Total inorganic nitrogen content distribution pattern in estuary region by means of hyperspectral remote sensing and water quality numeric simulation techniques

2008 ◽  
Author(s):  
Xiang Lin ◽  
Dong Zhang ◽  
Ying Zhang ◽  
Yong Xu ◽  
Huan Li
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Nitrogen Content ◽  
Distribution Pattern ◽  
Content Distribution ◽  
Inorganic Nitrogen ◽  
Hyperspectral Remote Sensing ◽  
Simulation Techniques ◽  
Numeric Simulation ◽  
Total Inorganic Nitrogen

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

Development of Air Supply Control Technology in Sidestream MLE Process by Measuring Conductivity

2020 ◽  
Vol 42 (3) ◽  
pp. 97-109
Author(s):  
Shinyo Chang ◽  
Pung Shik Shin ◽  
Yeon-Koo Jeong ◽  
Young June Choi
Keyword(s):  
Water Quality ◽  
Removal Efficiency ◽  
Biological Treatment ◽  
Inorganic Nitrogen ◽  
Ammonia Nitrogen ◽  
Control Technology ◽  
Treatment Efficiency ◽  
Air Supply ◽  
Total Inorganic Nitrogen ◽  
Nitrogen Treatment

Objectives : This study aimed to achieve improved process performance and energy saving by developing a technology to control the air supply of an aerobic basin by measuring the conductivity in the anoxic basin.Methods : To verify whether conductivity can be used as an operation indicator of biological treatment, the correlation analysis between water quality factor and conductivity of each process was conducted by dividing into summer (methanol input), winter and autumn periods. An empirical formula was presented by briefly arranging the required air quantity formula, and a quick reference was prepared by putting air supply in the conductivity range sequentially. The performance evaluation was judged based on the removal efficiency of ammonia nitrogen and total inorganic nitrogen, SNR and SDNR, the change of air supply, the stability of the process against inflow change.Results and Discussion : The seasonal correlation coefficients of conductivity and water quality items were calculated in the order of ammonia nitrogen, total inorganic nitrogen, DOC, and phosphate in the range of 0.5267 ~ 0.9115. It was found that the conductivity could be used as an operation indicator of the biological treatment process with a correlation coefficient of 0.5 or more. The regression equations for the conductivity and ammonia nitrogen are secured by season, so it is possible to estimate the ammonia nitrogen through the conductivity. At the end of the aerobic basin DO was 3.4 mg/L, the nitrogen treatment efficiency in winter was the best. The aerobic basin DO can be controlled by the air supply, and it can be seen that it is possible to control the air supply and improve the nitrogen treatment efficiency by directly measuring the conductivity having a high correlation with nitrogen. An empirical formula for estimating the required air volume through conductivity and inflow is presented. A' and (B' + X') are 0.0589 (m<sup>3</sup>-air/h)/(m<sup>3</sup>/h)/(μS/cm) and –77.562 (m<sup>3</sup>-air/h)/(m<sup>3</sup>/h). The result of automatic control of air supply according to the measured conductivity of anoxic tank during winter season showed that total inorganic nitrogen removal efficiency and SDNR were 8.3% and 0.007 g-N/g-MLSS/d higher than the actual plant conditions, respectively. During the automatic control period, the air supply/inflow average ratio was 36 (m<sup>3</sup>-air/h)/(m<sup>3</sup>/h), which could reduce the air supply by 21.7% compared to the actual plant conditions.Conclusions : The air supply can be estimated from the flow rate and conductivity. The air supply control technology of the conductivity-based MLE process will be able to simultaneously improve nitrogen removal efficiency and reduce energy consumption.

scholarly journals Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Qi Cao ◽  
Gongliang Yu ◽  
Shengjie Sun ◽  
Yong Dou ◽  
Hua Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Training Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Haihe River

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

Hyperspectral remote sensing for estimating coastal water quality: case study on the coast of Black Sea, Romania

2014 ◽  
Author(s):  
S. G. Ghezehegn ◽  
Peters Steef ◽  
Annelies Hommersom ◽  
De Reus Nils ◽  
Oana Culcea ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Black Sea ◽  
Coastal Water ◽  
Hyperspectral Remote Sensing ◽  
Coastal Water Quality
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