Negative correlation between soil salinity and soil organic carbon variability.

Soil organic carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC < 150 g kg-1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m-1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg-1 above the mean predicted SOC ([Formula: see text] = 18.47 g kg-1) to 0.46 g kg-1 below [Formula: see text] (~-430%), while for noncroplands, such decline is sharper, from 0.96 above [Formula: see text] = 35.96 g kg-1 to 4.99 below [Formula: see text] (~-620%). Although salinity's significance in explaining SOC variability is minor (<6%), we estimate a one SD increase in salinity of topsoil samples (0 to 7 cm) correlates with respective [Formula: see text] declines of ~4.4% and ~9.26%, relative to [Formula: see text] and [Formula: see text]. The [Formula: see text] decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needle-leaved trees are estimated with the highest [Formula: see text] decline in noncroplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index as the most significant parameters in explaining the SOC's variability. The findings provide insights into SOC dynamics under increased soil salinity, improving understanding of SOC stock responses to land degradation and climate warming.

Peanut production in saline-alkali land of Yellow River Delta: influence of spatiotemporal changes of meteorological conditions and soil properties.

BACKGROUND: This study clarified the synergistic relationship among annual changes to specify the changes in agro-meteorological factors, soil characteristics and peanut growth in saline-alkali land near the estuary of the Yellow River Delta. We aimed to find the key factors affecting peanut production to optimize and regulate peanut planting mode in saline alkali soil. RESULTS: The daily average temperature from early May to late September in Lijin and Kenli was above 24 °C, with 470-600 mm of precipitation. The sunshine duration was 7.9 h/day and 7.3 h/day and the accumulated temperature was 3742 °C and 3809 °C, in Lijin and Kenli, respectively. Agro-meteorological conditions were suitable for peanut growth and development with the consistent main developmental period in the two experiment regions. The best sowing period was when the soil temperature stabilized above 18 °C in early May, and the best harvest was in mid-September. The soil volumetric water content in Lijin concentrated among 25-40%. Salt was mainly distributed in the 40-60 cm soil layers, and increased rapidly to 2.5 g kg- 1 in 0-20 cm cultivation layer in mid-May due to lack of precipitation. In Kenli experiment region, the soil volumetric water content ranged from 10 to 35%. Soil salinity was mainly distributed in the 20 cm soil layer, and the changes in salinity was little affected by precipitation. From mid-July to mid-August, the effective accumulated temperature of 5 cm soil layer was above 520 °C in both regions, which could ensure the normal pod development. The slow dynamic growth of kernel, high unfilled pod rate (26.99%) and low shelling rate (66.0%) might be the main reasons for low peanut yield in Lijin. CONCLUSION: Soil salinity was the main factor affecting pod development and yield. It was also a key point in optimizing the peanut planting mode in the saline alkali land of the Yellow River Delta.

Soil salinity regulates spatial-temporal heterogeneity of seed germination and seedbank persistence of an annual diaspore-trimorphic halophyte in northern China.

BACKGROUND AND AIMS: Seed heteromorphism is a plant strategy that an individual plant produces two or more distinct types of diaspores, which have diverse morphology, dispersal ability, ecological functions and different effects on plant life history traits. The aim of this study was to test the effects of seasonal soil salinity and burial depth on the dynamics of dormancy/germination and persistence/depletion of buried trimorphic diaspores of a desert annual halophyte Atriplex centralasiatica. METHODS: We investigated the effects of salinity and seasonal fluctuations of temperature on germination, recovery of germination and mortality of types A, B, C diaspores of A. centralasiatica in the laboratory and buried diaspores in situ at four soil salinities and three depths. Diaspores were collected monthly from the seedbank from December 2016 to November 2018, and the number of viable diaspores remaining (not depleted) and their germinability were determined. RESULTS: Non-dormant type A diaspores were depleted in the low salinity "window" in the first year. Dormant diaspore types B and C germinated to high percentages at 0.3 and 0.1 mol L-1 soil salinity, respectively. High salinity and shallow burial delayed depletion of diaspore types B and C. High salinity delayed depletion time of the three diaspore types and delayed dormancy release of types B and C diaspores from autumn to spring. Soil salinity modified the response of diaspores in the seedbank by delaying seed dormancy release in autum and winter and by providing a low-salt concentration window for germination of non-dormant diaspores in spring and early summer. CONCLUSIONS: Buried trimorphic diaspores of annual desert halophyte A. centralasiatica exhibited diverse dormancy/germination behavior in respond to seasonal soil salinity fluctuation. Prolonging persistence of the seedbank and delaying depletion of diaspores under salt stress in situ primarily is due to inhibition of dormancy-break. The differences in dormancy/germination and seed persistence in the soil seedbank may be a bet-hadging strategy adapted to stressful temporal and spatial heterogeneity, and allows A. centralasiatica to persist in the unpredictable cold desert enevironment.

Natural variation in a K+ -preferring HKT transporter contributes to wheat shoot K+ accumulation and salt tolerance.

Soil salinity can adversely affect crop growth and yield, and an improved understanding of the genetic factors that confer salt tolerance could inform breeding strategies to engineer salt-tolerant crops and improve productivity. Here, a group of K+ -preferring HKT transporters, TaHKT8, TaHKT9 and TaHKT10, were identified and negatively regulate the wheat shoot K+ accumulation and salt tolerance. A genome-wide association study (GWAS) and candidate gene association analysis further revealed that TaHKT9-B substantially underlies the natural variation of wheat shoot K+ accumulation under saline soil conditions. Specifically, an auxin responsive element (ARE) within an 8-bp insertion in the promoter of TaHKT9-B is strongly associated with shoot K+ content among wheat accessions. This ARE can be directly bound by TaARF4 for transcriptional activation of TaHKT9-B, which subsequently attenuates shoot K+ accumulation and salt tolerance. Moreover, the tae-miR390/TaTAS3/TaARF4 pathway was identified to regulate the salt-induced root development and salt tolerance in wheat. Taken together, our study describes the genetic basis and accompanying mechanism driving phenotypic variation in wheat shoot K+ accumulation and salt tolerance. The identified tae-miR390/TaTAS3/TaARF4/TaHKT9-B module is an important regulator in wheat subjected to salt stress, which provides the potentially important genetic resources for breeders to improve wheat salt tolerance.

Elevated CO2 concentration increases maize growth under water deficit or soil salinity but with a higher risk of hydraulic failure.

Climate change presents a challenge for plants to acclimate their water relations under changing environmental conditions, and may increase the risks of hydraulic failure under stress. In this study, maize plants were acclimated to two different CO2 concentrations ([CO2]; 400 ppm and 700 ppm) while under either water stress (WS) or soil salinity (SS) treatments, and their growth and hydraulic traits were examined in detail. Both WS and SS inhibited growth and had significant impacts on hydraulic traits. In particular, the water potential at 50% loss of stem hydraulic conductance (P50) decreased by 1 MPa in both treatments at 400 ppm. When subjected to elevated [CO2], the plants under both WS and SS showed improved growth by 7-23%. Elevated [CO2] also significantly increased xylem vulnerability (measured as loss of conductivity with decreasing xylem pressure), resulting in smaller hydraulic safety margins. According to the plant desiccation model, the critical desiccation degree (time×vapor pressure deficit) that the plants could tolerate under drought was reduced by 43-64% under elevated [CO2]. In addition, sensitivity analysis showed that P50 was the most important trait in determining the critical desiccation degree. Thus, our results demonstrated that whilst elevated [CO2] benefited plant growth under WS or SS, it also interfered with hydraulic acclimation, thereby potentially placing the plants at a higher risk of hydraulic failure and increased mortality.

Evaluation of direct and transgenerational influences of salinity on germination and early seedling growth in an edible halophyte, Crithmum maritimum.

BACKGROUND AND AIMS: Crithmum maritimum is a wild, edible halophyte with large potential as a cash crop for salinized soils. However, the tolerance during seed germination appears to be highly site-specific and contradictory, whereas little is known on salinity tolerance during early seedling growth. This study was aimed at characterizing variation in the responses of germination and early seedling growth in diverse C. maritimum populations along the Iberian Southwest coast. Specifically, we sought to distinguish between direct salinity effects and those influenced by the salinity of maternal environments. METHODS: Physicochemical properties, including salinity of maternal environments, were assessed across diverse habitats. A total of 3480 seeds from 58 mother plants were utilized. Seeds were subjected to germination assays under various salinity treatments (0-500 mM NaCl), with subsequent monitoring of germination parameters. Non-germinated seeds were tested for recovery germination, and viability was assessed using the tetrazolium test. Of germinated seeds, 1160 seedlings were monitored for survival and early growth metrics. General Linear Models were employed to analyze the effects of salinity and maternal environmental influence on germination and early growth. KEY RESULTS: Despite reduced and delayed germination under salinity, seeds showed remarkable tolerance up to 150 mM, surpassing prior reports, with consistent viability up to 500 mM, indicating substantial salinity-induced dormancy. Seedling growth was more sensitive to continued treatment; no plants survived above 150 mM. The salinity experienced by maternal plants had only a marginal effect on germination but significantly contributed to reduce seedling biomass production, both above and below ground. CONCLUSIONS: This study highlights the significance of maternal salinity on early growth in C. maritimum, emphasizing the species' resilience to salt stress during germination and recovery. These insights are crucial for optimizing cultivation techniques and informing research on other halophytes in saline environments.

Seed priming with graphene oxide improves salinity tolerance and increases productivity of peanut through modulating multiple physiological processes.

Graphene oxide (GO), beyond its specialized industrial applications, is rapidly gaining prominence as a nanomaterial for modern agriculture. However, its specific effects on seed priming for salinity tolerance and yield formation in crops remain elusive. Under both pot-grown and field-grown conditions, this study combined physiological indices with transcriptomics and metabolomics to investigate how GO affects seed germination, seedling salinity tolerance, and peanut pod yield. Peanut seeds were firstly treated with 400 mg L⁻¹ GO (termed GO priming). At seed germination stage, GO-primed seeds exhibited higher germination rate and percentage of seeds with radicals breaking through the testa. Meanwhile, omics analyses revealed significant enrichment in pathways associated with carbon and nitrogen metabolisms in GO-primed seeds. At seedling stage, GO priming contributed to strengthening plant growth, enhancing photosynthesis, maintaining the integrity of plasma membrane, and promoting the nutrient accumulation in peanut seedlings under 200 mM NaCl stress. Moreover, GO priming increased the activities of antioxidant enzymes, along with reduced the accumulation of reactive oxygen species (ROS) in response to salinity stress. Furthermore, the differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) of peanut seedlings under GO priming were mainly related to photosynthesis, phytohormones, antioxidant system, and carbon and nitrogen metabolisms in response to soil salinity. At maturity, GO priming showed an average increase in peanut pod yield by 12.91% compared with non-primed control. Collectively, our findings demonstrated that GO plays distinguish roles in enhancing seed germination, mitigating salinity stress, and boosting pod yield in peanut plants via modulating multiple physiological processes.

Variations in soil microbial communities in different saline soils under typical Populus spp. vegetation in alpine region of the Qaidam Basin, NW China.

Salinization is a severe threat to agriculture and the environment in many areas, and the same in Qaidam Basin, Qinghai Province, Northwestern China. Microorganisms have an important influence on regulating the biochemical cycles of ecosystems; however, systematic research exploring microbial diversity and interactions with saline-soil ecosystems' environmental variables remains scarce. Thus, 16 S rRNA high-throughput sequencing was performed in this paper to characterize microbial diversity under different levels of salinized soils: non-salinized (NS, 2.25 g/L), moderately salinized (MS, 6.14 g/L) and highly salinized (HS, 9.82 g/L). The alpha diversity results showed that the HS soil was significantly different from the NS and MS soils. An analysis of similarity (ANOSIM) and a principal co-ordinates analysis (PCoA) indicated that NS and MS clustered closely while HS separated from the other two. Significant differences in microbial composition were observed at the taxonomic level. Proteobacteria (42.29-79.23 %) were the most abundant phyla in the studied soils. Gammaproteobacteria (52.49 and 66.61 %) had higher abundance in the MS and HS soils at the class level; Methylophaga and Pseudomonas were the predominant bacteria in the HS soil; and Azotobacter and Methylobacillus were abundant in the MS soil. Most genera belonging to Proteobacteria and Actinobacteria were detected via a linear discriminate analysis (LDA) effect size (LEfSe) analysis, which indicated that microbes with the ability to degrade organic matter and accomplish nutrient cycling can be well-adapted to salt conditions. Further analyses (redundancy analysis and Mantel test) showed that the microbial communities were mainly related to the soil salinity, electrical conductivity (EC1:5), total phosphorus (TP) and ammonia nitrogen (NH4+-N). Overall, the findings of the study can provide insights for better understanding the dominant indigenous microbes and their roles in biochemical cycles in different saline soils in the Qaidam Basin, Qinghai Province, China. The researches related to microbial community under typical poplar species should further clarify the mechanism of plant-microbial interaction and benefit for microbial utilization in salt soil remediation.

An Electrical Conductivity Sensor for the Selective Determination of Soil Salinity.

The measurement of electrical conductivity (EC) has long been a tool for understanding soil properties. Previous studies concluded that EC measurement is not an ion-selective method, but these papers did not address the measurement frequency. An experimental tool and method were developed for semi-factory conditions in a large-scale soil trough at the Institute of Technology of the Hungarian University of Agricultural and Life Sciences. A specially designed and built test apparatus mounted on the tractor's three-point hitch was used as a measuring device. The wear-resistant steel elements of the measuring device were also the sensors for measuring EC. This paper describes the conditions of the measurement series, the measurement results, and our conclusions from the experiments with the soil sensor. Different characteristics were measured in soil moistened with K and Ca solutions at different concentrations. The EC values show an increasing tendency with increasing salt concentration, and we also found that the rate of change of EC is different for different solution ratios. Based on our measurements, we found that the best method to isolate concentration differences is to use the test frequency range 20 Hz-250 kHz.

Precipitation changes alter plant dominant species and functional groups by changing soil salinity in a coastal salt marsh.

Specific mechanisms of precipitation change due to global climate variability on plant communities in coastal salt marsh ecosystems remain unknown. Hence, a field manipulative precipitation experiment was established in 2014 and 5 years of field surveys of vegetation from 2017 to 2021 to explore the effects of precipitation changes on plant community composition. The results showed that changes in plant community composition were driven by dominant species, and that the dominance of key species changed significantly with precipitation gradient and time, and that these changes ultimately altered plant community traits (i.e., community density, height, and species richness). Community height increased but community density decreased with more precipitation averaged five years. Furthermore, changes in precipitation altered dominant species composition and functional groups mainly by influencing soil salinity. Salinity stress caused by decreased precipitation shifted species composition from a dominance of taller perennials and grasses to dwarf annuals and forbs, while the species richness decreased. Conversely, soil desalination caused by increased precipitation increased species richness, especially increasing in the dominance of grasses and perennials. Specifically, Apocynaceae became dominance from rare while Amaranthaceae decreased in response to increased precipitation, but Poaceae was always in a position of dominance. Meanwhile, the dominance of grasses and perennials has the cumulative effect of years and their proportion increased under the increased 60% of ambient precipitation throughout the years. However, the annual forb Suaeda glauca was gradually losing its dominance or even becoming extinct over years. Our study highlights that the differences in plant salinity tolerance are key to the effects of precipitation changes on plant communities in coastal salt marsh. These findings aim to provide a theoretical basis for predicting vegetation dynamics and developing ecological management strategies to adapt to future precipitation changes.

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 2 weeks 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.

Spatial assessment and chemical characterization of degraded (salt-affected) soils at post-reclamation stage of the Indo-Gangetic Plain in Haryana State.

Dynamics of soil salinity and sodicity is a common feature driven by anthropogenic causes such as soil reclamation, the effect of extreme climate events, disturbed salt, and water balance in irrigated areas that are devoid of any good quality groundwater source and adequate natural surface drainage condition in a semiarid climatic region. Periodic soil salinity assessment is therefore vital to know the current soil salinity status, plan reclamation, and/or management strategies for sustained agricultural growth and livelihood security. Temporal studies using Indian Remote Sensing (IRS) LISS III data, at pre- (1997) and post-reclamation (2017) stages have indicated spatial changes as reclaimed areas (~ 35%) and dynamics of soil salinity as increased areas (~ 61%) under irrigation across the Gangetic plain of Haryana State. The prominent areas of reclaimed sodic soil soils were located in the old alluvial plain which covered Panipat (12.32%), Karnal (6.01%), and Jind (5.9%) districts. Based on pH, ECe, and ESP values, these were classified as slight (Sso1, 8.75%), moderate (Sso2, 24.73%), and strong (Sso3, 18.20%) sodic soils, respectively. Significant salinity-inflictions (emerging areas) were identified at low-lying, poorly drained, irrigated soils in south and central Haryana that cover Jhajjar (13.99%), Sirsa (11.06%), Hisar (10.15%), Rohtak (8.73%), Bhiwani (6.43%), Palwal (4.31%), and Rewari (3.01%) districts. Slight (Ssa1, 16.82%), and moderate (Ssa2, 22.13%), categories are dominant soils, respectively. Among the landforms, significant areas (28.24%) were identified in the old alluvial plain with sand dunes (OAPSD), aeo-fluvial plain (AFP, 8.6%), and fluvio-aeolian plain (FAP, 6.0%), respectively. Dominant areas of reclaimed soils (14.4%) were identified in OAPSD. The soil analysis data indicated that these soil are moderate to strongly sodic (pH 8.7-11.0) and saline (ECe 4-26 dS m-1). The reclaimed sodic soils showed prominent improvement in soil pH and sodicity levels (pH 8.3-9.2) at 0-15 cm depth and are commonly located in the Ghaggar and Yamuna river plains. Poor quality groundwater with high Residual Sodium Carbonate (RSC) was dominant at selected locations under the arid and semiarid climate. The database can also be used as a reference database for further monitoring of soil salinity status particularly in the irrigated regions. Currently, it is also used as a primary database for harmonization, monitoring, and reconciling of similar soils of the world under the Global Soil Partnership projects.

Modeling impacts of saltwater intrusion on methane and nitrous oxide emissions in tidal forested wetlands.

Emissions of methane (CH4 ) and nitrous oxide (N2 O) from soils to the atmosphere can offset the benefits of carbon sequestration for climate change mitigation. While past study has suggested that both CH4 and N2 O emissions from tidal freshwater forested wetlands (TFFW) are generally low, the impacts of coastal droughts and drought-induced saltwater intrusion on CH4 and N2 O emissions remain unclear. In this study, a process-driven biogeochemistry model, Tidal Freshwater Wetland DeNitrification-DeComposition (TFW-DNDC), was applied to examine the responses of CH4 and N2 O emissions to episodic drought-induced saltwater intrusion in TFFW along the Waccamaw River and Savannah River, USA. These sites encompass landscape gradients of both surface and porewater salinity as influenced by Atlantic Ocean tides superimposed on periodic droughts. Surprisingly, CH4 and N2 O emission responsiveness to coastal droughts and drought-induced saltwater intrusion varied greatly between river systems and among local geomorphologic settings. This reflected the complexity of wetland CH4 and N2 O emissions and suggests that simple linkages to salinity may not always be relevant, as non-linear relationships dominated our simulations. Along the Savannah River, N2 O emissions in the moderate-oligohaline tidal forest site tended to increase dramatically under the drought condition, while CH4 emission decreased. For the Waccamaw River, emissions of both CH4 and N2 O in the moderate-oligohaline tidal forest site tended to decrease under the drought condition, but the capacity of the moderate-oligohaline tidal forest to serve as a carbon sink was substantially reduced due to significant declines in net primary productivity and soil organic carbon sequestration rates as salinity killed the dominant freshwater vegetation. These changes in fluxes of CH4 and N2 O reflect crucial synergistic effects of soil salinity and water level on C and N dynamics in TFFW due to drought-induced seawater intrusion.

Differential gene expression analysis under salinity stress in the selected turmeric (Curcuma longa L.) cultivars for curcuminoid biosynthesis.

BACKGROUND: Curcuminoids are the phenolic compounds found exclusively in turmeric. Their presence is known to increase immunity and resistance against certain cancers and neurological disorders in humans also, protecting the plant itself against salinity stress. METHODS: In this experiment, we studied the expression levels of MAPK1 and DCS genes, their curcuminoid biosynthesis under salinity stress conditions so that the impact of individual genes can be understood using semi- quantitative PCR. RESULTS: The expressions of the genes with respect to curcuminoid biosynthesis showed fluctuations in their band intensity values due to the production of curcuminoids, which is initiated first in the leaves followed by the rhizomes. Not all the genes responsible for the curcuminoid biosynthesis show positive regulation under salt stress conditions which is observed in response to the severity of the stress imposed on the cultivars. CONCLUSIONS: In our findings, both the genes MAPK1 and DCS were down-regulated for curcuminoid biosynthesis compared to their controls in both the cultivars Vallabh Sharad and Selection 1.

Patterns of salt transport and factors affecting typical shrub in desert-oases transition areas.

Soil salinization and water deficits are considered the primary factors limiting economic development and environmental improvement in arid areas. However, there remains limited knowledge of the adaptability of typical shrubs to salinization of desert areas in arid zones. This study was conducted in a desert oasis transition zone (Tarim River, China), aiming to investigate: i) the spatial-temporal changes in soil salinity; ii) the interactions between the pedoenvironment vs typical shrub (Calligonum mongolicum). The van Genuchten soil salinity retention ensemble model (TVGSSREM-3D) was developed to simulate variations in soil water-salt transport in the desert-oasis zone and to accurately explain the main factors influencing Calligonum mongolicum desert-oases transition areas. The results showed that monthly average salinity ranged from 2.0 to 8.0 g kg-1, with a peak in August (9.17 g kg-1). The presence of human activities (Salt Drainage Canal) and the distribution of Calligonum mongolicum resulted in a clear spatial salinity zonation. Moreover, analysis of environmental indicators using the TVGSSREM-3D model revealed strong correlations between the distribution of salinity in Calligonum mongolicum desert-oases transition areas and groundwater depth (GD), minimum relative humidity (MRH), and water vapor pressure (WVP). These findings provide a scientific basis for stabilizing, restoring, and reconstructing the ecosystem of the oasis-desert transition zone.

Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features.

Gaofen-2 (GF-2) imagery data has been playing an important role in environmental monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use in soil salinity estimation. In this paper, we combined spectral and textual features for soil salinity estimation from GF-2 imagery. The spectral features comprised five classes of predictors: spectral value, vegetation index, salinity index, brightness index, and intensity index. Four gray-level co-occurrence matrix (GLCM) indices were used as the textural features. The least absolute shrinkage and selection operator (LASSO) was applied to select features. Four methods, namely, Random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and partial least squares regression (PLSR) were applied and compared. To this end, 211 soil samples were collected in the Yellow River Delta through field investigation. The results showed that GF-2 imagery could successfully estimate soil salinity by integrating spectral and texture features, and among the four methods, the RF had the highest accuracy with the determination coefficient for cross-validation (R2CV), a root mean square error for cross-validation (RMSECV), and the ratio of the standard deviation to the root mean square error of prediction (RPD) of 0.82, 2.36 g kg-1, and 2.28, respectively. Especially, the impact of different scale features on the soil salinity estimation accuracy was evaluated. The optimal window size for features was 9 × 9 pixels, and increasing or decreasing the window size will decrease the estimation accuracy. The study provides a novel application to soil salinity estimation from remote sensing imagery.

Predicting long-term dynamics of soil salinity and sodicity on a global scale.

Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of land degradation and for planning effective remediation strategies in face of future climatic uncertainties. However, conventional methods used for tracking the variability of soil salinity/sodicity are extensively localized, making predictions on a global scale difficult. Here, we employ machine-learning techniques and a comprehensive set of climatic, topographic, soil, and remote sensing data to develop models capable of making predictions of soil salinity (expressed as electrical conductivity of saturated soil extract) and sodicity (measured as soil exchangeable sodium percentage) at different longitudes, latitudes, soil depths, and time periods. Using these predictive models, we provide a global-scale quantitative and gridded dataset characterizing different spatiotemporal facets of soil salinity and sodicity variability over the past four decades at a ∼1-km resolution. Analysis of this dataset reveals that a soil area of 11.73 Mkm2 located in nonfrigid zones has been salt-affected with a frequency of reoccurrence in at least three-fourths of the years between 1980 and 2018, with 0.16 Mkm2 of this area being croplands. Although the net changes in soil salinity/sodicity and the total area of salt-affected soils have been geographically highly variable, the continents with the highest salt-affected areas are Asia (particularly China, Kazakhstan, and Iran), Africa, and Australia. The proposed method can also be applied for quantifying the spatiotemporal variability of other dynamic soil properties, such as soil nutrients, organic carbon content, and pH.

Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models.

Several recent studies have evidenced the relevance of machine-learning for soil salinity mapping using Sentinel-2 reflectance as input data and field soil salinity measurement (i.e., Electrical Conductivity-EC) as the target. As soil EC monitoring is costly and time consuming, most learning databases used for training/validation rely on a limited number of soil samples, which can affect the model consistency. Based on the low soil salinity variation at the Sentinel-2 pixel resolution, this study proposes to increase the learning database's number of observations by assigning the EC value obtained on the sampled pixel to the eight neighboring pixels. The method allowed extending the original learning database made up of 97 field EC measurements (OD) to an enhanced learning database made up of 691 observations (ED). Two classification machine-learning models (i.e., Random Forest-RF and Support Vector Machine-SVM) were trained with both OD and ED to assess the efficiency of the proposed method by comparing the models' outcomes with EC observations not used in the models´ training. The use of ED led to a significant increase in both models' consistency with the overall accuracy of the RF (SVM) model increasing from 0.25 (0.26) when using the OD to 0.77 (0.55) when using ED. This corresponds to an improvement of approximately 208% and 111%, respectively. Besides the improved accuracy reached with the ED database, the results showed that the RF model provided better soil salinity estimations than the SVM model and that feature selection (i.e., Variance Inflation Factor-VIF and/or Genetic Algorithm-GA) increase both models´ reliability, with GA being the most efficient. This study highlights the potential of machine-learning and Sentinel-2 image combination for soil salinity monitoring in a data-scarce context, and shows the importance of both model and features selection for an optimum machine-learning set-up.

Identification of Soil Types and Salinity Using MODIS Terra Data and Machine Learning Techniques in Multiple Regions of Pakistan.

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan's economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70-30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models' performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.