Soil salinity mapping by remote sensing south of Urmia Lake, Iran

Delavar, Mohammad Amir; Naderi, Arman; Ghorbani, Yousef; Mehrpouyan, Ahmad; Bakhshi, Ali

doi:10.1016/j.geodrs.2020.e00317

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…Detection of surface salinity using satellite images is based on the principle that salt crust and other salt deposits without crusts on soil surface affect spectral reflection from soils [ 21 ]. In arid and semi-arid environments where evaporation is higher than precipitation, soluble salts in lower part of soil profile rise towards surface layer by capillarity and form thin white salt crusts.…”

Section: Discussionmentioning

confidence: 99%

“…The basic approach in most of the studies was to develop regression models between ground sampling points and remote sensing datasets. Three different approaches, i.e., i) model development between reflection values of spectral bands in satellite images and measured soil salinity values [ 1 , 20 ], ii) estimating soil salinity directly by calculating salinity indices [ 14 , 19 , 21 ] and iii) estimating soil salinity indirectly by calculating vegetation indices [ 14 , 22 , 23 ] are used to map soil salinity.…”

Section: Introductionmentioning

confidence: 99%

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Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey

et al. 2022

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Soil salinity is a major land degradation process reducing biological productivity in arid and semi-arid regions. Therefore, its effective monitoring and management is inevitable. Recent developments in remote sensing technology have made it possible to accurately identify and effectively monitor soil salinity. Hence, this study determined salinity levels of surface soils in 2650 ha agricultural and natural pastureland located in an arid region of central Anatolia, Turkey. The relationship between electrical conductivity (EC) values of 145 soil samples and the dataset created using Landsat 5 TM satellite image was investigated. Remote sensing dataset for 23 variables, including visible, near infrared (NIR) and short-wave infrared (SWIR) spectral ranges, salinity, and vegetation indices were created. The highest correlation between EC values and remote sensing dataset was obtained in SWIR1 band (r = -0.43). Linear regression analysis was used to reveal the relationship between six bands and indices selected from the variables with the highest correlations. Coefficient of determination (R2 = 0.19) results indicated that models obtained using satellite image did not provide reliable results in determining soil salinity. Microtopography is the major factor affecting spatial distribution of soil salinity and caused heterogeneous distribution of salts on surface soils. Differences in salt content of soils caused heterogeneous distribution of halophytes and led to spectral complexity. The dark colored slickpots in small-scale depressions are common features of sodic soils, which are responsible for spectral complexity. In addition, low spatial resolution of Landsat 5 TM images is another reason decreasing the reliability of models in determining soil salinity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey

et al. 2022

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“…Remote sensing methods utilizing intelligent calculations can forecast surface saltiness at different time stretches in broad areas (Delavar et al, 2020). In smart calculations, ANN‐based approaches such as ANN‐GA have principally been used to consider the nonlinear connection between realistic estimations and satellite information with higher precision contrasted with linear models (Delavar et al, 2020; Ghimire et al, 2019). The evolutionary calculations like GA are combined with ANN for improvements of model constituents.…”

Section: Remote Sensing and Gis In Salinity Modellingmentioning

confidence: 99%

“…Recently, Delavar et al (2020) utilized remote sensing techniques for mapping soil salinity changes at Urmia Lake, Iran, over a time frame of 45 years starting from 1973. The dissemination of saltiness in 2018 was evaluated utilizing the supervised characterization by the ANN‐GA model, whereas the unsupervised technique was used for assessing the soil salinity of previous periods.…”

Section: Global Case Studiesmentioning

confidence: 99%

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Soil salinity: A global threat to sustainable development

Singh

2021

Soil Use and Management

110

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Soil is a vital resource for feeding the burgeoning global population, and it is also essential for realizing most of the ‘United Nations Sustainable Development Goals (SDGs)’. For example, it is vital to realizing the ‘Zero hunger (SDG2), Good health and well‐being (SDG3), Clean water and sanitation (SDG6), and Life on land (SDG15)’. Excess salts present in the soil make it saline, and it poses a significant threat to agricultural production and environmental health. Soil salinity is an extensive problem and spreads over one billion hectares extended over 100 countries. This paper presents a comprehensive review of global soil salinity management through the applications of remote sensing and GIS. All possible sources of relevant and current literature were accessed and more than 260 publications were collected and carefully analysed, for this review. The rationale and severity of the salinity problem are provided. The impact of salinity on plant yield and the effect of climate change on soil salinity are detailed. The salinity indicators and salinity monitoring and mapping are provided, and the global cases of soil salinity management through remote sensing and GIS applications under different agro‐hydro‐climatic environments are discussed, followed by a summary of conclusions and challenges along with future research directions. The analysis of past investigations showed that remote sensing strategy might be a practical approach to adequately assess plant response such as evapotranspiration under diverse salinity environments, yet it additionally has various difficulties. The lower spatial and temporal resolution of imagery may reason errors because of subpixel heterogeneity. However, with the improvement of the better‐resolution thermal infrared remote method, there is the possibility to spot spatial variations at a smaller scale.

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Soil salinity estimation in Shule River Basin using support vector regression model

2023

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Soil salinization is a progressive degradation process that spreads globally and leads to a decline in soil fertility. Assessing the scale of salinization is crucial for sustainable agricultural development and saline‐land rehabilitation. In this study, we proposed a support vector regression spatial (SVR‐S) model that utilizes spatial dependence information to predict soil salinity over the irrigation area of the Shule River Basin in northwestern China. To investigate the performance of the SVR‐S model, 50 soil samples were collected in the field. Semivariograms of soil salinity (measured as total salt content and ion concentrations) were constructed to measure spatial dependence. The SVR‐S model was compared with the original SVR model and the geographically weighted regression (GWR) model regarding the salinity prediction ability. The soil salinity in the experimental area demonstrated a strong spatial dependence pattern. The SVR‐S model delivered a better performance than the SVR‐O and GWR. SVR‐S showed a correlation coefficient R of 0.87 and a root mean square error (RMSE) of 1.83%, while the performance of SVR‐O (R = 0.75, RMSE = 3.32%) and GWR (R = 0.73, RMSE = 3.47%) was comparatively poor. Topographic indices integrating spatial information contributed the most to the estimation of salinity in the study area. This study provides a new approach to integrating spatial information for accurate soil salinity mapping.

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Soil salinity mapping by remote sensing south of Urmia Lake, Iran

Cited by 20 publications

References 30 publications

Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey

Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey

Soil salinity: A global threat to sustainable development

Soil salinity estimation in Shule River Basin using support vector regression model

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