Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal Mekong Delta, Vietnam

Nguyen, Phuong T.; Koedsin, Werapong; McNeil, Donald R.; Van, Tri P. D.

doi:10.1080/01431161.2018.1466071

Cited by 28 publications

(16 citation statements)

References 31 publications

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“…8 Kong. Similarly, at the Vietnamese Mekong Delta, the use of a green band of Landsat-8 OLI presented the highest correlation with salinity in the single-band selection (Nguyen et al 2018). However, Nguyen et al (2018) recommended multi-band combinations to enhance the sensitivity of surface water.…”

Section: Ec Estimation Using Sentinel-2mentioning

confidence: 93%

Monitoring saline intrusion in the Ayeyarwady Delta, Myanmar, using data from the Sentinel-2 satellite mission

Sakai

Omori

et al. 2021

Paddy Water Environ

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Summer rice cannot be grown near the coast of the Ayeyarwady Delta, Myanmar, because of the high salinity in river water during the dry season. This means that saline intrusion should be monitored to manage the irrigated rice-growing area. The aim of this study therefore is to develop a model to estimate the salinity of river water using satellite imagery. The imagery of Sentinel-2 was suitable for monitoring saline intrusion because of their high spatial (10-m) and temporal (10-day) resolutions. We found that the reflectance of the visible bands was correlated with electrical conductivity, which was influenced by the concentration and composition of dissolved salts. When the river water mixed with the salt water from the sea, suspended particles tended to flocculate and settle, as a result, less turbid water was more saline. The best-fitting model was obtained with the green band (coefficient of determination R2 of 0.776, root mean square error of 4.896 dSm−1, and mean absolute error of 3.340 dS m−1). The saline intrusion showed considerable spatial and temporal variability during the dry season. The salinity intrusion extended approximately 80 km inland at the end of the dry season in March in the Pathein River. The 1 ppt salt concentration line in March marked the boundary between cultivated and non-cultivated areas of paddy field, which indicates that cultivable areas were strongly affected by saline intrusion. The results show that more frequent and higher resolution monitoring than before of the Sentinel-2 can support effective water resource management.

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Section: Ec Estimation Using Sentinel-2mentioning

confidence: 93%

Monitoring saline intrusion in the Ayeyarwady Delta, Myanmar, using data from the Sentinel-2 satellite mission

Sakai

Omori

et al. 2021

Paddy Water Environ

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“…Recognition bands were important for setting the model to match with the accuracy of the predictive water salinity (Nguyen et al, 2018) .Therefore it is essential to determine which combination of bands would conclude the best outcomes. In order to address this issue, the Spectral signature of salty water was employed.…”

Section: Band Selectionmentioning

confidence: 99%

“…In addition, previous attempts at salinity modeling by OLI have implemented different band combinations, e.g. OLI bands 2, 3, 4, and 7 (Nguyen et al, 2018).Recently, many authors provided a water salinity model, which is calculated using various regression models, e.g. Geographically Weighted Regression (GWR) technique (Xie et al, 2013), Spatially Weighted Optimization Model (SWOM) technique (Khadim et al, 2017) and Multiple Linear Regression (MLR), Decision Trees (DT) and Random Forest (RF) techniques (Nguyen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Detecting of water salinity would be a challenging task when the number of observations might not be sufficient (Nguyen et al, 2018). To deal with this problem, in this study 102 In situ measurements (Electrical Conductivity) were collected from 8 stations from June 2013 to July 2018 along the Karun River (see Figure 1) .In this study the spectral signature was used to find the best band combination for modeling water salinity.…”

Section: Introductionmentioning

confidence: 99%

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Water Salinity Mapping of Karun Basin Located in Iran Using the SVR Method

Ansari

Akhoondzadeh

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Water salinity is a complex issue in coastal and estuarine areas. Currently, remote sensing techniques have been widely used to monitor water quality changes, ranging from river to oceans. The salinity of Karun River has been increasing due to some critical factors, therefore, This study aimed at building regression models to ascertain the water salinity through the relationship between the reflectance of the Landsat-8 OLI and In situ measurements. A total of 102 observed samples were divided into 70% training and 30% test from June 2013 to July 2018 along the Karun River. Spectral signature analysis showed that band 1 - Coastal/Aerosol (0.433–0.453 μm), band 2 - Blue (0.450–0.515 μm) and band 3 - Green (0.525–0.600 μm) are sensitive to salinity . Furthermore, to have a comprehensive investigation, the Support Vector Regression (SVR) method was applied. The outcomes related to the quality of the SVR depend on several factors e.g. proper setting of the SVR meta-parameters, therefore, to deal with this issue Genetic Algorithm (GA) was applied. The SVR model resulted in values of R2 and RMSE for test data which are respectively obtained to be 0.7 and 390 μs cm−1. Eventually, Karun water salinity maps were prepared by SVR method to demonstrate the Karun water salinity on 1 February 2015 and 5 September 2018.

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“…SI is a complex phenomenon depending on a variety of variables include freshwater discharge from upstream, capacity, and morphology of the rivers/canals, a configuration of the drainage network, tidal conditions, and presence of control artificial structures such as dams, sluice gates 3,4 . Moreover, the impacts of climate change and sea-level rise also exacerbate the damage of SI 5 . However, SI might be predicted by using statistical models.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of saline intrusion in Ham Luong river, Ben Tre province (Southern Vietnam) using Box-Jenkins ARIMA models

Yến¹,

Thien²,

Quảng³

et al. 2020

Sci. Tech. Dev. J.

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Introduction: Ham Luong River is a branch of Mekong River located in Ben Tre Province, which has played a crucial role in supporting livelihoods of local residents and the province's economic development. However, the saline intrusion has been expanding in Ham Luong River, which seriously affects the productive agriculture, aquaculture, and further causes tremendous difficulties for local people's lives. Thus, it is crucial to have research for forecast the saline intrusion in Ham Luong River. Our aim was to develop mathematical models in order to forecast the saline intrusion in Ham Luong River, Ben Tre Province. Methods: The Auto regressive integrated moving average (ARIMA) model was built to forecast the weekly saline intrusion in Ham Luong River, which has been obtained from Ben Tre Province's Hydro-Meteorological Forecasting Center over eight years (from 2012 to 2019). Results: The saline concentration increased from January to March and then decreased from April to June. The highest salinity occurred in February and March while the lowest salinity was observed in early June. Moreover, the ARIMA technique provided an adequate predictive model for a forecast of the saline intrusion in An Thuan, Son Doc, and An Hiep station. However, the ARIMA model in My Hoa and Vam Mon might be improved upon by other forecasting methods. Conclusion: Our study suggested that the nonseasonal/seasonal ARIMA is an easy-to-use modeling tool for a quick forecast of the saline intrusion.

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Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal Mekong Delta, Vietnam

Cited by 28 publications

References 31 publications

Monitoring saline intrusion in the Ayeyarwady Delta, Myanmar, using data from the Sentinel-2 satellite mission

Monitoring saline intrusion in the Ayeyarwady Delta, Myanmar, using data from the Sentinel-2 satellite mission

Water Salinity Mapping of Karun Basin Located in Iran Using the SVR Method

Forecasting of saline intrusion in Ham Luong river, Ben Tre province (Southern Vietnam) using Box-Jenkins ARIMA models

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