Soil Salinity Mapping Using Multitemporal Landsat Data

Azabdaftari, A.; Sunar, Filiz

doi:10.5194/isprsarchives-xli-b7-3-2016

Cited by 18 publications

(12 citation statements)

References 2 publications

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“…The classification of salinity level is based on the global standard salinity ranges (Azabdaftari & Sunar, 2016 ). From that, four salinity classes were considered in mapping salinity level (Table 3 ).…”

Section: Methodsmentioning

confidence: 99%

“…Currently, a variety of remote-sensing data and sensors have been developed and used to delineate salt-affected areas. Among them are LANDSAT, SPOT, IKONOS, and Terra-ASTER with the resolution ranging from medium to high as well as hyperspectral sensors (Metternicht & Zinck, 2003 ; Azabdaftari & Sunar, 2016 ). In recent years, various salinity and vegetation indices such as normalized difference vegetation index (NDVI), normalized difference salinity index (NDSI), salinity index (SI), and soil-adjusted vegetation index (SAVI) have been used to delineate salt-affected areas (Zhang et al, 2011 ; Taghadosi & Hasanlou, 2017 ; Yossif, 2017 ; Allbed et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal

Thiam

Villamor

Faye

et al. 2021

Environ Monit Assess

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Soil salinity is a major issue causing land degradation in coastal areas. In this study, we assessed the land use and soil salinity changes in Djilor district (Senegal) using remote sensing and field data. We performed land use land cover changes for the years 1984, 1994, 2007, and 2017. Electrical conductivity was measured from 300 soil samples collected at the study area; this, together with elevation, distance to river, Normalized Difference Vegetation Index (NDVI), Salinity Index (SI), and Soil-Adjusted Vegetation Index (SAVI), was used to build the salinity model using a multiple regression analysis. Supervised classification and intensity analysis were applied to determine the annual change area and the variation of gains and losses. The results showed that croplands recorded the highest gain (17%) throughout the period 1984–2017, while forest recorded 3%. The fastest annual area of change occurred during the period 1984–1994. The salinity model showed a high potential for mapping saline areas (R2 = 0.73 and RMSE = 0.68). Regarding salinity change, the slightly saline areas (2 < EC < 4 dS/m) increased by 42% whereas highly saline (EC > 8 dS/m) and moderately saline (4 < EC < 8 dS/m) areas decreased by 23% and 26%, respectively, in 2017. Additionally, the increasing salt content is less dominant in vegetated areas compared with non-vegetated areas. Nonetheless, the highly concentrated salty areas can be restored using salt-resistant plants (e.g., Eucalyptus sp., Tamarix sp.). This study gives more insights on land use planning and salinity management for improving farmers’ resilience in coastal regions.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal

Thiam

Villamor

Faye

et al. 2021

Environ Monit Assess

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“…The topsoil grain size: GSI GSI = (R − B)∕(R + V + B) [62] GSI or topsoil grain size index is an index appropriated to characterize the texture of the soil surface depending on the soil reflectance curve (Jieying Xiao, Shen, and Ryutaro [63]); [40] The soil crusting: CI CI = (R − B)∕(R + B) [28] It is used to detect and map from satellite imagery different lithological morphological units. It is also able to reveal poor infiltration, and reduced air exchange between the soil and the atmosphere [28] The soil redness: RI RI = R 2 ∕B * G 3 [38] Used as one of the indicators to evaluate the mineralogy of soils, including the iron content [17,36,51] The soil salinity: NDSI NDSI = (R − NIR)∕(R + NIR) [30] It is used to identify soils affected by salinity, and to show the spatial extent of salinity prevalent in our study area [3,5,6,11,24], (Narmada, et al [39])…”

Section: Statistical Patternsmentioning

confidence: 99%

Characterization of Soil Degradation from the Cameroonians Shores of Lake Chad Combining Spectral Indexes and Statistics Analysis

Gadal

Gbetkom

Mfondoum³

2023

SN COMPUT. SCI.

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This article aims to propose a model on the soil degradation risk along the Cameroonian shores of Lake Chad based on the statistical analysis of spectral indexes of Sentinel 2A satellite images. A total of four vegetation indexes such as the Greenness Index and Disease water stress index and nine soil indexes such as moisture, brightness, or organic matter content are computed and combined to characterize vegetation cover and bare soil state, respectively. All these indexes are aggregated to produce one image (independent variable) and then regressed by individual indexes (dependent variable) to retrieve correlation and determination coefficients. Principal Component Analysis and factorial analysis are applied to all spectral indexes to summarize information, obtain factorial coordinates, and detect positive/negative correlation. The first factor contains soil information, whereas the second factor focuses on vegetation information. The final equation of the model is obtained by weighting each index with both its coefficient of determination and factorial coordinates. This result generated figures' cartography of five classes of soils potentially exposed to the risk of soil degradation. Five levels of exposition risk are obtained from the "Lower" level to the "Higher": the "Lower" and "Moderate to low" levels occupy, respectively, 25,214.35 hectares and 130,717.19 hectares; the "Moderate" level spreads 137,404.34 hectares; the "High to moderate" and "Higher" levels correspond, respectively, to 152,371.91 hectares and 29,175.73 hectares.

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“…The secondary salinization process is occurring as a result of the accumulation of additional salts into irrigation layers, excessive irrigation measures, elevation of mineralized groundwater levels and their evaporation (Toderich et al, 2009;Saysel & Barlas, 2001;). So far the irrigated land that adjacent to the lake has been subjected to various, weak, moderate and very strong secondary salinization (Azabdaftari & Sunar, 2016;Wang et al, 2018). In areas near the lake, salinity increases.…”

Section: Soil Salinity Condition In Agroirrigation Landscapesmentioning

confidence: 99%

Use of Landsat EVI in Determining the Degradation of Agroirriation Landscapes Adjacent to the Aydar-Arnasay Lakes System

Sabirova

Groll

Abbasov

2021

Preprint

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The Arnasay depression in Central Uzbekistan received large quantities of drainage water leading to the formation of the Aydarkul-Arnasay Lake System (AALS). The water level of the AALS drastically increased in 1969, when a flood in the nearby Syrdarya River basin could not be contained in the Chardarya reservoir, and today it occupies an area of 4000 km² of the Mirzachul and Kyzylkum desert. Increasing the lake’s water level also affects the surrounding agricultural land, further enhancing the level of groundwater and soil salinization. But the irrigated farming areas also influence the lake system due to the pollution of the drainage water discharged into the lake. As a result, both the arable land and the lake system are in a process of degradation, leading to reduced productivity and a variety of ecological problems. We used more of the remote sensing method in determining the degradation process in agroirrigation landscapes. Landsat EVI (Enhanced vegetation index) extremely resistant to various atmospheric resistances (aerosols). It monitors plants with very high sensitivity even in low biomass areas. Landsat has 4,5,7,8 series programs. Herein, we used Landsat-5 TM Collection 1 Tier 1 32-Day EVI and Landsat-8 ETM + Collection 1 Tier 1 32-Day EVI. To classify the degradation process in agroirrigation landscapes around the lake, we compared Landsat EVI images from March-April, May-June, June-July, Ilyul-August, August-September. We selected July-August as the optimal month to determine the perennial degradation process.

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Soil Salinity Mapping Using Multitemporal Landsat Data

Cited by 18 publications

References 2 publications

Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal

Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal

Characterization of Soil Degradation from the Cameroonians Shores of Lake Chad Combining Spectral Indexes and Statistics Analysis

Use of Landsat EVI in Determining the Degradation of Agroirriation Landscapes Adjacent to the Aydar-Arnasay Lakes System

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