Soil Salinity Index Research Articles

Monitoring and assessment of spatiotemporal soil salinization in the Lake Urmia region

Soil salinization stands as a prominent global environmental challenge, necessitating enhanced assessment methodologies. This study is dedicated to refining soil salinity assessment in the Lake Urmia region of Iran, utilizing multi-year data spanning from 2015 to 2018. To achieve this objective, soil salinity was measured at 915 sampling points during the 2015–2018 timeframe. Simultaneously, remote sensing data were derived from surface reflectance data over the same study period. Four distinct scenarios were considered such as a newly developed spectral index (Scenario I), the newly developed index combined with other salt-based spectral indices from the literature (Scenario II), indirect spectral indices based on vegetation and soil characteristics (Scenario III), and the amalgamation of both direct and indirect spectral indices (Scenario IV). Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) were employed to assess soil salinity. The measured data divided to 75% of the data as the calibration dataset, while the remaining 25% constituted the validation dataset. The findings revealed a correlation between soil salinity and spectral indices from the literature, with a range of -0.53 to 0.51, while the newly developed spectral index exhibited a stronger correlation (r = 0.59). Furthermore, RF yielded superior results when using the newly developed spectral index (Scenario I). Overall, SVM emerged as the most effective model (ME = -9.678, R2 = 0.751, and RPIQ = 1.78) when integrating direct and indirect spectral indices (Scenario IV). This study demonstrates the efficacy of combining machine learning techniques with a blend of newly developed and existing spectral indices from the literature for the monitoring of soil salinity, particularly in arid and semi-arid regions.

Potential of land degradation index for soil salinity mapping in irrigated agricultural land in a semi-arid region using Landsat-OLI and Sentinel-MSI data.

Irrigated agricultural lands in arid and semi-arid regions are prone to soil degradation. Remote sensing technology has proven useful for mapping and monitoring the extent of this issue. To accurately discern soil salinity, it is essential to choose appropriate spectral wavelengths. This study evaluated the potential of the land degradation index (LDI) using the visible and near infrared (VNIR) and the short wavelength infrared (SWIR) spectral bands compared to that of soil salinity indices by integrating only the VNIR wavelengths. Landsat-OLI and Sentinel-MSI data, acquired 2weeks apart, were rigorously preprocessed and used. This research was conducted over irrigated agricultural land in Morocco, which is well known for its semi-arid climate and moderately saline soil. Furthermore, a field soil survey was conducted and 42 samples with variable electrical conductivity (EC) were collected for index calibration and validation of the results. The results showed that the visual analysis of the derived maps based on the examined indices exhibited a clear spatial pattern of gradual soil salinity changes extending from the elevated upstream plateau to the downstream of the plain, which limits agricultural activities in the southwestern sector of the study area. The results of this study show that LDI is effective in identifying soil salinity, as indicated by a coefficient of determination (R2) of 0.75 when using Sentinel-MSI and 0.72 with Landsat-OLI. The R2 value of 0.89 and root mean square error (RMSE) of 0.87 dS/m for soil salinity maps generated from LDI with Sentinel-MSI demonstrate high accuracy. In contrast, the R2 value of 0.83 and RMSE of 1.24 dS/m for maps produced from Landsat-OLI indicate lower accuracy. These findings indicate that high-resolution Sentinel-MSI data significantly improved the prediction of salinity-affected soils. Furthermore, this study highlights the benefits of using VNIR and SWIR bands for precise soil salinity mapping.

A longitudinal analysis of soil salinity changes using remotely sensed imageries

Soil salinization threatens agricultural productivity, leading to desertification and land degradation. Given the challenges of conducting labor-intensive and expensive field studies and laboratory analyses on a large scale, recent efforts have focused on leveraging remote sensing techniques to study soil salinity. This study assesses the importance of soil salinity indices’ derived from remotely sensed imagery. Indices derived from Landsat 8 (L8) and Sentinel 2 (S2) imagery are used in Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), and Support Vector Machine (SVR) are associated with the electrical (EC) conductivity of 280 soil samples across 24,000 hectares in Northeast Iran. The results indicated that the DT is the best-performing method (RMSE = 12.25, MAE = 2.15, R2 = 0.85 using L8 data and RMSE = 10.9, MAE = 2.12, and R2 = 0.86 using S2 data). Also, the results showed that Multi-resolution Valley Bottom Flatness (MrVBF), moisture index, Topographic Wetness Index (TWI), and Topographic Position Indicator (TPI) are the most important salinity indices. Subsequently, a time series analysis indicated a reduction in salinity and sodium levels in regions with installed drainage networks, underscoring the effectiveness of the drainage system. These findings can assist decision-making about land use and conservation efforts, particularly in regions with high soil salinity.

Quantitative Inversion of Soil Salinity in Different Seasons in Huinong District, Ningxia based on Sentinel-2 satellite

Salinisation of agricultural soils poses an important constraint to sustainable agricultural development, especially in the Huinong region of Ningxia, northwestern China, where salinised soils are widely distributed. However, due to the limitations of monitoring technology, the detailed situation of soil salinisation in this region is not known. Currently, the multispectral instrument (MSI) on board the Sentinel-2 satellite provides a good opportunity for monitoring soil salinity dynamics. Therefore, in this study, the feasibility of using the multispectral instrument (MSI) on board the Sentinel-2 satellite, combined with a machine learning model, to accurately monitor the soil salinity content during the spring and summer seasons was explored. And three additional red-edge bands (B5-B7) were used instead of the traditional red band (B4) to generate potential soil salinity indices. A screening method based on the PLS-VIP criterion was used to screen the spectral covariates, and three machine learning methods, namely, Random Forest (RF), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), were employed to build the inverse model of soil salt content. The results showed that the Random Forest model based on Sentinel-2 imagery performed the best in the inversion with good prediction results, with R2 and RE of 0.825 and 0.207 and 0.711 and 0.271 in spring and summer, respectively.The study also revealed that soil salinity varied significantly between seasons, and was higher in spring than in summer. This result is of great significance as a guide for soil salinity monitoring and land reclamation in arid or semi-arid regions.

Scenario analysis of local storylines to represent uncertainty in complex human-water systems

Storylines are important in evaluating the uncertainty inherent in complex human-water systems. The interrelated nature of qualitative and quantitative scenarios can enhance our ability to address the uncertainty of integrated modelling of complex systems. This study proposes a transdisciplinary approach that integrates social and environmental sciences to characterize and comprehend uncertainty in the dynamic interactions of key factors affecting a human-water system. We introduce a framework for representing uncertainty through linguistic and epistemic uncertainty quantification using storyline narratives in the context of a regional integrated dynamic model. A systematic exploration of uncertainty space is performed using storytelling, fuzzy sets, and low discrepancy sequences sampling methods. Scenario analysis is applied to the generated uncertain ensemble of projections to discover predominant storylines of interest. As a representative case of a human-water system operating in a developing country, we examine the uncertainty effects of a variety of drivers of climatic and socio-economic changes on key agriculture and water-related sectors in Pakistan’s Rechna Doab region. The findings revealed soil salinity and crop yield indices were the most uncertain and showed significant variance across all developed storylines. The 95th percentile for soil salinity in year 2100 was estimated to be nearly 60 % higher than the baseline level (year 2020). There was, however, considerable overlap in different socio-economic scenarios at the local scale, indicating that change in socio-economic conditions could not fully offset climate-related uncertainty. Our analysis provides better quantification and a deeper understanding of the uncertainty in integrated assessment modelling of coupled human-water systems and the complex relationships between inputs and outcomes.

Evaluation of Different Soil Salinity Indices Using Remote Sensing Techniques in Siwa Oasis, Egypt

Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This research, conducted in Siwa Oasis, Egypt, aimed to assess the effectiveness of remote sensing techniques in detecting and monitoring changes in soil salinity. Using Landsat 5 and Landsat 7 satellite images, the researchers evaluated various soil salinity indices based on 56 on-site ground measurements. The study aimed to improve the correlation between electrical conductivity (EC) and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months. Different approaches were employed, including stacking the data, categorizing EC measurements into salinity levels, analyzing data temporally, and conducting spatial correlation analysis. The initial approach revealed a weak correlation, due to substantial variation in EC values. However, the salinity index SI demonstrated the highest correlation coefficient of 0.38. In the second scenario, the salinity index 2 S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the salinity index 1S1 achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds, with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients than those of both the first and fourth scenarios. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

Спектральные характеристики засоленной пашни в предгорной долине Заилийского Алатау (Северный Тянь-Шань) в период 2016–2022 гг. по спутниковым данным Sentinel-2

Introduction. The foothill catchment of the Zailiisky Alatau (Northern Tien Shan), adjacent to the ridge from the north, occupies an altitude range of 475–925 m above sea level and has an area of about 8500 km2. The Almaty urban agglomeration with a population of 2.5 million people is located here and there is a developed agriculture. Crop production in the sharply continental, arid and hot climate of Central Asia is based on irrigation agriculture. For irrigation in the foothill catchment, river runoff from the Zailiysky Alatau Ridge is used. In some parts of the foothill catchment there are salt stores and salt outbreaks in the landscape, which contribute to the salinization of arable land. Secondary salinization of arable land is a negative factor that reduces their quality and productivity. Monitoring the salinity of arable land is practically an important task. The most effective technologies for detailed assessment of soil salinity are based on the processing of satellite data. The purpose of the work. The purpose of this study was to analyze the long-term spectral characteristics of agricultural land potentially susceptible to secondary salinization. This information is necessary to determine optimal strategies in the task of remote assessment of salinization of arable land in the foothill catchment of the Zailiysky Alatau Ridge. Materials and research methodology. The 323 cloudless Sentinel-2 images (10 m resolution) of the period 2016–2022 were selected. Four spectral indices: Normalized Difference Vegetation Index (NDVI); Soil Adjusted Vegetation Index (SAVI); Vegetation Soil Salinity Index (VSSI); and Simple Ratio Index (SRI); were used to monitor the test site, which was located in the northeastern part of the foothill catchment of the Trans-Ili Alatau Ridge. The test site included three polygons of irrigated arable land subjected to secondary salinization, and two polygons of saline abandoned land. The satellite monitoring of spectral indices was used to determine the most informative periods within the season, as well as to assess the inter-seasonal stability of the results. Results and discussion. The dynamics of the considered vegetation indices (NDVI, SAVI and SRI) differed most significantly in July-August, i.e., in conditions of the most developed vegetation. The VSSI index was the most informative in April-May and December-February. The vegetation condition of saline arable lands is controlled by the moisture content and salinity of the rootinhabited soil layer. The precipitation deficit leads to deterioration in the state of vegetation, which masks the negative impact of secondary salinization of fields. Thus, the assessment of the significance of the factor of secondary salinization in the state of vegetation requires annual ground sub-satellite surveys. Conclusion. The variations in the values of the spectral indices of test polygons are very large and depend on the seasonal weather characteristics. Therefore, practically significant satellite methods for assessing the salinity of arable land in the foothill catchment of the Trans-Ili Alatau Ridge should be based on annual sub-satellite surveys. This limits the range of significant remote sensing technologies, methods of extrapolation of sub-satellite surveys data to regular grids, within territories with similar hydrological characteristics and land-use types.

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.

Plant Diversity Response to Environmental Factors in Yellow River Delta, China

Plant diversity plays a crucial role in maintaining the functionality of a community and providing essential ecosystem services. Studying the plant diversity and its response to environmental factors in the Yellow River Delta, China, as a newly formed coastal land, is beneficial for protecting plant diversity in coastal areas and maintaining ecosystem stability. In this study, 56 sites were sampled to investigate the diversity of shrubs and herbaceous plant community and its response to environmental factors. The results indicate that the plants growing in the Yellow River Delta are predominantly from the Poaceae and Asteraceae families, with dominant communities consisting of species such as Suaeda salsa, Phragmites australis, Setaria viridis, Imperata cylindrica, and Tamarix chinensis. The Shannon–Wiener index, Simpson diversity index, and Pielou’s evenness index exhibit average values of 0.34, 0.21, and 0.25, respectively, within the Yellow River Delta. These values collectively indicate a low diversity in the vegetation community, reflecting a relatively uncomplicated ecological structure in this area. Additionally, there were no significant differences in biodiversity indices under different soil formation times, but under different land cover types, the biodiversity index of cropland was significantly higher than that of impervious land. Soil salinity index exhibited a significant negative correlation with plant diversity (R2 = 0.279, p < 0.001) in the Yellow River Delta. Moreover, elevation (R2 = 0.247, p < 0.001) and temperature (R2 = 0.219, p < 0.001) showed significant positive effects on plant diversity. Regarding the ecological stoichiometry of plant elements, soil organic carbon exhibited a negative effect on the biodiversity index, while litter carbon showed a positive effect. This may be attributed to the unique topographical conditions and soil salinization in the Yellow River Delta. Our findings provide important references for the sustainable management of wetlands in the Yellow River Delta under conditions of soil salinization.

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin.

Contribution of predictive factors of land degradation occurrence applying maximum entropy model

Land degradation (LD) is a significant environmental issue that needs to be spatially mapped in different areas. This research provides a framework to prioritize the geo-environmental and anthropogenic factors on LD in a semi-arid region, Qazvin Plain, in Iran based on the maximum entropy model for the first time and also to produce the LD hazard map. The LD inventory map that shows the occurrence points of LD was prepared based on the decreasing trend of net primary productivity (NPP) over 20 years. 120 random points were selected as degraded sites where NPP showed a decreasing trend. Several environmental and anthropogenic factors including temperature, rainfall, land use, normalized difference vegetation index (NDVI), vegetation soil salinity index (VSSI), visible and shortwave infrared drought index (VSDI), electrical conductivity (EC) and sodium adsorption ratio (SAR) of groundwater, groundwater table, annual depletion of groundwater resources, slope, aspect, and altitude were considered as predictive factors of LD and their relative contributions were determined using the maximum entropy model. According to the findings, 49.74% of the region was in the class of low degradation. Also, 22.57%, 17.87%, and 9.82% of the plain were related to the moderate, high, and very high degradation classes, respectively. The model specified altitude as the most significant variable influencing LD followed by land use, slope, and VSSI. The least important variable affecting LD in the study area was the aspect. The results of modeling LD also showed that the areas most sensitive to LD were frequently rangelands which are mostly found on steep slopes and highlands. Based on the UAC value, the model illustrated a very good efficiency with a value of 0.8. According to the results, the maximum entropy model successfully mapped the LD hazard in the Qazvin plain.

Методика валидации результатов спутникового измерения солености почвы на базе индексов SAVI и VSSI

A new methodology for validating the results of satellite measurements of soil salinity using satellite estimates of the SAVI and VSSI indices has been developed. A linear weighted sum of exponential functions of these indices has been proposed as a new soil salinity index. It has been shown that the proposed new index has an extremum in the form of a minimum of the signal in the red range. The presence of such a minimum eliminates the need to search for soil with a sufficiently high conductivity on the test site when validating the obtained large estimates of the SAVI and VSSI indices.

Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery

Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and the sustainable development of agriculture. Due to the spatial and temporal heterogeneity of soil properties and environmental conditions in a large-scale region, the monitoring accuracy of soil salinization can be challenging. This study investigated whether the classification of diverse crop types on a time series can improve the prediction accuracy of regional soil salinity levels. Specifically, we evaluated the changes in soil salt content (SSC) under diverse vegetation cover over time in the Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery and ground-truth data collected from June to September 2021 and 2022. Focused on sunflower and maize fields, this study analyzed the impact of classifying these two crop types and examining four distinct time series on the accuracy of SSC estimation. Five indices were selected as characteristic parameters from a pool of 17 vegetation indices (VIs) and 13 soil salinity indices (SIs) derived from satellite images. Moreover, three machine learning algorithms were used to establish SSC estimation models. The findings underscored the efficacy of classifying crop types and considering different time series in enhancing the response sensitivity of spectral indices to SSC and improving modeling accuracy. Among the spectral indices, VIs made more contributions to the SSC estimation model than SIs, achieving the highest coefficient of determination (R2) of 0.71. The artificial neural networks algorithm outperformed the other two machine learning algorithms in terms of accuracy and stability, yielding an optimal R2 of 0.72 and a Root Mean Square Error (RMSE) of 0.15%. This study proposed a modeling and mapping approach that considers crop types and various time series, offering valuable insights for accurately assessing soil salinization, guiding strategies for its prevention and remediation.

Assessment of salt‐affected soil in Indo‐Gangetic Plain of Uttar Pradesh and prediction using regression model

AbstractIn the present study, multiple sites in the Indo‐Gangetic Plain (IGP) were examined, and most of the sites were found to be non‐saline, with electrical conductivity (EC) values below 4.0 dSm−1. The highest value of EC 8.5 dSm−1 was reported for samples collected from JAN‐2, which, followed by site RBL‐3, also shows a high value of EC 6.5 dSm−1. The highest CEC (cmolkg−1) was reported in samples collected from site JAN‐1 (8.4), JAN‐2 (8.2), and JAN‐3 (8.21), and the lowest at RBL‐2 (3.99). The highest exchangeable sodium percentage (ESP) value of 34.07% was observed at RBL‐3, indicating a potential salinity risk associated with elevated sodium contents. The analysis showed that salt was present at all study sites, and there were strong correlations between the variables examined. The relationship between measured ESP and predicted ESP shows a strong correlation with an r2 value of 0.91, indicating the applicability of ESP–sodium adsorption ratio (SAR). Paired samples t‐test results p‐value = 0.27 indicated no significant difference (p > 0.05) between the soil ESP values predicted by the soil ESP–SAR model and those obtained through laboratory testing. The statistical results of the study suggested that the soil ESP–SAR model was reliable for predicting ESP values. The soil ESP differences between the two methods were normally distributed, and the 95% limits of agreement were calculated as −7.79 and 5.55. The mean difference between the ESP–SAR model and the “Measured” method was calculated as 2.24, representing the average deviation between the two. The study presents the ESP–SAR model as a cost‐effective and efficient method for estimating soil ESP and assessing soil salinity. By analyzing soil properties and validating the model, it provides valuable insights for policymakers and stakeholders in combating agricultural land degradation in the IGP. The novelty lies in its comprehensive evaluation of soil salinity indices, reinforcing the applicability of the ESP–SAR model for future research and agricultural management.

Partial least-squares regression for soil salinity mapping in Bangladesh

Estimating the salinity of the soil along the coast of south-western Bangladesh is the focus of this study. Thirteen soil salinity indicators were computed using the Landsat OLI images, and 241 soil salinity samples were gathered from secondary sources. In this research, partial least-squares regression was used to estimate soil salinity. Soil salinity zones were then divided into five classes based on Jenks natural breaks. There is a strong relationship between soil salinity and several indices, including B2 (Blue band), B5 (Near-Infrared band), SI_1 (Salinity index 1), and SI_2 (Salinity index 2). The correlation between B2 and soil salinity is particularly high, hovering around 0.92. The majority of other factors, however, have a negative or barely positive correlation of −0.260 to 0.032. Very high and high levels of salinity are estimated to cover 29.02% and 13.45% of the study area, respectively, based on the soil salinity map. Finally, cross-validation has been used to see how well the model works to predict soil salinity for a specified weight. Iteration has been done three times in order to assess the model's performance on various subsets of data and produce a more accurate prediction. In the third iteration, PRESS, RSS, and Q2cum values were 1.844826, 3.354687, and 0.9705131, correspondingly, which indicates a higher predictive accuracy. The findings of the study will be useful as a tool for policymakers in the south-western coastal area of Bangladesh who are responsible for integrated coastal zone management.

Evaluating the spatiotemporal variations of soil salinity in Sirjan Playa, Iran using Sentinel-2A and Landsat-8 OLI imagery

The creation of large-scale deserts is a consequence of soil salinization, a major land degradation phenomenon that also disrupts ecological equilibrium. Saline lands can be evaluated, mapped, and monitored in different world regions using satellite imaging and remote sensing techniques. In order to assess the ability of Landsat-8 and Sentinel-2A imagery for monitoring the spatiotemporal variations of electrical conductivity (EC) and to determine the importance of salinity indices for prediction in two seasons (summer and winter) using the systematic sampling method, from surface depth (0––20 cm), 90 soil samples were taken of marginal lands of Sirjan Playa, southeast of Iran. Satellite images for two seasons were acquired close to the sampling time to obtain the soil salinity indices. Four machine-learning algorithms, namely artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM), were applied to estimate the spatiotemporal changes in soil salinity. Results confirmed that the ANN model developed by the Sentinel-2A image had the highest performance (R2 = 0.77, RMSE = 16.1, NRMSE = 27.1) to predict ECe in the winter season. Furthermore, RF presents the lowest error for prediction ECe during the summer, independent type of satellite data used. Additionally, in virtually all of the analyzed models, the Sentinel-2A data produced lower RMSE and NRMSE values than the Landsat-8 data throughout the two seasons. Results also confirmed that among the soil salinity indices, the Vegetation Soil Salinity Index (VSSI) was identified as the most effective predictor. Overall, this study demonstrates the potential of satellite imaging and machine learning algorithms for monitoring and predicting soil salinity, which can contribute to the sustainable management of marginal lands in arid and semi-arid regions. The findings also highlight the importance of selecting appropriate satellite data and salinity indices for accurate and reliable predictions.

A novel method for detecting soil salinity using AVIRIS-NG imaging spectroscopy and ensemble machine learning

Soil salinization is one of the major land degradation processes spread over millions of hectares of global land. Hyperspectral Remote Sensing (HRS) coupled with modern data mining approaches help in real-time and cost-effective assessment or monitoring of salt-affected soils. This study aimed at predicting soil salinity across five sites in India using the Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) data in low to moderately salt-affected cropland soils. We have identified four unique spectral absorption features having sensitivity towards soil salinity through a hybrid feature selection algorithm. Soil electrical conductivity (EC) was estimated using different machine learning (ML) based models such as random forest (RF), gradient boosting machines (GBM), and deep learning (DL). An ensemble of RF and DL models showed the best performance with the coefficient of determination (R2) of 0.89 and 0.55 and normalized root-mean-squared error of 0.15 and 0.16 in training and test datasets, respectively. We also proposed a new hyperspectral soil salinity index using Shannon entropy-based aggregation of selected absorption features. The newly proposed index outperformed other majorly used remote sensing-based salinity indices. It also showed a strong correlation with measured EC (r = 0.68) and ML-predicted soil EC (r = 0.78), both being significant at 1% level of significance. The index was effective in classifying HRS images into six distinct salinity classes. We also assessed the feasibility of applying the proposed salinity index for future hyperspectral missions through the simulation of various spectral-spatial resampling scenarios and estimated the optimal spectral and spatial resolution for salinity prediction. The hyperspectral salinity index can be directly estimated from HRS data without the need for time-consuming and expensive field samplings and used as a proxy to evaluate soil salinity status under field conditions.

Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China

To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity–Hue–Saturation and Gram–Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2–L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2–L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models.

Sinking delta: Quantifying the impacts of saltwater intrusion in the Indus Delta of Pakistan

This study focused on an integrated assessment of coastline change and its impacts on the deltaic sustainability of the Indus Delta, the world's fifth-largest delta. The increase in salinity and degradation of mangrove habitat was examined using multi-temporal Landsat satellite imagery from 1990 to 2020. The tasselled cap transformation indices, multi-statistical End Point Rate and Linear Regression were used to extract the shorelines rates. Mangrove cover area was estimated by applying the Random Forest clasification approach. Impacts of coastal erosion on mangroves and sea-water salinity were determined through the association between electrical conductivity and vegetation soil salinity index (VSSI). The accuracy of the analysis was evaluated using ground truth information obtained from field surveys and Fixed-Point Photography. Major findings of the analysis indicate that the North-West Karachi experienced accretion at an average rate of 7.28 ± 1.15 m/year, with medium salinity (VSSI<0.81) and increased mangrove cover, from 11.0 km2 area in 1990 to 14.5 km2 in 2020. However, the Western Delta has undergone massive erosion at a mean rate of −10.09 ± 1.61 m/year with obtrusive salinity (0.7 ≤ VSSI ≤ 1.2) and 70 km2 of mangrove cover loss. In the Middle West Delta and Middle East Delta erosion is observed at an average rate of −28.45 ± 0.55 m/year rate, with high obtrusive salinity (0.43 ≤ VSSI ≥ 1.32) and rapid mangroves cover loss (14 km2). The Eastern Delta was relatively stable and accelerating towards the sea with increasing mangrove cover (629 km2). Our analysis revealed that erosion, which occured due to reduced sediments flow linked to development of water infrastructures as well as climate change, have serious implications for the ecosystem. Future policy and action-plans should priotitise addressing vulnerabilities by integrate nature-based solutions for revival of the Delta.

Effect of Deep Straw Return under Saline Conditions on Soil Nutrient and Maize Growth in Saline–Alkali Land

To clarify the effect of tillage methods on saline–alkali land improvement and maize growth in cropland salinized to different degrees, we set up two treatments (shallow rotation (15 cm depth; CK) and deep straw return (35 cm depth; DPR)) in land characterized by three different salinization degrees and analyzed the effects of the two treatments on soil nutrient content, salinity index, chlorophyll fluorescence, growth status, and yield at three salinization levels. The results show that (1) compared with CK, alkaline N, total N, Olsen P, exchangeable K, and organic matter in saline soils were all significantly improved, and total salt and pH values were reduced by 34.01–50.79% and 2.56–7.54%, respectively, under deep straw return conditions, representing the largest values in moderately saline–alkali land. (2) Compared with CK, chlorophyll fluorescence was significantly improved, and maximum photochemical efficiency (Fv/Fm), photochemical quenching (qP), and effective quantum yield of PSII (ΦPSII) were significantly increased by 8.09–15.41%, 9.13–17.93%, and 38.79–70.83% following deep straw return treatment; these increases were the largest ones observed in moderately saline–alkali land. (3) Deep straw return promoted the growth of maize and significantly increased the yield of maize. Plant height, leaf area index, and yield increased the most in moderately saline–alkali land and increased by 6.84–21.79%, 0.59–2.28 units, and 12.78–28.07%, respectively. The yield increased by 33.89 %, which was mainly due to the increase in 1000-grain weight. The results provide a theoretical basis for taking straw return measures to improve soil and increase maize yield in saline–alkali land.