Spatial Distribution Of Soil Salinity Research Articles

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.

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.

Estimating the Spatial Distribution of Soil Salinity with Geographically Weighted Regression Kriging and Its Relationship to Groundwater in the Western Jilin Irrigation Area, Northeast China

Estimating the Spatial Distribution of Soil Salinity with Geographically Weighted Regression Kriging and Its Relationship to Groundwater in the Western Jilin Irrigation Area, Northeast China

Multivariate Analysis of Soil Salinity in a Semi-Humid Irrigated District of China: Concern about a Recent Water Project

The Chaobai River (CBR) basin in northern China is experiencing an unprecedented continuous inflow of external water via the South–North Water Diversion Project, which has channeled water from the southern part of the country to the north. Consequently, the steady rise of groundwater table in recent years is threatening soil salinity regulation. The purpose of this study was to describe the status of salinity of the surface soil in the CBR basin and to evaluate the impact of environmental factors including groundwater table on the spatial distribution of soil salinity using multivariate analysis (MVA) technique. In this study, 10 chemical variables of soil samples collected in 204 sites along CBR were analyzed, considering their interaction with three environmental factors: the density of irrigation canals, groundwater depth and topography. Statistical analysis mainly consisted of principal component analysis (PCA), redundancy analysis (RDA) and clustering analysis (CA). The results allow defining the surface soil in the CBR basin as a slightly saline and moderately alkaline media. The first two axes of multivariate model approximately explains 51% of the observed variability and allows distinguishing two main domains: the saline and the alkaline. The variability of the saline domain, defined by major cations and anions, is obviously controlled by macro environmental factors, of which the density of irrigation canals and groundwater depth contributes 71% and 28%, respectively, while that of the alkaline domain, related to pH and bicarbonate, mainly manifests as singular behaviors of soil groups like rice cultivation or sewage irrigation. The results suggests that more attention should be paid to the ongoing water table rise to help inform future land management decisions and to prevent a double threat of both groundwater and surface water on soil salinization. Meanwhile, this study shows the enormous potential of MVA technique, specifically the complementary duo of RDA and CA, for integrating both global and local information of soil salinity and environmental factors.

Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment

AbstractThe land use and land cover (LULC) changes and the implications of soil and irrigation water salinity have adverse effects on crop production and the ecosystems of arid and semiarid regions. In this study, an attempt has been made to analyze and monitor the LULC changes using multitemporal Landsat data for years 1986, 1998, 2007, and 2016 in Al-Ahsa Oasis, Saudi Arabia. In addition, efforts were made to measure the spatial distribution of soil and irrigation water salinity along the oasis. The supervised maximum likelihood classification method was applied to classify the individual images independently. Moreover, soil samples were collected at surface soil depth from the selected LULC types, namely, date palm, croplands, and bare land. Also, groundwater samples were collected from bore wells located in agricultural farms. The spatial distribution of the soil salinity (Ece) and irrigation water salinity (ECiw) was classified based on the Food and Agriculture Organization guidelines. The results showed that significant changes in LULC patterns have occurred during 1986–2016 in the study area. The ECewas found higher in date palm compared with cropland and bare land. However, the spatial distribution of the ECiwover the oasis indicated that 94% of irrigation water ranged between moderate and severe salinity risk. The study concludes that salinity management practices need to be developed in the study area aiming to sustain crop yields, improve soil properties, and minimize the environmental impacts of LULC changes on the ecosystem of Al-Ahsa Oasis.

Analysis of saline land and determination of the level of salinity of irrigated lands with use of the geographic information system technologies

In Uzbekistan, more than half of population lives in rural area, their well-being depends on quality of land and water resources availability. Quality of land is determined by ameliorative indicators: ground water depth level, ground water salt amount and salinity of soil. These factors do not appear naturally but rather due to the human activity. Inefficient irrigation and excessive consumption of irrigation water on irrigated land in Boyavut District of Syrdarya region of Uzbekistan within several decades have led to a salinization of soil. The primary objective of this article is determination of the level of salinity of soil for modelling spatial distribution of soil salinity throughout an irrigated land by using GIS technology. This technology is focused on automation of development and creation of ameliorative maps, while totally eliminating manual operations. Nowadays, ameliorative expedition specialists still create cadastral map using tracing paper over the marginal areas within irrigated lands based on their ameliorative conditions and by selecting from the three thematic maps and then selecting the poorest conditions of ameliorative indicators. The suggested technology is designed for professionals of cadastral subdivisions of regional ameliorative expeditions, who use the GIS-based software, such as ArcView 3.2. or ArcGIS 10x; their duties include creating of thematic maps based on salinity levels of irrigated lands. Exact coordinates of collection sites of soils samples (collected in 2018-2019) were determined using GPS. The Inverse Distance Weighting (IDW) interpolation method was applied to use that data to create ameliorative maps categorized by the salinity levels (non-saline, slightly saline, saline area and highly saline areas). Those maps were then analysed to develop procedures on how to improve ameliorative conditions of irrigated areas.

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

Soil salinization is a global problem, which threatens agricultural productivity and sustainability, especially in arid and semi-arid regions. Soil salinity and moisture are important factors affecting agricultural production in arid regions. However, few studies have considered the influence of topographic factors on the spatial distribution patterns of soil salinity and moisture. This research aims to explore the spatial distribution characteristics and its influencing factors of soil salinity and moisture in the oasis farmland of arid areas. In this paper, GIS and geostatistics methods were applied to analyze the spatial distribution characteristics and variability of soil salinity and moisture, and then the corresponding proxy variables were used to quantitatively study the influence factors by using the geographical detector model. The results showed the coefficients of the variation of soil salinity and moisture to be 71.25% and 31.89%, respectively. There was moderate spatial autocorrelation of soil salinity and moisture. Soil salinity in the southwest was higher than in the northeast, and soil moisture in the northwest and southeast were lower than in the center and the northeast edge. The main influencing factors were available phosphorus, roughness of terrain, alkaline nitrogen, available potassium, and elevation. Combined action of topographic factors and soil nutrients has a major influence on the spatial distribution of soil salinity and moisture. Therefore, developing a suitable fertilizer regime under different topographic conditions could be an effective way to promote the sustainability of oasis agriculture in arid areas.

SPATIAL MODELING OF SOIL SALINITY USING MULTIPLE LINEAR REGRESSION, ORDINARY KRIGING AND ARTIFICIAL NEURAL NETWORK METHODS IN THE LOWER CHELIFF PLAIN, ALGERIA

Soil salinity is one of the most damaging environmental issues worldwide, essentially in arid and semi-arid regions, caused by various factors. Spatial estimation and prediction of salinity is important to predict land evaluation in order to develop and determine leaching factor and the precise management for maximum production. The Lower Cheliff is characterized by the augmentation of rate of soil salinity with 80 % of area. In this study, we have analyzed the relationship between both elevation and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. To conduct this work, we have taken 406 samples and analysis of electric conductivity as well as measurement of the elevation with a GPS, we analyzed the correlations of soil salinity with elevation. In this study we have given a great focus on the use of the multiple linear regressions, Ordinary kriging and artificial neural network methods, the results showed that soil salinity had a good correlation with elevation, and according to the values of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE), implied superiority of MLP model with the value of R² = 0.994, RMSE= 0.63 and MAE = 0.33.

Spatial distribution of soil salinity and potential implications for soil management in the Manas River watershed, China

AbstractUnderstanding the watershed‐scale spatial distribution of soil salinity and its compositions is important for soil management. Here, we present the first study on the Manas River watershed in northwest China. In this study, we took soil samples in upper 20 cm of soil from 186 locations across the watershed and measured total salt concentration (TSC), salt ion composition and soil particle size distribution (PSD). We found that on average topsoil TSC tended to increase, from 3.55 g kg−1 in upstream regions to 19.40 g kg−1 in downstream regions. The stoichiometric analysis showed that the equivalence ratio of soil Cl‐ to SO42− increased from 0.53 in upstream regions to 2.12 in midstream regions, and further to 3.76 in downstream regions; thus, the soil types were classified into chloride–sulfate, sulfate–chloride and chloride soils types, respectively. Additionally, proportions of small (<2 μm in diameter) and large (>2,000 μm) soil particles increased, while that of medium sizes (2–50 μm) decreased from upstream to downstream, with an increasing coefficient of variance (CV) in PSD. Taken together, watershed‐scale topsoil salinity may be horizontally characterized by increased TSC and Na+ & Cl− proportions, greater equivalence ratio of Cl− vs. SO42− and more balanced distribution of PSD along with surface water flow. Results demonstrated that soil salinity and its ions compositions showed a great variation across the watershed scale, suggesting that soil management may consider the spatial heterogeneity of saline–alkaline soil types, and our results provided scientific guidance for local soil management and restoration.

Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria

In semi-arid and arid areas, soil salinity has adverse effects both on the environment and agricultural production. The main causes of this salinization come from natural or anthropogenic processes, which is certainly an environmental problem that affects more than 20% of the world's land. This study was made in order to map the spatial distribution of soil salinity of the irrigated perimeter of El Ghrous in southeastern Algeria. These maps were performed based on data collected from 190 soil samples from 0 to 15 cm deep. We used ordinary kriging (OK) to analyze the spatial variability of soil salinity, while indicator kriging (IK) was used to analyze salinity versus threshold values. The salinity map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method showed the different classes of salinity according to Durand's classification with moderately saline 3rd order dominance, while the unsalted soil (EC < 0.6 dS m<sup>-1</sup>) represents a very low percentage (1.5%). The indicator kriging (IK) was carried out by four thresholds which correspond to the salinity class limits: EC > 0.6, EC > 1, EC > 2, EC > 3, and EC > 4 dS m<sup>-1</sup>, for developing probability maps to determine risk areas. This study has shown the spatial trend of soil salinity by geolocation of different classes, and to carry out risk maps using geostatistical techniques.

Spatial Analysis of Soil Salinity in the Indus River Delta, Pakistan

Soil salinization is one of the most damaging environmental problems in coastal areas, including Indus River Delta (IRD). Due to the reduction of flow in the Indus basin, saline water from the Arabian Sea is intruding into the IRD and has degraded the agricultural lands drastically. Focusing on the gravity of the problem, the present study was designed to explore the spatial distribution of soil salinity in the IRD. Physicochemical analysis of 375 soil samples randomly collected from 125 different locations within the study area was used. Analysis revealed that for the top 0-20cm of soil, about 66.4% of the samples had electrical conductivity (EC) values, and 72.8% sodium content (ESP) values higher than the FAO guidelines. Similarly, for soil depth of 20-40cm, 60.8% of the EC values, and 72% of ESP exceeded the safe limits. Finally, for 40-60cm of soil depth, 56.8% of the EC values and 79.2% of the ESP values were higher than the safe limits. Spatial analysis revealed that more than 50% of the IRD samples were affected by soil salinity. Reduced freshwater flow and the entry of saline water into the delta may likely be the causes of soil salinity in the IRD.

Soil salinity mapping in Everglades National Park using remote sensing techniques and vegetation salt tolerance

Everglades National Park (ENP) is a hydro-ecologically enriched wetland with varying salinity contents, which is a concern for terrestrial ecosystem balance and its nearby urban sustainability. In this study, spatio-temporal soil salinity maps are created using remote sensing techniques, coupled with literature review to understand vegetation salt tolerance properties, and field assessments entailing insitu electric conductivity (EC) measurements. The mapping first entailed the execution of a supervised machine learning technique—the maximum likelihood classification algorithm—to delineate seven vegetation-based land cover classes for the area, namely, mangrove forest, mangrove scrub, low-density forest, sawgrass, prairies and marshes, barren lands with woodland hammock and water, for the years 1996, 2000, 2006, 2010 and 2015. The classifications for 1996–2010 yielded accuracies of 82%–94%, and the 2015 classification was supported through ground truthing. Afterwards, EC tolerance thresholds for each vegetation class were established, which yielded soil salinity maps comprising four soil salinity classes—i.e., the non- (EC = 0–2 dS/m), low- (EC = 2–4 dS/m) and high-saline (EC ≥4 dS/m) areas. The soil salinity maps visualized the spatial distribution of soil salinity with no significant temporal changes. Furthermore, insitu EC measurements carried out at 23 sampling sites covering all land cover classes mostly validated (91% samples were tested within range) the estimated soil salinity ranges for the latest distribution. The approach of using land cover classes to sense salinity ranges in an urban-proximal ecosystem is pragmatic and application oriented, attributing to novel and useful study upshots considering the diversifying ecological context.

THE EFFECT OF DIFFERENT WELL WATER QUALITY AND IRRIGATION METHODS ON SOME CHEMICAL PROPERTIES AND SALINITY DISTRIBUTION OF SOIL IN ARID AREAS: A CASE STUDY OF ISMAILIA GOVERNORATE

The increasing demand for irrigation water to secure food for growing populations with limited water supply suggests re-thinking the use of non-conventional water resources. The study was conducted to monitor the salinity distribution in soil under different well water quality and irrigation methods in specific sites of Ismailia Governorate region. Water and soil samples were collected from hundred randomly selected sites. Both soil and water samples were analyzed for chemical characteristic which included salinity, cations, anions and SAR. The findings indicated that the mean soil pH was 7.35 while the mean EC value was 3.73 dS/m. The mean cations in the water samples were 7.43, 5.98, 0.17, and 23.33 meq/l for Ca2+, Mg2+, K+, and Na+, respectively and the a mean value of Sodium Adsorption Ratio (SAR) was 9.11. The mean bicarbonates concentration detected in the irrigation water was 4.43 meq/l, while the mean chloride and sulphate were 29.95 and 2.49meq/l, respectively. The mean values of EC of soil were 3.94, 5.98 and 4.56 for drip, sprinkler and surface irrigation methods in depth 0-20 cm, respectively. While, in soil depth 20-40 cm the mean values of EC were 6.78, 4.94 and 2.99 for drip, sprinkler and surface irrigation methods, respectively. The spatial distribution of soil salinity in the soil horizons were found to be higher at shallow depths of 0-20cm and decreasing gradually up to a depth of 20-40cm. This trend is clear with surface and sprinkler irrigation method. In drip irrigated method, the salinity distribution showed a different pattern from that observed in surface and sprinkler irrigation. Salinity was the lowest in the surface layer (0-20 cm), and increased gradually with soil depth (20-40 cm). Salt concentration factor (SCF) of Ismailia soil under studies ranged between0.14 to 6.33. The results showed that SCF varies according to water and soil quality and irrigation methods.EC of soil was positively and significantly correlated with Ca2+, Mg2+, Na+ , K+, Cl-, HCO-3 but positively and not significantly correlated with pH andSO2-4.The general trend showed that soil salinity increased with an increase in water salinity. Correlation coefficient (r) between EC of irrigation water and EC of soil was 0.550**.

Three‐dimensional mapping of soil salinity in the southern coastal area of Laizhou Bay, China

AbstractSoil salinization is an important driver of land degradation and has been a major factor inhibiting agricultural production and development in the southern coastal area of Laizhou Bay. In this study, 3‐D and 2‐D geostatistics were respectively used to evaluate the spatial distribution of soil salinity and groundwater total dissolved solids (TDS) and depth. Results showed that soil electrical conductivity differed greatly within the same soil layer, and the difference in average electrical conductivity of different soil layers was relatively small. The mean value of TDS exceeded 3.0 g L−1 and the mean value of groundwater depth was 13.359 m. The geographic distribution showed that the soil salinity gradually decreased from the north to the south horizontally, and the soil salinity showed a bottom accumulation tendency vertically. Over 90% of the study area has TDS values exceeding 1.0 g L−1, and approximately one‐third of the area had a groundwater depth less than 3.0 m. TDS and groundwater depth had good correlations with soil salinity, with coefficients of determination of 0.544 and 0.572, respectively. Changes in vegetation corresponded to changes in soil salinity. Soil salinity gradually decreased perpendicular to coastal lines in tidal flats without vegetative coverage to inland agricultural areas of wheat and vegetables. Soil salinization in the study area is mainly slight and moderate. Reasonable regional management measures are conducive to improving land economic benefits. The results provide a theoretical basis and reference for the partition, improvement, management of saline soil, and support for land degradation control.

Conductivity gradients as inferred by electromagnetic-induction meter (EM38) readings within a salt-affected wetland in Saskatchewan, Canada

The change from deep-rooted grass and shrub vegetation to annual-cropping dryland farming has contributed to serious soil salinization challenges on the semi-arid North American Great Plains. In some cases, cultivation of the Great Plains has increased the availability of water, causing dominant sulfate salts to travel from the uphill areas to depressions where it will surface when water evaporates at the soil surface. A potential solution could include the replanting of the native deep-rooted vegetation, which requires knowledge of the spatial distribution of soil salinity. This study tested the soil factors influencing electromagnetic-induction meter (EM38) readings of soil salinity distribution around wetlands. The objectives were to: (1) predict growth and survival of Salix dasyclados Wimm. (cv. ‘India’) along a salinity gradient in a small wetland, and (2) investigate whether newly established willows affected water-table fluctuations, which would indicate their phreatophytic nature or their ability to obtain their water supply from the zone of saturation. Results indicated significantly lower salinity values for sampling points with EM38 readings above 175 and 250 mS m−1 for height and survival, respectively. In addition, diurnal fluxes of the water table in areas of good willow growth and lower salinity indicated that cultivar ‘India’ was phreatophytic in these areas and therefore has great potential for being used to combat saline seeps.

Soil Salinity of an Urban Park after Long‐Term Irrigation with Saline Ground Water

Core Ideas Changes in soil salinity after 46 yr of irrigation. Using electromagnetic induction to map soil salinity and sodicity. Correlate soil salinity with irrigation system distribution uniformity. Chamizal National Park, located in El Paso, TX, extends over 140,000 m2 and has been irrigated with saline water for 46 yr. In recent years, turf areas in the park have severely degraded and bare spots have developed. Root zone salinity and sodicity were suspected to be the main reasons for the turf conditions. Developing salinity management and remediation strategies to improve turf quality requires information on the distribution of salinity (ECe) within the turf root zone. Electromagnetic induction (EMI) uses apparent electrical conductivity (ECa) to delineate salinity distribution, and is reportedly superior to traditional wet chemistry analyses. This study was conducted to investigate the spatial distribution of soil salinity and sodicity using the EMI technique. In addition, we assessed irrigation distribution uniformity and compared findings with root zone salinity and sodicity. The EMI data correlated well with saturated paste results and indicated that root zone salinity ranged from <1 to 43 dS m−1. In several parts of the park, ECe exceeded the threshold values for bermudagrass of 15 dS m−1. Root zone sodium adsorption ratio values ranged from <1 to 21 mmol1/2 L−1/2 and in areas where increased runoff and surface ponding were observed, values exceeded the threshold level of 12 mmol1/2 L−1/2. Correlation analysis between irrigation uniformity parameters and standard deviation of ECe and SAR values revealed that more than 90% of the variability of EC and SAR in the top 30 cm of the root zone could be explained by irrigation uniformity.

Using sequential Gaussian simulation to assess the uncertainty of the spatial distribution of soil salinity in arid regions of Northwest China

ABSTRACTAccurately mapping and monitoring the spatial distribution pattern of soil salinity is essential for sustainable soil management and decision-making. The kriging-based interpolation technique is generally used to map the spatial distribution of soil salinity; however, this technique neglects the variation caused by interpolation for each unsampled location. The sequential gaussian simulation (SGS) is an effective tool to collect mapping uncertainties at several locations simultaneously, which is not possible in the kriging-based technique. Soil electrical conductivity has been widely used as an index for soil salinity. Based on 0–100 cm soil profile from 117 locations in the Manas River basin, Northwest China, the SGS algorithm was used to assess the uncertainty of the spatial distribution of soil electrical conductivity. It was found that the SGS algorithm was reliable in reproducing the spatial distribution of soil electrical conductivity. The SGS algorithm reproduced the sample statistics reasonably well. The standard deviations of the samples generated by the SGS algorithm (0.463–0.508 (dS m−1)) were closer to the actual samples (0.675 (dS m−1)) than those generated by kriging (0.454 (dS m−1)). Most of the study area was lightly affected by salinity. Around 30% of the study area was moderately affected, and the heavily affected areas were sporadically scattered across the study area. The spatial uncertainty at multiple point presented a declining trend as the critical probability at a single point increased. The spatial estimation of the soil electrical conductivity in multiple point was more robust than that in the local location because of the low uncertainty.

Spatial prediction of saline and sodic soils in rice‒shrimp farming land by using integrated artificial neural network/regression model and kriging

ABSTRACTIn the context of widespread saline and sodic soil, mapping and monitoring spatial distribution of soil salinity and sodicity are important for utilization and management in agriculture lands. In this study, two-stage assessment was proposed to predict spatial distribution of saline and sodic soils. First, artificial neural network (ANN) and multiple linear regressions (MLR) model were used to predict sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) based on soil electrical conductivity (EC) and pH. Then, the Kriging interpolation method combined with overlay mapping technique was used to perform saline spatial predictions in the study area. The model accuracy level is evaluated based on coefficient of determination (R2) and root mean square error (RMSE). In the first stage, the values of R2 and RMSE of SAR and ESP were 0.94, 0.17 and 0.94, 0.24 for ANN, and 0.35, 0.52 and 0.34, 0.76 for MLR, respectively. Similarly, in the second stage, the RMSE of ANN-Kriging were much closer to 0 and relatively lower than MLR-Kriging and Kriging. The results show that ANN-Kriging can be used to improve the accuracy of mapping and monitoring spatial distribution of saline and sodic soil in areas that develop the rice-shrimp cultivation model.

Prediction of soil salinity in the Yellow River Delta using geographically weighted regression

ABSTRACTIt is essential to determine the content and spatial distribution of soil salinity in a timely manner because soil salinization can cause land degradation on a regional scale. Geographically weighted regression (GWR) is a local regression method that can achieve the spatial extension of dependent variables based on the relationships between the dependent variables and environment variables and the spatial distances between the sample points and predicted locations. This study aimed to explore the feasibility of GWR in predicting soil salinity because the existing interpolation methods for soil salinity in the Yellow River Delta are still of low precision. Additionally, multiple linear regressions, cokriging and regression kriging were added to compare the accuracy of GWRs. The results showed that GWR predicted soil salinity with high accuracy. Furthermore, the accuracy was improved when compared to other methods. The root mean square error, correlation coefficient, regression coefficient and adjustment coefficients between the observed values and predicted values of the validation points were 0.31, 0.65, 0.57 and 0.42, respectively, which were better than that of other methods, indicating that GWR is an optimal method.

Geostatistical monitoring of soil salinity for precision management using proximally sensed electromagnetic induction (EMI) method

Accurate and real-time information on soil salinity is required to understand the evolution of soil salinization, to develop appropriate management strategies, and to implement practices to improve the soil productivity and land development. This is particularly the case for the coastal agroecosystem in which the farmlands were developed from marine alluvial sediments and naturally saline. Our primary objectives were to calibrate soil salinity with proximal sensing electromagnetic induction (i.e., EM38) measurements obtained at different heights above ground, to delineate the spatial pattern of soil salinity and its temporal dynamics, and to determine potential management zones of soil salinity. Intensive EMI survey with EM38 positioned at five heights above ground was made in a coastal salt-affected farmland, and soil samples at 0–1.0 m layer were collected for calibration. This work was repeatedly conducted on January 9, 2012, September 3, 2012, March 31, 2013, and October 16, 2013. EMI measurements were calibrated to root zone soil salinity using multiple linear regression (MLR) and restricted maximum likelihood, and spatial pattern of soil salinity on different dates was generated, and management zones were determined based upon the spatial–temporal variability of soil salinity. Results indicated that EMI measurements obtained at 0, 20, 40, and 60 cm heights above ground were optimal to develop a MLR model identifying the relationship between root zone ECe and ancillary variables. The generated spatial distribution of soil salinity showed that the predominant soil types were moderately saline and very saline soils during the four survey periods. The spatial pattern of soil ECe was closely related to the layout of field drainage ditches, and significant spatial trend was well fitted by a parabolic curve in various periods. A general increasing trend of soil ECe occurred, and a drastic increase in soil salinity was observed from January 9, 2012, to March 31, 2013, and soil ECe across the field exhibited temporal dynamic during the survey periods. Two clearly defined management zones were determined based upon the spatial variability and temporal stability of ECe. Site-specific management and periodical soil salinity monitoring and assessment were recommended in this experimental site. It could be concluded that repeated proximal sensing EMI surveys could be effectively used to characterize the spatial pattern of soil salinity, to monitor the spatial–temporal changes in soil salinity, and to distinguish site-specific zones for precision management of soil salinity.