Characterising dryland salinity in three dimensions

Moisture movement, soil salt migration, and nitrogen transformation under different irrigation conditions: Field experimental research

Irrigation and fertilizer application can lead to significant changes in groundwater quality. In this study, a field irrigation experiment was carried out from April 9 to 23, 2021 under irrigation and fertigation conditions to understand the mechanisms of moisture movement, soil salt migration, and nitrogen transformation in the soil profile. Continuous in-situ monitoring and sampling of soil and irrigation water, as well as stable isotopes, chemical parameters, and soluble salt analyses, were performed in this research. The results showed that the time cost by the irrigation water in the vadose zone was about 5 h. The infiltrated irrigation water was accompanied by high concentrations of soluble salts, leached from the soil layers of 20-80 cm and 100-150 cm, which is associated with the leaching of Na⁺, Cl^-, SO₄^2-, and Ca²⁺ and the dissolution of minerals such as gypsum and halite. Furthermore, the variations in nitrogen concentrations (NH₄⁺ and NO₃^-) in the soil profile suggested that fertilizer application was the main source of NO₃^- in the soil and groundwater, while irrigation was the biggest driving force for nitrogen transport and transformation in soil. The application of urea fertilizer can increase the content of ammonium nitrogen at the soil layer of 0-80 cm. This nitrogen form can be subsequently transformed to nitrate nitrogen during the water transport to the groundwater. The current study provides a strong scientific basis for the protection and management of groundwater and soil quality in agricultural areas.

An interplay of soil salinization and groundwater degradation threatening coexistence of oasis-desert ecosystems

In salt-affected and groundwater-fed oasis-desert systems, water and salt balance is critically important for stable coexistence of oasis-desert ecosystems, especially in the context of anthropogenic-induced over-development and perturbations due to climate variability that affects the sustainability of human-natural systems. Here, an investigation of the spatio-temporal variability of soil salinity and groundwater dynamics across four different hydrological regions in oasis-desert system is performed. An evaluation of the effects of soil salinization and groundwater degradation interplays on the coexistence of oasis-desert ecosystems in northwestern China is undertaken over 1995-2020, utilizing comprehensive measurements and ecohydrological modelling framework. We note that the process of salt migration and accumulation across different landscapes in oasis-desert system is reshaping, with soil salinization accelerating especially in water-saving agricultural irrigated lands. The continuous decline in groundwater tables, dramatic shifts in groundwater flow patterns and significant degradation of groundwater quality are occurring throughout the watershed. Worse so, a clear temporal-spatial relationship between soil salinization and groundwater degradation appearing to exacerbate the regional water-salt imbalance. Also, the eco-environmental flows are reaching to their limit with watershed closures, although these progressions were largely hidden by regional precipitation and streamflow variability. The oasis-desert ecosystems tend to display bistable dynamics with two preferential configurations of bare and vegetated soils, and soil salinization and groundwater degradation interplays are causing catastrophic shift in the oasis-desert ecosystems. The results highlight the importance of regional adaptive water and salt management to maintain the coexistence of oasis-desert ecosystems in arid areas.

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR + Landsat data); the BP model accuracy (R2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR + Landsat data), and then combined with the model inversion plots, we found that PALSAR + Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.

Spatio-temporal dynamics and eco-hydrological controls of water and salt migration within and among different land uses in an oasis-desert system

Identifying the eco-hydrological processes associated with water-salt dynamics is important for the sustainable management of water resources and eco-environmental systems in groundwater-dependent ecosystems, especially across different land use types in salt-affected oasis-desert ecosystems. In this study, a typical cropland-shelterbelt-desert site at the oasis-desert system in the Sangong River watershed of northwestern China was selected to investigate the spatio-temporal variations of water-salt dynamics using the Spearman rank correlation analysis and water/mass balance analysis, and to identify the response of vegetation dynamics to water-salt variations based on a model framework for vegetation-salinity-groundwater interactions, within and among these land uses during crop growth period (CGP: April 1-June 28, 2018) and non-crop-growth period (Non-CGP: June 29-October 31, 2018). Results showed that the soil water content (SWC) and soil electrical conductivity (SEC) had clear vertical stratification, horizontal transition and seasonal fluctuation characteristics during both CGP and Non-CGP. Significant differences in groundwater depth and salinity were exhibited between both study periods. The water exchange flux (WEF) and salt exchange flux (SEF) in both the cropland and shelterbelt were closely related to irrigation events and evidently higher than that in desert. The cropland maintained a salt accumulation state (especially at the >60-80 cm soil layer) during CGP. Hydrological links and salt transport processes among adjacent land uses have been weakened owing to the application of water-saving irrigation in cropland and the significantly declined of regional groundwater tables. Groundwater pumping and lateral groundwater flow (LGWF) were the most important media for water-salt exchange in the site. The interactions of vegetation with both the soil water-salt balance and groundwater dynamics may cause a discontinuous and irreversible ecosystem response to changes in land use or environmental conditions. Anthropogenic processes, especially the development of modern water-saving irrigation agriculture with groundwater-fed, are dominating the vegetation-salinity-groundwater interactions and its ecohydrological consequences in this ecosystem. Adaptive management of water and salt migration in soil and groundwater is essential for maintaining the coexistence of oasis-desert ecosystems in arid areas.

Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China

Tarim River Basin is experiencing heavy soil degeneration in a long term because of the extreme natural conditions, added with improper human activities such as reclamation and rejected field repeatedly, which hindered the soil health. One of the mainly form is soil salinization. Spatial distribution and variation of soil salinity is essential both for agricultural resource management and local economic development. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since 1980s while land use and climate have undergone major changed. Electromagnetic induction (EMI) has been successfully used to directly measurement the spatial distribution of targeting soil property at field- scale, and apparent electrical conductivity (ECa, mS m^-1) has become a surrogate of soil salinity (EC, dS m^-1) studied by many researchers at local scale. However, the effectiveness of this equipment has not been verified in the typical soil salinization areas in southern Xinjiang, especially on a large scale. This study was aimed to test the performance of ECa jointed with Random Forest (RF) for soil salinity regional-scale mapping at a typical arid area, taking Tarim River Basin as an example. The result showed that ECa together with environmental derivative variables and with RF were suited for regional-scale soil salinity mapping. Predicted accuracy of EC was higher at surface (0-20 cm, R² = 0.65, RMSE = 5.59) and deeper soil depth (60-80 cm, R² = 0.63, RMSE = 2.00, and 80-100 cm, R² = 0.61, RMSE = 1.73), lower at transitional zone (20-40 cm, R² = 0.55, RMSE = 2.66, and 40-60 cm, R² = 0.51, RMSE = 2.49). When ECa is involved in modeling, the prediction accuracy of multiple depths of EC is improved by 13.33%-61.54%, of which the most obvious depths are 60-80 cm and 0-20 cm. The results of variable importance show that SoilGrids were also favored the power EC model. Hence, we strongly recommended to joint EMI reads with remote sensing imagery for soil salinity monitoring at large scale in southern Xinjiang. These EC and ECa map can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor water and salt dynamics, and a guide for the design of future soil surveys.

Soil salinization management for sustainable development: A review

The expansion of irrigated agriculture is of paramount importance to feed the burgeoning global population. However, without proper management, this expansion can result in environmental problems of irrigation-induced soil salinization. A recent FAO estimate reported that a large portion of total global soil resources are degraded and this problem is persistently expanding. Many irrigated areas of the world are facing the twin problems of soil salinization and waterlogging and presently over 20% of the total global irrigated area is negatively affected by these problems. And, if left unattended, this problem could expand to over 50% of the total global irrigated areas by 2050. The proper management of the aforementioned soil salinization is imperative for achieving most of the Sustainable Development Goals (SDGs) of the United Nations. For example, soil salinization management is vital for achieving the 'Zero Hunger' (SDG2) and 'Life on Land' (SDG15) among other SDGs. This paper provides a comprehensive review of different measures used for managing the environmental problems of soil salinization. All the possible sources of related and up to date literature have been accessed and over 250 publications were collected and thoroughly analyzed for this review. The centrality of the environmental problems is provided. The background of the problems, managing rising water table to control soil salinization, the role of drainage frameworks, the conjunctive use of diverse water sources, utilization of numerical models, and the use of remote sensing and GIS systems are described. And the application of the aforementioned techniques and methods in various case study regions across the globe are discussed which is followed by discussion and research gaps. Derived from the literature analysis and based on the identified research gaps, some key recommendations for future research have been made which could be useful for the stakeholders. The literature analysis revealed that an all-inclusive approach for dealing with the aforesaid environmental problems has been barely considered in the previous studies. Similarly, the continuing impacts of growing salt-tolerant plants on soil characteristics and the environment in total have not been widely considered in the previous investigations. Likewise, better irrigation practices and improved cropping systems along with the long-term environmental impacts of a particular approach has not been extensively covered in these studies. Also, previous studies have scarcely incorporated economic, social, and environmental aspects of the salinization problem altogether in their analysis. The analysis suggested that an inclusive feedback-supported simulation model for managing soil salinization should be considered in future research as the existing models scarcely considered some vital aspects of the problem. It is also suggested to enhance the sensing methods besides retrieval systems to facilitate direct detection of salinization and waterlogging parameters at large-scales. The existing time-lag between occurrence and recording of various data is also suggested to improve in the future scenario by the usage of information from multiple satellites that lessens the problems of spatial resolution by increasing the system efficiency.

Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

Identifying Seasonal Accumulation of Soil Salinity with Three-Dimensional Mapping—A Case Study in Cold and Semiarid Irrigated Fields

Soil salinity is an active and complex part of soil property in arid and semiarid irrigation areas that restricts the sustainability of agriculture production. Knowledge of seasonal distributions and migration of soil salinity is important for the management of agriculture. In this study, three-dimensional (3-D) geostatistical methods were used to construct seasonal 3-D spatial distribution maps of soil salinity, and then the quantitative analysis methods were used to study the seasonal accumulation patterns of soil salinity for the 0–150 cm soil depth in cold and semiarid irrigated rice fields. The results revealed that there were different spatial distribution and migration patterns of soil salinity in autumn 2015, spring 2016, autumn 2016, and spring 2017. The migration of soil salinity had a dispersion trend from autumn to spring, and the area of non-saline soil increased. Whereas there was an accumulation trend from spring to autumn, and the area of non-saline soil decreased. There were about 10–20% of the study area had experienced transitional changes of different soil salinity levels in different seasons. The correlation coefficient showed that there were significant positive correlations among the five depth increments (30 cm) in different seasons, and the correlations of soil salinity were higher in adjacent layers than in nonadjacent layers. The ECe values were higher in the topsoil (0–30 cm) and deeper subsoil (120–150 cm), indicating that soil soluble salts accumulated in the soil surface due to evaporation and accumulated in the bottom due to leaching and drainage. Microtopography was the major factor influencing spatial distribution of soil salinity in different seasons. The ECe values were generally higher in the swales or in areas with rather poor drainage, whereas the values were lower in relatively higher-lying slopes or that were well-drained. The results provide theoretical basis and reference for studying the variation of seasonal soil salinity in irrigated fields.

Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales.

Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran

Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R² values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications.

Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China

Precise and spatially explicit regional estimates of soil salinity are necessary to efficiently management and utilise limited land and water resources. Despite advances achieved in remote sensing over the past century, knowledge about the distribution and severity of soil salinization in economically important areas, such as oasis agroecosystems and desert-oasis ecotones (OADoE), is currently limited. An example of an area is southern Xinjiang, where the OADoE has a high anthropogenic influence. This study was conducted with the aim of mapping soil salinity in typical OADoE using remote sensing and machine learning techniques (Cubist and Random Forest, RF). A range of covariates was obtained from the multi-temporal Landsat-8 operational land imager (OLI) satellite for the period from 2013 to 2018. The values of coefficients of determination (R²), Lin's concordance correlation coefficient, root mean square error, and relative root mean squared error values, were 0.78, 0.87, 9.59, and 0.76, respectively, for the Cubist and 0.78, 0.86, 9.79, and 0.78, respectively, for RF models. The slope of the linear fitting equation was higher for the Cubist model (0.75) than for RF (0.69). The explanatory power of Cubist and RF for soil salinity variation were 33.22% and 31.41% in the agroecosystem, and 72.25% and 71.66% in desert-oasis ecotone, respectively. For the agroecosystem, the range of the predicted values for 89.13% (Cubist) and 84.78% (RF) of sample was controlled within the same observational range at an interval of 0-5 dS m^-1. Compared to single-year data (from 2013 to 2018), the ability to account for model spatial variability in soil salinity based on multi-year Landsat images was increased by 16%-35%. According to the variable importance evaluation, soil-related indices are the most important predictor variables, followed by vegetation, topography, landform, and land use, with relative importance values of 60%, 21%, 16%, and 3%, respectively. The predicted map was also broadly consistent with those obtained for Xinjiang in the Harmonized World Soil Database (HWSD) from the second national soil survey of China conducted from 1984 to 1997. The results also showed that the average value of the study area is 8.10 dS m^-1 based on the Cubist-based map whereas that of the HWSD is 10.60 dS m^-1, this implied that the overall salinity level has reduced by 23.58%. The methodological framework presented covers all prediction process steps and has considerable potential to be used in future soil salinity mapping at large scales for other similar region as OADoEs. The map derived from the Cubist/RF model revealed more detailed variation information about spatial distribution of the soil salinity compared to HWSD, and can further assist with decision-making when planning and utilising on existing soil and water resources in OADoEs.