Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: a Case Study in Iran

Shahrayini, Elham; Noroozi, Ali Akbar

doi:10.1007/s10666-022-09823-8

Cited by 7 publications

(2 citation statements)

References 63 publications

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“…The DEM of the study area was used to derive a series of topographical indicators, related to the processes of soil salinization. Those included the slope, plan curvature, channel network base level, and topographic wetness index represent terrain attributes that have been previously used for modeling soil salinity (Omuto et al, 2020 ; Shahrayini & Noroozi, 2022 ; Salcedo et al, 2022 ). Those datasets were produced using the basic terrain analysis tool available in QGIS open source software.…”

Section: Methodsmentioning

confidence: 99%

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

Lekka

Petropoulos

Triantakonstantis

et al. 2023

Environ Monit Assess

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The salt-affected soils national map of Greece was recently made available within the initiative of the Global Soil Partnership (GSP) of Food and Agriculture Organization of the United Nations FAO. The present study explores the development of higher resolution soil property maps included in this national scale product adopting a modified version of the FAO methodology and a logistic regression (LR) method based on ground and satellite data. Furthermore, it also investigates the correlation between saline soils and soil organic carbon (SOC) using geospatial analysis methods. The island of Lesvos in Greece has been selected as a case study. A probabilistic model for saline soils in the agricultural land of Lesvos is produced by exploiting geoinformation technologies. As a result, the spatial distribution of saline soils in the croplands of Lesvos was obtained. Indicatively, areas with p > 0.80 for the occurrence of saline soils accounting for ∼20% of a total area of 169.51 km2 of the croplands in Lesvos. The Nagelkerke R2 coefficient showed that the probabilistic model interprets 11.3% of the variance of the dependent variable from the independent factors. The model accuracy was assessed adopting the receiver operating characteristic (ROC) curve, which showed a reasonable adaptability with area under curve (equal to 0.73). The methodological approach proposed herein can support decision-making on agricultural land protection and planning activities which are key priority today due to environmental instability, food security, and climate change.

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Section: Methodsmentioning

confidence: 99%

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

Lekka

Petropoulos

Triantakonstantis

et al. 2023

Environ Monit Assess

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“…The use of other wetness indices can also be tested, as not only the topographic wetness index can be used to spatially delineate areas that are likely to be wet. The vertical distance to channel network (VDCN) 104 is also an algorithm that can be used to model flow lines to identify areas that are likely to be wet or under water.…”

Section: Perspectivesmentioning

confidence: 99%

Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution

Le Guillou,

Niculescu,

Schmullius

2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Wetlands are one of the most important ecosystems in the world. Today, however, their fate is under serious threat, and their alarming decline highlights the urgent need to preserve these areas rich in biodiversity. The aim of this work is to spatially map and mapping the wetlands of the Crozon peninsula in Brittany France. The methodology is divided into two parts; the first part is to map the wetlands as a whole, while the second part is to map the wetlands using a adapted typology. Several databases were used to spatialize the wetlands: 12 Sentinel-2 images in L3A format, 23 Sentinel-1 VV and VH images and the RGE Alti (DTM at 1 metre resolution). The images were processed and stacked alone or in synergy. A Random Forest (RF) machine learning algorithm was then trained to predict wetlands in our study area using binary training data. The training data were obtained from a wetland inventory conducted in Brittany, distributed at the scale of a Sentinel-2 tile (30UUU). Post-processing was then carried out on the best result: binary morphological erosion and thresholding based on the DTM to remove outliers. We carried out two classifications, which we later merged. The classifications were carried out using a Pleiades time series (five dates) to achieve a very fine scale classification. A classification of 13 land cover classes with six different wetland types (mudflats, salt marshes, coastal lagoons, wet meadows, wet forest, swamps/bogs) was performed using three methods: pixel-by-pixel random forest, object-based random forest and Convolutional Neural Network (CNN). The best results obtained was for the pixel-based classification: kappa = 0.89, overall accuracy = 0.90, F1-score = 0.90.

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Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study

Han

Ding

et al. 2022

International Journal of Applied Earth Observation and Geoinfor

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Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: a Case Study in Iran

Cited by 7 publications

References 63 publications

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution

Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study

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