Estimate Soil Salinity Research Articles

Estimating Soil Salinity Using HISUI Hyperspectral Data in the Western Australian Wheatbelt

Abstract. Soil salinity, caused by natural and anthropogenic factors, significantly impacts agricultural productivity, ecosystems, and global biodiversity. To mitigate severe salinity levels, it is necessary to detect early the salt-affected land for implementing solutions, such as adequate irrigation and cultivation of salt-tolerant crops. Before launching spaceborne hyperspectral sensor, Hyperspectral Imager Suite, (HISUI) developed by Japanese Ministry of Economy Trade and Industry, Kobayashi et al. (2010 and 2013) developed methods utilizing airborne hyperspectral data from HyMap to estimate soil salinity in wheatbelt region of Western Australia as a previous study. This paper aims to assess the feasibility of estimating soil salinity with HISUI in the same study area, following the past approach. Also, the goal is to estimate low soil salinity levels as same as the past objectives. As a result, past approach could not be fully applied in spaceborne hyperspectral sensor, caused by atmospheric effect in SWIR region. However, absorption of soil salinity in SWIR regions could be detected by HISUI. In the soil index, the NDSI (Normalized Difference Soil Index) showed higher accuracy than the Soil Index (SI) developed in previous studies. Then, the lowest values of RMSE were less than 100 mS/m in NDSI. As a result, HISUI data could not leach to map soil salinity at lower levels in this study. However, it showed the potential to estimate it with higher accuracy using hyperspectral data.

Soil Salinity Inversion Based on a Stacking Integrated Learning Algorithm

Soil salinization is an essential risk factor for agricultural development and food security, and obtaining regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of soil salinity inversion, this study focuses on the Manas River Basin oasis area, the largest oasis farming area in Xinjiang, as the study area and proposes a new soil salinity inversion model based on stacked integrated learning algorithms. Firstly, we selected four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network, for performance evaluation. Based on the model performance, we selected the more effective RF and BPNN as the basic regression models and further constructed a stacking integrated learning model. This stacking integration learning model improved the prediction accuracy by training a secondary model to fuse the prediction results of these two basic models as new features. We compared and analyzed the stacking integrated learning model with four single machine learning regression models. Findings indicated that the stacking integrated learning regression model fitted better and had good stability; on the test set, the stacking integrated learning regression model showed a relative increase of 8.2% in R2, a relative decrease of 14.0% in RMSE, and a relative increase of 6.5% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south from 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.80 g kg−1 in the study area had decreased by 19.6% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.

Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China

Texture features have been consistently overlooked in digital soil mapping, especially in soil salinization mapping. This study aims to clarify how to leverage texture information for monitoring soil salinization through remote sensing techniques. We propose a novel method for estimating soil salinity content (SSC) that combines spectral and texture information from unmanned aerial vehicle (UAV) images. Reflectance, spectral index, and one-dimensional (OD) texture features were extracted from UAV images. Building on the one-dimensional texture features, we constructed two-dimensional (TD) and three-dimensional (THD) texture indices. The technique of Recursive Feature Elimination (RFE) was used for feature selection. Models for soil salinity estimation were built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), and Convolutional Neural Network (CNN). Spatial distribution maps of soil salinity were then generated for each model. The effectiveness of the proposed method is confirmed through the utilization of 240 surface soil samples gathered from an arid region in northwest China, specifically in Xinjiang, characterized by sparse vegetation. Among all texture indices, TDTeI1 has the highest correlation with SSC (|r| = 0.86). After adding multidimensional texture information, the R2 of the RF model increased from 0.76 to 0.90, with an improvement of 18%. Among the three models, the RF model outperforms PLSR and CNN. The RF model, which combines spectral and texture information (SOTT), achieves an R2 of 0.90, RMSE of 5.13 g kg−1, and RPD of 3.12. Texture information contributes 44.8% to the soil salinity prediction, with the contributions of TD and THD texture indices of 19.3% and 20.2%, respectively. This study confirms the great potential of introducing texture information for monitoring soil salinity in arid and semi-arid regions.

Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios

AbstractSoil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil surface salinity (0‐10 cm) in Central Asia from 2025 to 2100 under two shared socio‐economic pathways (SSPs): SSP2‐4.5 and SSP5‐8.5. These models are developed using data from 20 global climate models (GCMs) obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results reveal that the RF model exhibits superior predictive capability in estimating soil salinity. RF performed on the calibration set with a coefficient of determination (R2) of 0.86, root mean square error (RMSE) of 9.84 and 9.90 dS m−1, ratio of performance to interquartile distance (RPIQ) of 3.09 and 3.07, and a Nash–Sutcliffe efficiency (NSE) of 0.86. The multi‐GCM ensemble means revealed the potential for varying degrees of salinization in Central Asia, with higher levels predominantly observed in the southeast and southwest of the study area, particularly downstream of the river and in the lakeside areas. Temporal analysis of soil salinity evolution reveals an overall increase in salinity across the region, with more notable changes projected under SSP5‐8.5. Specifically, the projected increase rate in soil salinity for Central Asia was 0.0005 dS m−1/year under SSP2‐4.5 and 0.01 dS m−1/year under SSP5‐8.5. Turkmenistan is notable for possessing the highest regional average of soil salinity, with the exception of a declining trend observed within this area. The remaining regions of Central Asia exhibit an upward trend in average soil salinity, particularly noteworthy under the SSP5‐8.5 scenario, where variations in soil salinity are more obvious. These findings hold significant potential in enhancing our understanding of how Central Asia responds to global change, advances toward sustainable development, and enhances comprehension of the dynamics within the region.

Spatially refined salinity hazard analysis in gypsum‐affected irrigated soils

AbstractThe global extent of salt‐affected agricultural land, 20% of which is deemed gypsiferous, results in billions of dollars of annual economic loss, a serious problem deserving of attention. However, the analysis of gypsiferous saline soils, such as in the irrigated Lower Arkansas River Valley (LARV) of Colorado, can result in an inflated estimation of soil salinity when using the traditional soil saturated paste extract electrical conductivity (ECe), leading to inaccurate crop yield loss predictions and misguided decisions for remediation. Sparingly soluble gypsum (CaSO4 2H2O) in these soils dissolves more readily during laboratory preparation of saturated paste extracts because of excess soil water dilution coupled with sample disturbance. We present a pragmatic linear‐regression approach to correct for this phenomenon, calibrated using two adapted methods for correcting ECe on an individual sample basis. The novel approach used electrical conductivity of pore water samples from saline fields to evaluate the accuracy of the correction methods. The approach was applied on soil samples from two surface‐irrigated, saline fields in the LARV, which were mapped using electromagnetic induction data and analysis of covariance linear regression, calibrated for ECe and ECe corrected for excess gypsum dissoultion (ECeg). Average ECeg values are as much as 26% lower than uncorrected ECe in gypsum‐biased portions of the fields. Estimation of corn salinity hazard in these gypsum‐affected areas using ECeg in lieu of ECe in a traditional yield response function generated mean relative yield values that are higher by up to 13 percentage points. We discuss lessons learned and suggest enhancements to the techniques.

Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development

This study aims to investigate the combined use of multi-sensor datasets (Landsat 4–5 &8 OLI satellite imagery, spatial resolution = 30 m) coupled with field studies to evaluate spatio-temporal dynamics of soil salinization along the coastal belt in West Bengal, India. This study assesses soil salinization by mapping the salinity and electrical conductivity of saturation extract (ECe) and utilizing spectral signatures for estimating soil salinity. The SI change (%) was analyzed (2021–1995), categorizing increases in salinity levels into 5%, 10%, and 50% changes possibly due to salt encrustation on the soil layers. The land use land cover (LULC) change map (2021–1995) demonstrates that the study area is continuously evolving in terms of urbanization. Moreover, in the study area, soil salinity ranges from 0.03 ppt to 3.87 ppt, and ECe varies from 0.35 dSm−1 to 52.85 dSm−1. Additionally, vulnerable saline soil locations were further identified. Classification of soil salinity based on ECe reveals that 26% of samples fall into the non-saline category, while the rest belong to the saline category. The Spectral signatures of the soil samples (n = 19) acquired from FieldSpec hand spectrometer show significant absorption features around 1400, 1900, and 2250 nm and indicate salt minerals. The results of reflectance spectroscopy were cross-validated using X-ray fluorescence and scanning electron microscopy. This study also employed partial least square regression (PLSR) approach to predict ECe (r2 = 0.79, RMSE = 3.29) and salinity parameters (r2 = 0.75, RMSE = 0.51), suggesting PLSR applicability in monitoring salt-affected soils globally. This study’s conclusion emphasizes that remote sensing data and multivariate analysis can be crucial tools for mapping spatial variations and predicting soil salinity. It has also been concluded that saline groundwater used for irrigation and aqua-cultural activities exacerbates soil salinization. The studywill help policymakers/farmers identify the salt degradation problem more effectively and adopt immediate mitigation measures.

Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China

(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions.

Estimating soil salinity in mulched cotton fields using UAV-based hyperspectral remote sensing and a Seagull Optimization Algorithm-Enhanced Random Forest Model

Xinjiang is a crucial production base for mulched cotton fields but faces increasingly severe challenges of soil salinization. Under the current drive towards precision agriculture, high precision monitoring of agricultural conditions at the field scale has become critical. Traditional remote sensing technologies are limited by insufficient spatial and spectral resolution, making field monitoring under mulched conditions exceptionally challenging. This study introduces a novel approach combining drone-based hyperspectral remote sensing with the Seagull Optimization Algorithm-Enhanced Random Forest Model (SOA-RF). After employing the preprocessing techniques of standard normal variable (SNV) and fractional order derivative (FOD), we employed the model to assess the level of field-scale salinization under optimization of feature selection using separate dimensionality reduction algorithms (sequential projection algorithm (SPA), optimal band combination analysis (OBCA)) and a combined approach (SPA-OBCA). The following main results were found: (1) SNV and FOD enhanced the spectral characteristics of cotton canopies, with the FOD technique performing better in detecting subtle signal changes in the positive and negative peaks of the spectrum. (2) SPA and OBCA were effective in screening the soil salt-sensitive bands in cotton canopies but the combined SPA-OBCA method provided a better selective preference for hyperspectral bands, with an optimal correlation of 0.926. (3) The model combined the advantages of SOA with the power of Random Forest to optimize the parameters and improve the estimation accuracy (R2 = 0.925, RPD = 4.128). We used unmanned aerial vehicle hyperspectral imagery to create a 5-cm resolution map of soil salinity distribution in cotton fields that allowed detection of differences in soil salinity between mulches, thus providing a scientific basis for soil salinity management and the development of precision agriculture.

Assessment of soil salinity using explainable machine learning methods and Landsat 8 images

The aim of this study is to comparatively analyze the performance of machine learning (ML) algorithms for modeling soil salinity using field-based electrical conductivity (EC) data and Landsat-8 OLI satellite images with derived environmental covariates. We also aim to interpret and explain the ML models with and without over-sampling methods using Shapley (SHAP) values, an explainable ML approach that has not yet been utilized for soil salinity estimation tasks as an ML problem. We investigate two case study areas from western and southeastern Lake Urmia Playas (LUP) in the Northwest of Iran. Our study uses 26 environmental covariates, two ML models, namely extreme gradient boosting (XGBoost) and random forest (RF), and two over-sampling methods: synthetic minority over-sampling technique (SMOTE) and random over-sampling (ROS). Results indicate that XGBoost performs better compared to RF in terms of both R2 and RMSE. Additionally, the visual interpretation of soil salinity maps demonstrated the superiority of XGBoost. SMOTE produced superior results than ROS and no over-sampling test cases. Finally, SHAP analysis illustrated that vegetation indices made a greater contribution to the soil salinity prediction in the West LUP, while visible bands contributed more in the Southeast LUP Region.

Potential of solar-induced chlorophyll fluorescence (SIF) to access long-term dynamics of soil salinity using OCO-2 satellite data and machine learning method

The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardizedsolar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation.

Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

Soil Salinity Estimation by 3D Spectral Space Optimization and Deep Soil Investigation in the Songnen Plain, Northeast China

Saline–alkaline soil is a severe threat to Sustainable Development Goals (SDGs), but it can also be a precious land resource if properly utilized according to its properties. This research takes the Songnen Plain as the study area. The aim is to figure out the saline–alkaline status and mechanisms for its scientific utilization. Sentinel-2 multispectral imagery is used, and a 3D spectral space optimization method is proposed according to the restrictive relationships among the surface soil salinity index (SSSI), vegetation index (VI), and surface soil wetness index (SSWI) to construct a surface soil salinization–alkalization index (SSSAI) for estimation of the surface soil salinity (SSS). It is testified that SSS can be precisely estimated using the SSSAI (R2 = 0.74) with field verification of 50 surface salinized soil samples. Surface water and groundwater investigations, as well as deep soil exploration, indicate that the salt ions come from groundwater, and alkalinization is a primary problem in the deep soils. Fine-textured clay soils act as interrupted aquifers to prevent salt ions from penetrating and diluting downward with water, which is the cause of the salinization–alkalization problem in the study area. Finally, a sustainable solution for the saline–alkaline land resource is proposed according to the deep soil properties.

Using Different Extraction Methods to Estimate Soil Salinity and Salt Type Changes and Their Effects on Soil Inorganic Carbon in Plowed Desert–Sierozem Soil

Understanding the actual soil salt ion content and salt type is one of the important prerequisites for determining the nature of saline soils and their development and utilization in drylands. Desert–sierozem soils are widely distributed in the plains between the northern piedmont of the Tianshan Mountains and the Gurbantunggut Desert in Xinjiang, Northwest China, which contain abundant calcium sulfate (gypsum) and calcium carbonate and are high-quality arable land resources for agriculture. These soils have been extensively reclaimed for farmland in recent decades. In this study, 10 plots of desert–sierozem soils with different tillage years in the Qitai Oasis plain area in the northern piedmont of Tianshan Mountain were studied. Three soil profiles were excavated in each sample plot with a depth of 1.2 m. A total of 30 farmland profiles were dug up and sampling was conducted according to the soil occurrence layers of the farmland. All the soil samples were predominantly sandy in texture, with more than 92% of the soil particles distributed between 0.05 and 0.25 mm, and less than 8% between 0.002 and 0.05 mm. Conventional water extraction (CWE) and intensified water extraction (IWE) were adopted to extract the water-soluble salt ions content of K+, Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3− and CO32−, and this study compared the extraction results using two different methods with varying extraction strengths. Additionally, the calcium sulfate and calcium carbonate contents were obtained using the chemical extraction and mineral assay methods, respectively. Differences in the salts and salt types in desert–sierozem soils with different tillage years were analyzed, and the effect of calcium sulfate on the inorganic carbon sinks in plowed desert–sierozem soil was demonstrated. The findings indicated that the standard conventional extraction procedure involving a 5:1 water–soil ratio and 3 min of shaking was inadequate for accurately measuring the soil salt content in high calcium sulfate content desert–sierozem soils. With the IWE method, by increasing the water–soil ratio, prolonging the shaking time, and grinding the particles finer, the amount of water-soluble salts extracted using the IWE method was 5.83 times higher than that of the conventional method. Among them, the average content of IWE-Mg2+ increased by 123.41 mg kg−1 compared to CWE-Mg2+, the average content of IWE-Ca2+ was 3.82 times higher than CWE-Ca2+, with an average increase of 2931.59 mg kg−1, and the IWE-SO42− content was 5.96 times higher than CWE-SO42−, with an average increase of 8658.95 mg kg−1. A markedly negative correlation (p < 0.01) was observed between calcium carbonate and calcium sulfate in desert–sierozem soils after tillage. The calcium sulfate content consistently decreased and calcium carbonate increased with an increasing number of tillage years. The pedogenic calcium carbonate content in desert–sierozem soils increased by an average of 10.86 g kg−1 after more than 40 years of cultivation, and the transfer of Ca2+ from calcium sulfate to calcium carbonate was identified as the cause of the increase in the soil inorganic carbon sink. Overall, a new method for extracting water-soluble salts was utilized for aridisols, which commonly consist of complex salt types. The results elucidate the changes in salinity within plowed desert–sierozem soils as well as the impact of soil salt on soil inorganic carbon sinks. Based on our research, desert–sierozem soils are well suited for agricultural farming because they contain high levels of sulfate, which is one of the important inorganic nutrients essential for crops, and it also provides a source of calcium for increasing inorganic carbon sinks. This study serves as a reference and scientific basis for soil carbon cycles in arid zones.

Correlation between electrical conductivity in saturated paste extracts and different diluted extracts (1/2.5, 1/5) of coarse-textured soils

Soil salinity poses a significant challenge to agricultural productivity, necessitating precise measurement methods. Rather than relying on complex determinations like saturated paste extracts (ECe), a practical alternative involves assessing soil salinity through the Electrical Conductivity (EC) of soil-water extracts. This simplified yet reliable method is crucial for understanding the impact of soil salinity on agricultural yields. To improve accuracy in representing soluble salts relevant to plant growth, the conversion of EC from soil water extracts to ECe becomes pivotal.In an experimental study, a clear relationship was sought between the Electrical Conductivity (EC) of saturated paste extracts (ECe) and the ratios found in soil-water extracts (1/2.5 and 1/5). Fifty samples of coarse-textured soil were collected from the Djamâa palm grove in Sidi Yahia village, located in the Oued Righ region within the Djamaa Zone. The analysis of the collected data revealed compelling findings, indicating a robust and statistically significant correlation between values obtained from ECe and those derived from EC1/2.5 and EC1/5 (correlation coefficient of R = 0.93 and 0.91, at a significance level of P = 0.05). These results highlight the promising potential of accurately estimating ECe in coarse-textured soils by employing EC1/2.5 and EC1/5, paving the way for streamlining assessment methodologies for soil salinity. This study not only elucidates the correlation between conductivity measures but also proposes a practical means to estimate soil salinity more efficiently. The findings contribute to informed decision-making in agricultural practices within the Northern Sahara's Djamâa palm grove and similar regions, offering valuable insights for sustainable soil management.

Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing

Salinization is a threat to global agricultural and soil resource allocation. Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets (>250 m) and semiqualitative classification rules (five ranks). Based on these two limitations, we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images, climate, parent material, terrain data, and machine learning. In hyper-arid and arid region, models established using Sentinel-2 and other geospatial data showed the highest accuracy with R 2 of 0.85 and 0.62, respectively. In semi-arid, dry sub-humid, and humid regions, models performed best using Sentinel-1, Sentinel-2, and other geospatial data with R 2 of 0.87, 0.80, and 0.87, respectively. The accuracy of the global models is considerable with field validation in Iran and Xinjiang, and compared with digitized salinity maps in California, Brazil, Turkey, South Africa, and Shandong. The proportion of extremely saline soils in Europe is 10.21%, followed by South America (5.91%), Oceania (5.80%), North America (4.05%), Asia (1.19%), and Africa (1.11%). Climatic conditions, groundwater, and salinity index are key covariates in global soil salinity estimation. Use of radar data improves estimation accuracy in wet regions. The map of global soil salinity at 10 m provides a detailed, high-precision basis for soil property investigation and resource management.

Soil Salinity Estimation based on Sentinel-1/2 Texture Features and Machine Learning

Soil Salinity Estimation based on Sentinel-1/2 Texture Features and Machine Learning

Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China

Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China

Remote Sensing and GIS Applications in Soil Salinity Analysis: A Comprehensive Review

Soil salinity is a pressing global issue with far-reaching implications for agricultural productivity and environmental sustainability, particularly in arid and semi-arid regions. The expansion of cultivated lands and the need for food production have intensified the challenges associated with soil salinization. This paper reviews the significance of monitoring and assessing soil salinity, especially in regions where traditional irrigation practices and inadequate drainage systems exacerbate the problem. The paper highlights the importance of satellite-based technologies for spatial and temporal mapping of soil salinity, providing cost-effective, rapid, and efficient sources of qualitative and quantitative spatial information. Multispectral remote sensing data have significantly improved the monitoring of soil salinity. The spectral characteristics of salt-affected soil, visible and near-infrared bands, enable the detection of salinity in both barren and vegetated areas. Various salinity and vegetation indices have been developed, with their effectiveness depending on the context and the extent of vegetation cover. Proper timing of fieldwork and measurement is essential for accurate results. The paper presents a comprehensive review of the remote sensing and GIS based methods of soil salinity estimation including salinity indices, vegetation indices, regression methos, neural network methods plus, sensing approaches, and satellite data utilized in soil salinity mapping. The majority of recent studies favour remote sensing technology over traditional methods due to its cost-effectiveness and efficiency. The choice of mapping approach is context-dependent, and there is no universally superior method. This review underscores the critical role of remote sensing in addressing the challenges posed by soil salinity, offering a promising avenue for monitoring and managing this imperative global concern.

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

The Kerkennah archipelago in Tunisia is one of the most vulnerable areas where the influence of climate change is undeniable. Soil salinization has emerged as a major consequence of climate variation on this island. In this study, remote sensing techniques were implemented to develop a model for predicting soil salinity from satellite images. Machine learning algorithms, Sentinel-1 and Sentinel-2 data, and ground truth measurements were used to estimate soil salinity. Several algorithms were considered to achieve accurate findings. These algorithms are categorized as polynomial regression, random forest regression, exponential regression, and linear regression. The results demonstrate that exponential regression is the pre-eminent algorithm for estimating soil salinity with high predictive accuracy of R2 = 0.75 and RMSE = 0.47 ds/m. However, spatiotemporal soil salinity maps reveal distinct and clear distribution patterns, highlighting salty areas (i.e., sebkhas) and agricultural parcels. Thus, through the model, we explore areas of moderately high salinity within agricultural lands that could be affected by irrigation practices. The present work demonstrates a reliable model for soil salinity monitoring in the Kerkennah archipelago and inspires more successful technologies such as remote sensing and machine learning to improve the estimation of soil salinity in climate-affected vulnerable areas.