Assessment of salinity intrusion in coastal districts of Ben Tre province using Landsat 8 image

Monitoring and evaluation of saline water intrusion is an important task, especially for agricultural production in Ben Tre province. The paper introduces a new solution in the application of Landsat 8 satellite imagery and field survey data to determine the soil electrical conductivity (EC) for soil salinity assessment through the distribution of EC indice value. Analyzing and establishing the correlation between reflectance value, salinity indices and EC allow selecting a suitable model for the creation of a soil salinity map in 4 levels corresponding to EC values: no salinity (0 - 4), mild (4 - 8), moderate (8 - 16), very salinity (> 16). Research results in 2019 showed that most of the coastal districts of Ben Tre province were salty with EC values ranging from 8 to 16. The salinity decreased gradually from the East Sea to the mainland with the distance from 15 to 25 km. In brief, the study proposed solutions for rapid monitoring and evaluation of soil salinity based on the easy access of Landsat 8 images to calculate the necessary indices in the establishment of soil salinity maps for the local and regional scale.

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Paddy in saline water: Analysing variety-specific effects of saline water intrusion on the technical efficiency of rice production in Vietnam

Outlook on Agriculture ◽

10.1177/0030727019850841 ◽

2019 ◽

Vol 48 (3) ◽

pp. 237-245 ◽

Cited By ~ 1

Author(s):

Thi Huyen Trang Dam ◽

TS Amjath-Babu ◽

Peter Zander ◽

Klaus Müller

Keyword(s):

Technical Efficiency ◽

Soil Salinity ◽

Saline Water ◽

Rice Variety ◽

Stochastic Frontier ◽

Rice Production ◽

Adaptation Measures ◽

Saline Water Intrusion ◽

Water Intrusion ◽

The Impact

The purpose of our study is to evaluate the impact of saltwater intrusion on the productivity and technical efficiency (TE) of rice farms in Central Vietnam using the stochastic frontier (SF) production function. In contrast to existing studies, this research quantitatively analyses rice variety and season-differentiated impact of soil salinity (as measured by electrical conductivity (EC)) on the TE of rice production. The empirical results indicate that salinity induces significantly varying negative impacts on yield and technical inefficiency of rice farms depending on the salinity class, variety planted and the season. TE begins to sharply decline after reaching salinity class 3 (EC = 4–8 dS/m) and drops to zero under salinity class 4 (EC = 8–16 dS/m) unless salt-tolerant (ST) varieties are planted. A 1% increase in the EC level decreases rice yields by 0.24% in various SF models, while TE shows a cubic relationship with EC, with negative coefficients for linear and quadratic terms. A combination of farm plots consolidation, irrigation, integrated pest management, input optimisation and shifts in varietal selection can potentially offset the yield decline caused by saline intrusion for salinity classes 1 to 4, while adoption of ST varieties seems to be the best option for higher salinity classes over 4. These adaptation measures could also help farmers to avoid maladaptive options such as increased use of pesticide sprays to offset the yield losses due to soil salinity resulting from saline water intrusion. The insights offered by the study is applicable to coastal delta regions cultivating rice in whole of Asia and in other continents.

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Assessing soil salinity using remote sensing in Campo de Cartagena

10.5194/egusphere-egu21-14478 ◽

2021 ◽

Author(s):

Francisco Pedrero Salcedo ◽

Juan José Alarcón Cabañero ◽

Pedro Pérez Cutillas

Keyword(s):

Soil Salinity ◽

New Technologies ◽

Saline Water ◽

Regional Scale ◽

Integrated System ◽

Growth And Yield ◽

Irrigated Agriculture ◽

Saline Irrigation ◽

Initial Objective ◽

Campo De Cartagena

<p>A pioneering study in Murcia within the framework of the ASSIST (Use of Advanced information technologies for Site-Specific management of Irrigation and SaliniTy with degraded water) research project, seeks to lay the foundations for a new integrated system for the assessment of salinity through combined use of traditional techniques (soil and plant sampling) and new technologies (multispectral aerial videography or satellite observation; and image analysis) to help quantify and map soil salinization / degradation and the effects of soil-plant interactions (salinity-toxicity) on the growth and yield of irrigated crops. In this sense, the initial objective was to evaluate the salinity of the soil and the development of lettuces irrigated with unconventional water resources through thermal and multispectral images. Different soil and plant salinity indices were studied, observing that the temperature (on plant) and salinity index (SI) (on soil), had a moderate correlation with the soil salinity. Although the results obtained have been encouraging, more research is needed to develop specific equations capable to predic soil salinity from the values of these indices taken remotely. In this context, a review of the spectral salinity indices has been prepared to be applied at a regional scale. As an experimental area, El Campo de Cartagena located in the southeast of the Iberian Peninsula has been chosen, since there is intensive irrigated agriculture in a semi-arid environment. Due to this, farmers resort to using non-conventional and saline water sources, consequently the use of saline irrigation water is causing salinization of the soils and damage to the crops. Values from existing salinity records combined with soil salinity data obtained in various plots, provided information that was correlated with time series of Landsat images (1984-2020). Regression models were also applied in which environmental variables provided an improvement in the estimation of soil salinity. The results allowed us to determine the main salinity concentration areas, as well as inputs to establish criteria for improvement in the management of irrigation systems.</p>

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Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain

SN Applied Sciences ◽

10.1007/s42452-021-04587-4 ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

Ghada Sahbeni

Keyword(s):

Soil Salinity ◽

Ridge Regression ◽

Land Surface ◽

Regional Scale ◽

Regression Modeling ◽

Mineral Weathering ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Great Hungarian Plain ◽

Hungarian Plain

AbstractSalt's deposition in the subsoil is known as salinization. It is caused by natural processes such as mineral weathering or human-made activities such as irrigation with saline water. This environmental issue has grown more critical and is frequently occurring in the Hungarian Great Plain, adversely influencing agricultural productivity. This study aims to predict soil salinity in the Great Hungarian Plain, located in the east of Hungary, using Landsat 8 OLI data combined with four state-of-the-art regression models, i.e., Multiple Linear Regression, Partial Least Squares Regression, Ridge Regression, and Feedforward Artificial Neural Network. For this purpose, seventy-six soil samples were collected during a field survey conducted by the Research Institute for Soil Sciences and Agricultural Chemistry between the 15 of September and the 15 of October, 2016. We used the min–max accuracy, the root-mean-square error (RMSE), and the mean squared error (MSE) to evaluate and compare the four models' performance. The results showed that the ridge regression model performed the best in terms of prediction (MSEtraining = 0.006, MSEtest = 0.0007, RMSE = 0.081), with a min–max accuracy equal to 0.75. Hence, the application of regression modeling on spectral indices, principal component analysis, and land surface temperature derived from multispectral data is an efficient method for soil salinity assessment at local scales. The resulting map can provide an overview of salinity levels and evaluate the efficiency of land management strategies in irrigated areas. An increase in sampling density will be recommended to validate this approach on the regional scale.

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Hydrologic data from monitoring of saline-water intrusion in the Cape Coral area, Lee County, Florida

Open-File Report ◽

10.3133/ofr82772 ◽

1982 ◽

Author(s):

D.J. Fitzpatrick

Keyword(s):

Saline Water ◽

Saline Water Intrusion ◽

Water Intrusion ◽

Lee County ◽

Hydrologic Data ◽

Coral Area

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Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

Remote Sensing ◽

10.3390/rs13101875 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1875

Author(s):

Wenping Xie ◽

Jingsong Yang ◽

Rongjiang Yao ◽

Xiangping Wang

Keyword(s):

Electrical Conductivity ◽

Spatial Distribution ◽

Soil Salinity ◽

Yangtze River ◽

Laboratory Data ◽

River Estuary ◽

Yangtze River Estuary ◽

Soil Electrical Conductivity ◽

The Yangtze River ◽

The Yangtze River Estuary

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.

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