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Soil salinization is a crucial type in the degradation of coastal land, but its spatial distribution and drivers have not been sufficiently explored especially at the depth scale owing to its multidimensional nature. In this study, we proposed a multi-depth soil salinity prediction model (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Delta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing parameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial location. Then, three machine learning methods including back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show the following: (1) Environmental variables in this study are all effective variables for soil salinity prediction. The most sensitive indicators to multi-depth soil salinity are GDVI, ENDVI, SI-T, NDWI, and LST. (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at four soil depths. (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) The soil salinity throughout the four depths examined was found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type. This study proposed a remote sensing prediction method for salinization in multiple soil layers in the coastal plain, which could provide decision support for spatial monitoring of land salinization and achieving land degradation neutrality targets.

Background and Aims The intercropping has great advantages in production gain, resources use and soil quality improvement. However, the effects of perennial forage legumes intercropped with annual grain crop on soil alkali-salinity was not well revealed. This study aimed to propose a spring wheat ( Triticum aestivum L.)/alfalfa ( Medicago sativa L.) intercropping and reveal whether this cropping system could ameliorate soil salinity. Methods A two-year field experiment was conducted under 3 cropping systems at 3 irrigation amounts in the inland arid area of northwestern China. The cropping systems were sole spring wheat cropping (W), 12 rows spring wheat/4 rows alfalfa intercropping (W12A4) and 8 rows spring wheat/4 rows alfalfa intercropping (W8A4), and irrigation amounts included 450 (full irrigation amount, I f ), 360 (moderate irrigation amount, I m ) and 180 mm (low irrigation amount, I l ). Results The intercropping system, especially W8A4, could more decrease soil salinity, pH and topsoil bulk density, and increase soil water content, topsoil porosity, and Ca 2+ and Mg 2+ contents, compared to W. Soil electrical conductivity was positively correlated with topsoil bulk density and negatively correlated with topsoil porosity, which were enhanced by intercropping and irrigation. There was weak correlation between soil electrical conductivity and soil water content. W12A4 and W8A4 led to reduction in contents of Na + , K + , Cl − , and SO 4 2− , while Ca 2+ and Mg 2+ accumulated in the soil. Conclusion Intercropped alfalfa and spring wheat could reduce soil salinity, improve soil structure and ions balance. It was recommended that W8A4 with 360 mm irrigation could be extensively used in the arid area of northwestern China, gaining grain and forage production, and helping reduce salt accumulation.

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).

PurposeMonitoring and evaluating spatiotemporal dynamics of soil salinity over large areas for an extended period is important for keeping crop yield in salt-affected areas, but difficult due to its high variability. In this study, measurements of soil salinity with 68 sampling sites at different depth from top soil to 1.8 m were carried out in 2017–2018 to understand soil salinity variability and temporal stability at an agricultural area (> 80 km2).MethodsThe spatial variability and mean of soil salinity was estimated by the geostatistical analysis and temporal stability analysis, respectively. Then, improved temporal stability analysis was proposed by dividing samples into 7 groups, and mean soil salinity in each group was estimated by temporal stability analysis. Lastly, monitoring network was recommended to evaluate long-term soil salinity.Results and discussionStrong spatial dependency of soil salinity was found with most degree of spatial dependence smaller than 25%. The temporal stability analysis was difficult to choose the representative sites due to large range of mean relative difference and standard deviation of relative difference of soil salinity. The predictions of improved temporal stability analysis were significantly improved with mean relative error of soil salinity means ranging from − 2.72 to 1.61%, and determination coefficient more than 0.90. Spatial distribution of soil salinity determined by 32 long-term soil salinity monitoring locations was consistent with that of all 68 sampling locations.ConclusionThe improved temporal stability analysis combined with geostatistical analysis can obtain spatial pattern and spatial mean of regional soil salinity, and improve monitoring efficiency greatly.

Soil salinity is a major issue causing land degradation in coastal areas. In this study, we assessed the land use and soil salinity changes in Djilor district (Senegal) using remote sensing and field data. We performed land use land cover changes for the years 1984, 1994, 2007, and 2017. Electrical conductivity was measured from 300 soil samples collected at the study area; this, together with elevation, distance to river, Normalized Difference Vegetation Index (NDVI), Salinity Index (SI), and Soil-Adjusted Vegetation Index (SAVI), was used to build the salinity model using a multiple regression analysis. Supervised classification and intensity analysis were applied to determine the annual change area and the variation of gains and losses. The results showed that croplands recorded the highest gain (17%) throughout the period 1984–2017, while forest recorded 3%. The fastest annual area of change occurred during the period 1984–1994. The salinity model showed a high potential for mapping saline areas ( R 2  = 0.73 and RMSE = 0.68). Regarding salinity change, the slightly saline areas (2 < EC < 4 dS/m) increased by 42% whereas highly saline (EC > 8 dS/m) and moderately saline (4 < EC < 8 dS/m) areas decreased by 23% and 26%, respectively, in 2017. Additionally, the increasing salt content is less dominant in vegetated areas compared with non-vegetated areas. Nonetheless, the highly concentrated salty areas can be restored using salt-resistant plants (e.g., Eucalyptus sp . , Tamarix sp.). This study gives more insights on land use planning and salinity management for improving farmers’ resilience in coastal regions.

Global agricultural lands are becoming saline because of human activities that have affected crop production and food security worldwide. In this study, the spatiotemporal variability of soil electrical conductivity (EC) in Central Asia was evaluated based on high-resolution multi-year predicted soil EC data, Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product, precipitation, reference evapotranspiration, population count, and soil moisture datasets. We primarily detected pixel-based soil EC trends over the past three decades and correlated soil EC with potential deriving factors. The results showed an overall increase in salt-affected areas between 1990 and 2018 for different land cover types. The soil EC trend increased by 6.86% (p < 0.05) over Central Asia during 1990–2018. The open shrub lands dominated by woody perennials experienced the highest increasing soil salinity trend, particularly in Uzbekistan and Turkmenistan local areas, while there was a decreasing soil EC trend in the cropland areas, such as in Bukhara and Khorezm (Uzbekistan). The main factors that affect the variability of soil salinity were strongly associated with population pressure and evapotranspiration. This study provides comprehensive soil EC variations and trends from the local to regional scales. Agriculture and land resource managers must tackle the rising land degradation concerns caused by the changing climate in arid lands and utilise geoinformatics.

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 ( R 2 ) of 0.75 when using Sentinel-MSI and 0.72 with Landsat-OLI. The R 2 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 R 2 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.

Salinity is one of the main environmental stresses, and it affects potato growth and productivity in arid and semiarid regions by disturbing physiological process, such as the photosynthesis rate, the absorption of essential nutrients and water, plant hormonal functions, and vital metabolic pathways. Few studies are available on the application of combined nanomaterials to mitigate salinity stress on potato plants (Solanum tuberosum L. cv. Diamont). In order to assess the effects of the sole or combined application of silicon (Si) and potassium (K) nanoparticles and biochar (Bc) on the agro-physiological properties and biochemical constituents of potato plants grown in saline soil, two open-field experiments were executed on a randomized complete block design (RCBD), with five replicates. The results show that the biochar application and nanoelements (n-K and n-Si) significantly improved the plant heights, the fresh and dry plant biomasses, the numbers of stems/plant, the leaf relative water content, the leaf chlorophyll content, the photosynthetic rate (Pn), the leaf stomatal conductance (Gc), and the tuber yields, compared to the untreated potato plants (CT). Moreover, the nanoelements and biochar improved the content of the endogenous elements of the plant tissues (N, P, K, Mg, Fe, Mn, and B), the leaf proline, and the leaf gibberellic acid (GA3), in addition to reducing the leaf abscisic acid content (ABA), the activity of catalase (CAT), and the peroxidase (POD) and polyphenol oxidase (PPO) in the leaves of salt-stressed potato plants. The combined treatment achieved maximum plant growth parameters, physiological parameters, and nutrient concentrations, and minimum transpiration rates (Tr), leaf abscisic acid content (ABA), and activities of the leaf antioxidant enzymes (CAT, POD, and PPO). Furthermore, the combined treatment also showed the highest tuber yield and tuber quality, including the contents of carbohydrates, proteins, and the endogenous nutrients of the tuber tissues (N, P, and K), and the lowest starch content. Moreover, Pearson’s correlation showed that the plant growth and the tuber yields of potato plants significantly and positively correlated with the photosynthesis rate, the internal CO2 concentration, the relative water content, the proline, the chlorophyll content, and the GA3, and that they were negatively correlated with the leaf Na content, PPO, CAT, ABA, MDA, and Tr. It might be concluded that nanoelement (n-K and n-Si) and biochar applications are a promising method to enhance the plant growth and crop productivity of potato plants grown under salinity conditions.

Salinity is one of the environmental factors that affects both productivity and genetic diversity in plant species. Within the soil profile, salinity is a dynamic indicator and significantly changes with depth. The present study examined the effects of the vertical heterogeneity of soil salinity chemistry on the plant height, fresh and dry biomass accumulation, water content, level of genetic polymorphism, and observed and expected heterozygosity in seven populations of halophyte Bassia prostrata in natural habitats. Soil salinity ranged from slight (Ssalts = 0.11–0.25%) to extreme (Ssalts = 1.35–2.57%). The main contributors to salinity were Na+, Ca2+, and Mg2+. Multivariate analysis revealed that biomass accumulation is positively affected by moderate/high salinity in 20–60 cm soil layers, which may be associated with the salt required for the optimal growth of the halophyte B. prostrata. The formation of seed genetic diversity is negatively affected by slight/moderate salinity in the 0–40 cm layers. An increase in divalent ion content can reduce genetic diversity and increase the local adaptation of B. prostrata to magnesium–calcium sulfate salinity. The effect of the in-depth distribution of soil salinity on productivity and genetic diversity may be related to seasonal variables during biomass accumulation (summer) and seed formation (autumn).

Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development.