Modeling and Spatiotemporal Mapping of Water Quality through Remote Sensing Techniques: A Case Study of the Hassan Addakhil Dam

With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R2, the root mean square error and p-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 µg/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions.

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A Mechanistic Dissolved Oxygen Modeling for Riverine Fish Kill Prevention

10.5194/egusphere-egu21-9327 ◽

2021 ◽

Author(s):

Jia-Yin Dai ◽

You-Jia Chen ◽

Gwo-Wen Hwang ◽

Su-Ting Cheng

Keyword(s):

Water Quality ◽

Root Mean Square Error ◽

Dissolved Oxygen ◽

Mean Square Error ◽

Oxygen Demand ◽

Mean Square ◽

Fish Kill ◽

Fish Kills ◽

Hydrological Data ◽

Simulation Results

<p>Dissolved oxygen (DO) is a critical factor that controls the health and survival of the aquatic life. In the lower Danshuei River of Taiwan, DO was occasionally lower than 2 mg/L leading to several fish kill events. Since 2018, the Taipei city government started to continuously monitor hourly DO and other water quality factors at sites of Cheng-Mei Bridge and Cheng-De Bridge. However, at most sites, the monitoring has been conducted once a month. To provide sufficient DO predictions for preventing the occurrence of fish kills, a mechanistic DO modeling is required. As a result, in this study, we developed a system dynamic DO modeling considering oxygen exchange between the air-water and up/downstream interfaces with instream interactions of reaeration, photosynthesis, sediment oxygen demand (SOD), biochemical oxygen demand (BOD), respiration, and deoxygenation using the STELLA Architect software. In the model, we used meteorological data, water quality data, and hydrological data (flow rate, cross-section area, and hydraulic depth) simulated by HEC-RAS as input data to simulate daily DO at Cheng-Mei Bridge. Field measurements ranging from 0.21 to 10.34 mg/L were used to calibrate and validate the simulation results during Jan. to Aug. 2018, and Sep. to Dec. 2018, respectively. Our simulation results appeared reasonably good accuracy, in which the root mean square error (RMSE) ranging from 0.5 to 1.5 mg/L, and the percentage root mean square error (PRMSE) ranging from 5 to 15%. Moreover, results showed that DO was most sensitive to hydrological data, deoxygenation coefficient, and reaeration coefficient such that the meteorological conditions, like temperature and wind speed, were also important variables triggering hypoxia or anoxia that caused fish kills. Consequently, to better avoid or mitigate the occurrence of fish kills, we believe this physically-based DO modeling coupled with meteorological variables will offer useful information in predicting the condition of DO along the lower Danshuei River for managers to take preventative actions.</p>

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Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

Remote Sensing ◽

10.3390/rs13091630 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1630

Author(s):

Yaohui Zhu ◽

Guijun Yang ◽

Hao Yang ◽

Fa Zhao ◽

Shaoyu Han ◽

...

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Minimum Temperature ◽

Price Regulation ◽

Flowering Period ◽

Mean Square ◽

Prevention Measures ◽

Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations

Remote Sensing ◽

10.3390/rs11131598 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1598 ◽

Cited By ~ 5

Author(s):

Hua Su ◽

Xin Yang ◽

Wenfang Lu ◽

Xiao-Hai Yan

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Estimation Error ◽

Satellite Observations ◽

The Other ◽

Global Ocean ◽

Gradient Boosting ◽

Mean Square ◽

Thermohaline Structure

Retrieving multi-temporal and large-scale thermohaline structure information of the interior of the global ocean based on surface satellite observations is important for understanding the complex and multidimensional dynamic processes within the ocean. This study proposes a new ensemble learning algorithm, extreme gradient boosting (XGBoost), for retrieving subsurface thermohaline anomalies, including the subsurface temperature anomaly (STA) and the subsurface salinity anomaly (SSA), in the upper 2000 m of the global ocean. The model combines surface satellite observations and in situ Argo data for estimation, and uses root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), and R2 as accuracy evaluations. The results show that the proposed XGBoost model can easily retrieve subsurface thermohaline anomalies and outperforms the gradient boosting decision tree (GBDT) model. The XGBoost model had good performance with average R2 values of 0.69 and 0.54, and average NRMSE values of 0.035 and 0.042, for STA and SSA estimations, respectively. The thermohaline anomaly patterns presented obvious seasonal variation signals in the upper layers (the upper 500 m); however, these signals became weaker as the depth increased. The model performance fluctuated, with the best performance in October (autumn) for both STA and SSA, and the lowest accuracy occurred in January (winter) for STA and April (spring) for SSA. The STA estimation error mainly occurred in the El Niño-Southern Oscillation (ENSO) region in the upper ocean and the boundary of the ocean basins in the deeper ocean; meanwhile, the SSA estimation error presented a relatively even distribution. The wind speed anomalies, including the u and v components, contributed more to the XGBoost model for both STA and SSA estimations than the other surface parameters; however, its importance at deeper layers decreased and the contributions of the other parameters increased. This study provides an effective remote sensing technique for subsurface thermohaline estimations and further promotes long-term remote sensing reconstructions of internal ocean parameters.

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Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Remote Sensing ◽

10.3390/rs12111814 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1814

Author(s):

Phamchimai Phan ◽

Nengcheng Chen ◽

Lei Xu ◽

Zeqiang Chen

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Vegetation Index ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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Using GIS and Remote Sensing Techniques to Study Water Quality Changes and Spectral Analysis of Tigris River within Mosul City, North of Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2019.60.10.24 ◽

2019 ◽

pp. 2300-2307

Author(s):

Muthanna F. Allawai ◽

Bushra A. Ahmed

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Near Infrared ◽

Water Clarity ◽

Seasonal Difference ◽

Near Infrared Reflectance ◽

Gis And Remote Sensing ◽

Tigris River ◽

Remote Sensing Techniques

The aim of the study is the measuring of changes in the spectral reflectivity water quality, analyzing the seasonal difference of Tigris River within Mosul City in the north of Iraq using Geographic Information Systems (GIS) and remote sensing techniques during the period (2014-2018). For this paper, Satellite images of the 8 Landsat in 2018 for four seasons have been selected in order to study the seasonal changes on the river they took place during 2018. A total of ten sample datasets were taken at the upstream, midstream and downstream along the Tigris River. This research focuses on analyzing the locational variance of reflectance, analyzing seasonal difference, and finding modeling algal amount change. There are distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth implies that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.

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Bayesian Calibration of the Aquacrop-OS Model for Durum Wheat by Assimilation of Canopy Cover Retrieved from VENµS Satellite Data

Remote Sensing ◽

10.3390/rs12162666 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2666 ◽

Cited By ~ 3

Author(s):

Deepak Upreti ◽

Stefano Pignatti ◽

Simone Pascucci ◽

Massimo Tolomio ◽

Wenjiang Huang ◽

...

Keyword(s):

Root Mean Square Error ◽

Durum Wheat ◽

Mean Square Error ◽

Root Mean Square ◽

Satellite Data ◽

Satellite Images ◽

Central Italy ◽

Canopy Cover ◽

Transfer Model ◽

Mean Square

Crop growth models play an important role in agriculture management, allowing, for example, the spatialized estimation of crop yield information. However, crop model parameter calibration is a mandatory step for their application. The present work focused on the regional calibration of the Aquacrop-OS model for durum wheat by assimilating high spatial and temporal resolution canopy cover data retrieved from VENµS satellite images. The assimilation procedure was implemented using the Bayesian approach with the recent implementation of the Markov chain Monte Carlo (MCMC)-based Differential Evolution Adaptive Metropolis (DREAM) algorithm DREAM(KZS). The fraction of vegetation cover (fvc) was retrieved from the VENµS satellite images for two years, during the durum wheat growing seasons of 2018 and 2019 in Central Italy. The retrieval was based on a hybrid method using PROSAIL Radiative Transfer Model (RTM) simulations for training a Gaussian Process Regression (GPR) algorithm, combined with Active Learning to reduce the computational cost. The Aquacrop-OS model was calibrated with the fvc data of 2017–2018 for the Maccarese farm in Central Italy and validated with the 2018–2019 data. The retrieval accuracy of the fvc from the VENµS images were the Coefficient of Determination (R2) = 0.76, Root Mean Square Error (RMSE) = 0.09, and Relative Root Mean Square Error (RRMSE) = 11.6%, when compared with the ground-measured fvc. The MCMC results are presented in terms of Gelman–Rubin R statistics and MR statistics, Markov chains, and marginal posterior distribution functions, which are summarized with the mean values for the most sensitive crop parameters of the Aquacrop-OS model subjected to calibration. When validating for the fvc, the R2 of the model for year (2018–2019) ranged from 0.69 to 0.86. The RMSE, Relative Error (RE), Relative Variability (α), and Relative Bias (β) ranged from 0.15 to 0.44, 0.19 to 2.79, 0.84 to 1.45, and 0.91 to 1.95, respectively. The present work shows the importance of the calibration of the Aquacrop-OS (AOS) crop water productivity model for durum wheat by assimilating remote sensing information from VENµS satellite data.

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Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data

PeerJ ◽

10.7717/peerj.7593 ◽

2019 ◽

Vol 7 ◽

pp. e7593 ◽

Cited By ~ 3

Author(s):

Yaohui Zhu ◽

Chunjiang Zhao ◽

Hao Yang ◽

Guijun Yang ◽

Liang Han ◽

...

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Lidar Data ◽

Optical Remote Sensing ◽

Mean Square ◽

Above Ground Biomass ◽

Multispectral Data ◽

Separation Strategy ◽

Stem Leaf

Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.

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Application of Parallel Computing in Feature Point Extraction Processing of Remote Sensing Images

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.3600 ◽

2014 ◽

Vol 687-691 ◽

pp. 3600-3603

Author(s):

Xian Chen Xiao ◽

Yu Min Chen ◽

Jin Fang Yang

Keyword(s):

Remote Sensing ◽

Parallel Computing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Feature Point ◽

Mean Square ◽

Computing Environment ◽

Remote Sensing Images ◽

Time Consumption ◽

Feature Point Extraction

This study attempts to introduce parallel computing into processing of remote sensing images and discuss its influence on the processing results. We select an processing of remote sensing images called feature point extraction to run in parallel computing environment and calculate the time consumption and root-mean-square error, then give an analysis based on the result.

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Impacts of Human Interventions on Water Quality of Upper Ganga River Near Haridwar, Uttarakhand: A GIS-Based Analysis

10.20944/preprints202110.0381.v1 ◽

2021 ◽

Author(s):

Pradip Kumar Maurya ◽

Sk Ajim Ali ◽

Ateeque Ahmad Ahmad ◽

Krishna Kumar Maurya

Keyword(s):

Water Quality ◽

Land Use ◽

Root Mean Square Error ◽

Mean Square Error ◽

Spatial Correlation ◽

Water Quality Index ◽

Human Activities ◽

Quality Index ◽

Mean Square

The water quality of the river is becoming deteriorated due to human interference. It is essential to understand the relationship between human activities and land-use types to assess the water quality of a region. GIS has the latest tool for analyzing the spatial correlation. Land use land cover and change detection is the best illustration to show the human interactions on land features. The study assessed water quality index of upper Ganga River near Haridwar, Uttarakhand and spatially correlated them with changing land use to reach a logical conclusion. At the upper course of Ganga along 78 Km long from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index the physicochemical parameters like pH, EC, DO, TDS, CaCO3-, CaCO3, Cl¯, Ca++, Mg++, Na+, K+, F-, Fe2+ were considered. The result of the spatial analysis was evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index of selected sampling sites was estimated as 0.1443. The spatial correlation between land-use change and site-wise differences in water quality index has also shown a high positive correlation with R² = 0.8455. The degree of positive correlation and root mean square error has strongly indicated that the water quality of the river at the upper course of Ganga is highly impacted through human activities.

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Phycocyanin Monitoring in Some Spanish Water Bodies with Sentinel-2 Imagery

Water ◽

10.3390/w13202866 ◽

2021 ◽

Vol 13 (20) ◽

pp. 2866

Author(s):

Rebeca Pérez-González ◽

Xavier Sòria-Perpinyà ◽

Juan Miguel Soria ◽

Jesús Delegido ◽

Patricia Urrego ◽

...

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Water Bodies ◽

World Health ◽

Mean Square ◽

Monitoring Method ◽

And Control ◽

Sentinel 2

Remote sensing is an appropriate tool for water management. It allows the study of some of the main sources of pollution, such as cyanobacterial harmful algal blooms. These species are increasing due to eutrophication and the adverse effects of climate change. This leads to water quality loss, which has a major impact on the environment, including human water supplies, which consequently require more expensive purification processes. The application of satellite remote sensing images as bio-optical tools is an effective way to monitor and control phycocyanin concentrations, which indicate the presence of cyanobacteria. For this study, 90 geo-referenced phycocyanin measurements were performed in situ, using a Turner C3 Submersible Fluorometer and a laboratory spectrofluorometer, both calibrated with phycocyanin standard, in water bodies of the Iberian Peninsula. These samples were synchronized with Sentinel-2 satellite orbit. The images were processed using Sentinel Application Program software and corrected with the Case 2 Regional Coast color-extended atmospheric correction tool. To produce algorithms that would help to obtain the phycocyanin concentration from the reflectance measured by the multispectral instrument sensor of the satellite, the following band combinations were tested, among others: band 665 nm, band 705 nm, and band 740 nm. The samples were equally divided: half were used for the algorithm’s calibration, and the other half for its validation. With the best adjustment, the algorithm was made more robust and accurate through a recalculation, obtaining a determination coefficient of 0.7, a Root Mean Square Error of 8.1 µg L−1, and a Relative Root Mean Square Error of 19%. In several reservoirs, we observed alarming phycocyanin concentrations that may trigger many environmental health problems, as established by the World Health Organization. Remote sensing provides a rapid monitoring method for the temporal and spatial distribution of these cyanobacteria blooms to ensure good preventive management and control, in order to improve the environmental quality of inland waters.

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