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Soil salinization seriously affects the sustainable development of agricultural production; thus, the timely, efficient, and accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0–20 cm and 20–40 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0–20 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0–20 cm is higher than that of 20–40 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.

Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services 1 . As a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred 1 – 6 . However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats—one of the most extensive coastal ecosystems—remain unknown 7 . Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984–2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation 7 , occupy at least 127,921 km 2 (124,286–131,821 km 2 , 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series—which included East Asia, the Middle East and North America—we estimate that 16.02% (15.62–16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development 1 , reduced sediment delivery from major rivers 8 , 9 , sinking of riverine deltas 8 , 10 , increased coastal erosion and sea-level rise 11 signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems. Analyses of over 700,000 satellite images to map the global extent of tidal flats over the past thirty years, and enable assessments of the status and likely future trajectories of these coastal ecosystems.

The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

Over the last decades, economic developments in the Vietnamese Mekong Delta have led to a sharp increase in groundwater pumping for domestic, agricultural and industrial use. This has resulted in alarming rates of land subsidence and groundwater salinization. Effective groundwater management, including strategies to work towards sustainable groundwater use, requires knowledge about the current groundwater salinity distribution, in particular the available volumes of fresh groundwater. At the moment, no comprehensive dataset of the spatial distribution of fresh groundwater is available. To create a 3D model of total dissolved solids (TDS), an existing geological model of the spatial distribution and thickness of the aquifers and aquitards is updated. Next, maps of drainable porosity for each aquifer are interpolated based on the sedimentological description of the borehole data. Measured TDS in groundwater, inferred TDS from resistivity measurements in boreholes and soft incomplete data (derived from measurements in boreholes and data from domestic wells) are combined in an indicator kriging routine to obtain the full probability distribution of TDS for each (x,y,z) location. This statistical distribution of TDS combined with drainable porosity yields estimates of the volume of fresh groundwater (TDS < 1 g L−1) in each aquifer. Uncertainty estimates of these volumes follow from a Monte Carlo analysis (sequential indicator simulation). Results yield an estimated fresh groundwater volume for the Mekong Delta of 867 billion cubic metres with an uncertainty range of 830–900 billion cubic metres, which is somewhat higher than previous assessments of fresh groundwater volumes. The resulting dataset can for instance be used in groundwater flow and salt transport modelling as well as aquifer storage and recovery projects to support informed groundwater management decisions, e.g. to prevent further salinization of the Mekong Delta groundwater system and land subsidence, and is available at https://doi.org/10.5281/zenodo.4441776 (Gunnink et al., 2021).

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO3), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties.

The study area is situated in the Qelabshowah–Belqas region, known for its Quaternary deposits. This research aims to demonstrate the two-dimensional (2D) variation of subsurface layers and salinity distribution using geoelectrical data, hydrochemical analysis, and geostatistical analysis. DC resistivity measurements were taken at fifteen vertical electrical sounding (VES) survey points using a Schlumberger array (AB/2 = 100 m) along three profiles. In addition, an electrical resistivity tomography (ERT) survey was conducted with a dipole–dipole array across one profile. Seven surface water samples were collected in the area. From the 1D and 2D inversion of VES and ERT data sets, three-to-four geoelectric layers were identified, including unconsolidated surface deposits, saturated clayey sand, saturated sand, and a salt-rich layer. The 2D inversion of VES data revealed an ancient salt-rich layer deposited in swampy conditions over a conductive wet sand layer along profile one due to salt mineral infiltration and dissolution. The 2D inversion of ERT data showed accurate lateral geometric accuracy compared to the 2D inversion of VES data, highlighting geological features, such as caves in the second layer and a buried water canal on the ground surface. Surface water samples showed high salinity levels with sodium hazards, indicating an Na–Cl composition. Geoelectric and hydrochemical data sets were geostatistically analyzed using spherical variogram supported ordinary Kriging interpolation. The analysis indicated weak to moderate spatial dependency for true resistivity parameters, while sodium content (SC) and permeability index (PI) showed strong spatial correlation. The 2D spatial distribution resistivity maps based on the 1D inversion of VES data displayed a general decrease in resistivity with depth, likely due to clay minerals or moist soil in the second layer and saline irrigation water infiltration in the third layer. The 2D spatial distribution of SC and PI showed a high concentration zone, posing a potential risk to agricultural crops regardless of soil permeability. It is recommended to use these maps when cultivating plants that can tolerate high sodium levels during the reclamation process.

Soil salinization is a crucial factor that impacts plant distribution and growth. Apocynum venetum, an ornamental plant with medicinal value, has shown remarkable salt tolerance. However, the specific mechanisms through which A. venetum responds to salt stress are not yet fully understood. To address this gap, we conducted a study where 10-week-old A. venetum seedlings were subjected to salt stress by irrigating them with a nutrient solution containing varying concentrations of NaCl (0, 100, 200, and 350 mmol·L−1). After the salt stress treatment, various growth indicators (such as plant height, root length, root fresh weight, root dry weight, leaf fresh weight, leaf dry weight, root water content, leaf water content, and root–leaf ratio) as well as physiological indicators (including SOD and CAT activities in both leaves and roots, soluble protein contents in leaves and roots, and chlorophyll and carotene contents in leaves) were determined. In addition, the gene expression profile of roots under salt stress was examined by transcriptome sequencing to explore the mechanism of salt response in A. venetum. Our results show that salt stress led to yellowing and wilting of A. venetum seedling leaves. Furthermore, the chlorophyll and carotenoid contents in the leaves of the 350 mmol·L−1 NaCl-treated group were significantly reduced. Although the leaf and root biomass gradually decreased with an increase in the salt concentration, the root–leaf ratio exhibited a decreasing trend. NaCl stress also caused significant changes in physiological indices in the A. venntum leaves and roots. The activities of superoxide dismutase (SOD) and catalase (CAT) increased in both leaves and roots of the 100 mmol·L−1 NaCl-treated group. The soluble protein content in both leaves and roots increased under the 200 mmol·L−1 NaCl stress. To screen changes in root gene expression, transcriptome sequencing and qRT-PCR were performed. GO and KEGG enrichment analyses revealed that salt stress primarily affects carbohydrate metabolism, MAPK signaling transduction, phytohormone signaling pathways, glyoxylate and dicarboxylate metabolism, and other pathways. This study provides a novel understanding of the growth and physiological response of A. venetum leaf and root to NaCl stress, as well as the changes in the transcription levels in A. venetum root. The results serve as a reference for future research on salt-tolerant mechanisms and molecular breeding of A. venetum.

Substrate properties are pivotal in shaping porewater chemistry and groundwater quality, serving as the primary driving factors. While spatial analysis of geochemical distribution has been extensively explored in hydrochemistry, the application of geostatistical techniques in tropical island soil investigation remains lacking. In this study, we aim to characterise the geochemical and micro-spatial variability of soil along the flow path of groundwater via integration of chemometric and geostatistical techniques. The study was conducted across a ~ 350 m 2 area of unconsolidated soil profile (2.75 m depth × 125 m length) on a sedimentary island’s aquifer. The results revealed that multivariate statistical analysis coupling iso-factor scores maps can provide key insights to constraint the subsurface geochemical behaviour. Marine and lithogeneous (natural-derived) materials were found to significantly influence the metals distribution across the soil profile. Clay and sesquioxides (Al 2 O 3 and Fe 2 O 3 ) from the high-relief area appear to influence the micro-variability of the inland soil, while seawater mixing impacts the subsoil geochemistry. This study concludes that natural sources, pedogenic processes, and seawater intrusion are the major drivers of metals variability in the tropical island aquifer. These findings provided a scientific overview of metals distribution in a seawater–freshwater mixing zone and significant references for the vertical and horizontal distribution of metals in a sandy soil profile. In particular, the chemometric and geostatistical analysis can be potentially used as robust tools for investigating biogeochemical cycling and the source and sink of groundwater pollutants. It is envisaged that future studies could employ a similar approach to explore other islands worldwide.

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