machine learning models, the random forest (RF) model exhibits the best performance for the PAD with a coefficient of determination, normalized root-means-square error, and relative root-mean-square error of 0.92, 7.51%, and 27.36%, respectively. Our study analyzes the spatiotemporal TSS distribution based on 21 years of Landsat remote sensing data and the RF model. The results showed that the TSS concentrations of the Peace River were elevated in 2000, 2016, 2019, and 2020. The long-term TSS concentration distribution in the PAD showed a decreasing trend from west to east, and the TSS concentrations ranged from 6 to 80 mg/L level and remained stable in Lake Athabasca during the investigated period. Moreover, 57.14% of the PAD area exhibited no significant change between 2000 and 2020. The results of our study provide detailed information for the local government on the TSS concentrations in the PAD. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha−1. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.

Total phosphorus (TP) is a significant indicator of water eutrophication. As a typical macrophytic lake, Lake Baiyangdian is of considerable importance to the North China Plain’s ecosystem. However, the lake’s eutrophication is severe, threatening the local ecological environment. The correlation between chlorophyll and TP provides a mechanism for TP prediction. In view of the absorption and reflection characteristics of the chlorophyll concentrations in inland water, we propose a method to predict TP concentration in a macrophytic lake with spectral characteristics dominated by chlorophyll. In this study, water spectra noise is removed by discrete wavelet transform (DWT), and chlorophyll-sensitive bands are selected by gray correlation analysis (GRA). To verify the effectiveness of the chlorophyll-sensitive bands for TP concentration prediction, three different machine learning (ML) algorithms were used to build prediction models, including partial least squares (PLS), random forest (RF) and adaptive boosting (AdaBoost). The results indicate that the PLS model performs well in terms of TP concentration prediction, with the least time consumption: the coefficient of determination (R2) and root mean square error (RMSE) are 0.821 and 0.028 mg/L in the training dataset, and 0.741 and 0.029 mg/L in the testing dataset, respectively. Compared with the empirical model, the method proposed herein considers the correlation between chlorophyll and TP concentration, as well as a higher accuracy. The results indicate that chlorophyll-sensitive bands are effective for predicting TP concentration.

Water quality monitoring is very important in agricultural catchments. UV-Vis spectrometry is widely used in place of traditional analytical methods because it is cost effective and fast and there is no chemical waste. In recent years, artificial neural networks have been extensively studied and used in various areas. In this study, we plan to simplify water quality monitoring with UV-Vis spectrometry and artificial neural networks. Samples were collected and immediately taken back to a laboratory for analysis. The absorption spectra of the water sample were acquired within a wavelength range from 200 to 800 nm. Convolutional neural network (CNN) and partial least squares (PLS) methods are used to calculate water parameters and obtain accurate results. The experimental results of this study show that both PLS and CNN methods may obtain an accurate result: linear correlation coefficient (R-2) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R-2 between predicted value and true values of TSS concentrations is 0.827 with PLS model and 0.915 with CNN model. CNN method may obtain a better linear correlation coefficient (R-2) even with small number of samples and can be used for online water quality monitoring combined with UV-Vis spectrometry in agricultural catchment.

随着城市化发展和经济增长，水污染成为制约城市可持续发展的关键因素。传统的水质检测方法耗时长、容易造成二次污染，水质在线监测可实现水质的自动、快速、实时、原位检测，而卫星数据可为大范围、长时序的水质监测提供数据源。本文以中国内陆水体实时在线监测需求为出发点，将水质光谱在线监测系统和多源遥感数据结合起来，开展星地协同水质监测系统研发及应用研究，同时与地面实时在线监测数据进行数据交叉验证，提升长时序、大范围水域水质参数反演的精度和稳定性。

作物精准识别和分类是农业遥感检测的重要内容,对作物长势监测以及估产十分重要.以美国混合农业带为研究区,基于Sentinel-2时间序列影像,根据其传感器响应函数计算了针对Sentinel-2的通用归一化植被指数(Universal Normalized Vegetation Index,UNVI),并通过两个对比实验,分析UNVI等6个指数在作物精准分类中的性能.实验一以JM(Jeffries-Matusita)距离为指标对不同作物类别之间的可分性进行分析,结果表明UNVI优于NDVI、EVI、WDRVI、NDre1和NDWI指数,在玉米和棉花、玉米和水稻、玉米和水稻的区分上,UNVI优于其他指数区分能力相当,但在其余的作物组合上如棉花和水稻,NDVI等指数则无法将其很好的区分,此时UNVI指数依然可以表现出较好的区分能力;实验二对6种时间序列指数特征分别使用随机森林和支持向量机进行作物分类,结果表明UNVI指数的总体精度和Kappa系数最高,其次是NDre1指数和WDRVI指数,EVI的总体精度和Kappa系数最低,这表明UNVI比其他6个指数更好地区分了研究区大豆、玉米、棉花和水稻等4种主要作物.综上,基于Sentinel-2时间序列的UNVI指数在进行作物分类时与其他5种遥感植被指数相比,具有较大的优势,UNVI可为农作物长势分析和作物估产研究等农业研究和应用的可选植被指数.

利用溶液中物质的分子或离子对紫外-可见光全谱段的吸收特性来定性、定量研究水质参数的光谱分析方法,具有检测速度快、成本低、原位测量、无二次污染、可实现水质的多参数同时在线监测等优点.在论述水质光谱分析理论依据的基础上,系统分析了各种测量方式的原理和各自特点,通过对比国内外全谱段水质在线监测设备,指出了建立高精度在线水质参数反演的关键技术难点,进一步展望了水质光谱多参数在线监测系统的发展趋势.为基于光谱分析理论的水环境监测技术研究和水质参数检测仪器开发提供参考.