Development of regression equation to study the Total Nitrogen, Total Phosphorus and Suspended Sediment using remote sensing data in Gujarat and Maharashtra coast of India

Mathew, Merin Mariam; Rao, N. Srinivasa; Mandla, Venkata Ravibabu

doi:10.1007/s11852-017-0561-1

Cited by 29 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many scholars have proposed a variety of methods for water-quality monitoring [4,[6][7][8][9], and some studies were used for Erhai Lake and Chaohu Lake [5,11,39,40]. These methods mainly focus on a single water-quality parameter, such as Chl-a or TP.…”

Section: Discussionmentioning

confidence: 99%

“…These nonoptically active variables are closely related to optically active parameters such as Chl-a, TSS, and colored dissolved organic matter (CDOM) [3,5]. Different kinds of regression models have been introduced to establish the relation between nonoptically active variables and optical active variables or remote-sensing reflectance [6][7][8][9]. However, the presented regression models are site-, season-, and scene-specific [10].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Ding

Chao

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.water-quality measurement, the cost is high, limiting the usage of in situ sampling and measurement. Optimization of a monitoring network is considered a trade-off between water-monitoring precision and cost [2]. A comprehensive and low-cost water-quality monitoring method was our goal.Remote-sensing monitoring of water quality and its parameters is considered as a promising and cost-effective monitoring method. There are many free-of-charge remote-sensing data, such as Landsat and Sentinel open-access data. Remote sensing provides a synoptic, repetitive, and consistent view of a water body. Water-quality-related variables, such as surface temperature, chlorophyll-a (Chl-a), turbidity and suspended solids (TSS) can be estimated from remote-sensing images according to their reflectance from the water surface [3]. The accuracy and stability of the remote-sensing inversion of surface-reflectance variables (Chl-a and SS) have improved after several years [4].Nonoptically active variables related to water pollution, such as total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and dissolved oxygen (DO), cannot be directly sensed from water-surface reflectance because of their weak optical characteristics and low signal-to-noise ratio [3]. These nonoptically active variables are closely related to optically active parameters such as Chl-a, TSS, and colored dissolved organic matter (CDOM) [3,5]. Different kinds of regression models have been introduced to establish the relation between nonoptically active variables and optical active variabl...

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Ding

Chao

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Taking R 2 as the comparison standard of retrieval accuracy, this research surveyed most existing publications regarding the retrieval of TP, TN and COD from 2000 to present and compared the performance of the proposed method in this research with previous research works, Figure 12 shows the results of comparison. Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works.…”

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

“…Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works. For COD, the average R 2 in the past research works [17][18][19][20] was 0.722, and the performance of retrieval COD in this research was slightly higher than the upper quartile of the past research works.…”

Section: Comparison Of Retrieval Accuracymentioning

confidence: 99%

“…Over the past several decades, scholars have carried out extensive research on water quality monitoring by remote sensing, and have achieved good results in estimating optically active parameters, such as Chlorophyll-a (Chl-a), suspended solids (SS), colored dissolved organic matter (CDOM), turbidity and transparency [11][12][13][14][15][16]. There are however few studies on non-optically active parameters, such as chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) [17,18,[27][28][29][19][20][21][22][23][24][25][26]. Estimating non-optically active parameters directly from spectral characteristics is difficult.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

View full text Add to dashboard Cite

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R 2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

show abstract

GIS-Based Modelling for Estimation of Water Quality Parameters: A Review

Bari,

Tiwari,

Mitra

et al. 2023

Geospatial Analytics for Environmental Pollution Modeling

View full text Add to dashboard Cite

Development of regression equation to study the Total Nitrogen, Total Phosphorus and Suspended Sediment using remote sensing data in Gujarat and Maharashtra coast of India

Cited by 29 publications

References 7 publications

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

GIS-Based Modelling for Estimation of Water Quality Parameters: A Review

Contact Info

Product

Resources

About