Modelling soil salinity in Oued El Abid watershed, Morocco

Sabri, El Mouatassime; Boukdir, Ahmed; Ismail, Kharizam; Arioua, Abdelkrim; Messlouhi, Rachid; Idrissi, Abdelkhalek El Amrani

doi:10.1051/e3sconf/20183704002

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The focus of this study was to quickly estimate the salt content of the soil profile using RS data; thus, auxiliary data were not considered. Various studies have included terrain attributes (e.g., DEM) as environmental covariates when estimating soil salinity, and the results of our study also proved that micro-topography affects the accumulation of salt at the parcel scale (Boukdir et al, 2018). Land cover (especially vegetation) and soil conditions are two important indicators in the monitoring of soil salinity in this study.…”

Section: Last Three Years Of Data As Inputsupporting

confidence: 69%

A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

et al. 2022

View full text Add to dashboard Cite

Section: Last Three Years Of Data As Inputsupporting

confidence: 69%

A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

et al. 2022

View full text Add to dashboard Cite

“…Landsat-8 was launched on 11 February 2013, with two sensors: Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The OLI provides 8 bands at a spatial resolution of 30 m and a panchromatic band at 15 m. TIRS includes two separate thermal infrared bands with a resolution of 100 m [24]. To ensure the accuracy of the inversion results, the shooting time of each remote sensing image acquired was basically the same as the field sampling time, and the specific parameters are shown in Supplementary Material (Table S1).…”

Section: Remote Sensing Data Processingmentioning

confidence: 99%

Spatio-Temporal Variation in Soil Salinity and Its Influencing Factors in Desert Natural Protected Forest Areas

Zhao,

Xi,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Soil salinity is a crucial parameter affecting soil health. Excessive surface salt accumulation degrades soil structure, inhibits vegetation growth, and diminishes plant diversity. Such increases in salinity can accelerate desertification, leading to soil resource loss, hampering agricultural progress, and compromising ecological security. However, the vastness of arid regions and data acquisition challenges often hinder efficient SSC monitoring and modeling. In this study, we leveraged remote sensing data coupled with machine learning techniques to investigate the spatio-temporal dynamics of SSC in a representative desert natural forest area, the Alxa National Public Welfare Forest. Utilizing the geodetector model, we also delved into the factors influencing SSC. Our results underscored the effectiveness of the Convolutional Neural Networks (CNN) model in predicting SSC, achieving an accuracy of 0.745. Based on this model, we mapped the spatial distribution of SSC, revealing hydrothermal conditions as pivotal determinants of salt accumulation. From 2016 to 2021, soils impacted by salinity in the research area exhibited a rising trend, attributed to the prevailing dry climate and low precipitation. Such intensified salinity accumulation poses threats to the healthy growth of protective forest vegetation. This study can provide a theoretical reference for salinization management and ecological protection in desert natural forest areas.

show abstract

Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China

et al. 2020

View full text Add to dashboard Cite

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

show abstract

Modelling soil salinity in Oued El Abid watershed, Morocco

Cited by 4 publications

References 15 publications

A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

Spatio-Temporal Variation in Soil Salinity and Its Influencing Factors in Desert Natural Protected Forest Areas

Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China

Contact Info

Product

Resources

About