Spatial Variability Of Soil Salinity Research Articles

Assessment of soil salinity by geo-spatial technology in Ballia district of Uttar Pradesh

Soils vary in their characteristics from one place to another place due to interactions of soil forming and processes. Assessment of spatial and temporal variation of characteristics is needed for any mean or use it's crucial to take into account the diverse spatial differences in soil properties, which has long in this context. Geographic information system (GIS) technology has enormous potential in the field of soil's assessment for upgrading soil statistic systems for an accurate and cost-effective means of understanding changes in the landscape. The future agricultural monitoring of the nutritional quality of the soil in diverse regions and villages is crucial. In this paper assessment of spatial variation of soil salinity using GIS techniques around the Ballia District of Uttar Pradesh is presented from the year 1994 to 2019. Normalized Difference Salinity Index (NDSI) was used. It showed increasing trend in salinity from 1994 to 2019 for each block. Using Landsat (TM/OLI) imageries with a 30 m spatial resolution, the spatial distribution of salinity from 1994 to 2019 was determined and low negative NDSI value was observed from - 0.143 to -0.117 in the southeast part of Ballia which can be an important explanation for soil degradation due to increasing soil salinity.

Detection of the Spatial Variability of Soil Salinity East of the Bitter Lake of the Suez Canal, Egypt, as a Basis for Management Zones Using Sentinel-2 Multispectral Imagery

ABSTRACT Soil salinity is one of the more serious problems that cause soil degradation, particularly in semi-arid and arid regions. In agricultural lands, it threatens plant growth and productivity, which requires specific management decisions for various inputs, such as fertilizers. Accurate prediction and mapping of soil salinity have become possible through remote sensing techniques. The focus of this research is to derive soil salinity from spectral indices measured from different bands of sentinel-2 satellite in order to delineate management zones (MZs). Four spectral salinity indices were used for the purpose of this study, using blue, green, red, and NIR bands in different combinations. Exponential regression best describes the relationship between salinity indices and soil electrical conductivity (EC) through groundtruthing. The results indicate that salinity indices 2 () and 4 () (SI2 and SI4) outperformed the other indices and could describe 70% and 71% of the variability in soil EC with prediction models of: and , respectively. The generated models were validated by normalized root mean square error and the d coefficient of agreement. The results of the best-fit models were 16.5% and 0.86 for SI2 and 16.7% and 0.84 for SI4, respectively. The box-and-whiskers diagram proposed that the field can be divided into four soil EC-dependent groups, which are considered MZs to support a variety of management decisions. This study shows that through sentinel-2 multispectral imagery, the spatial variability of soil salinity can be mapped and used as a basis for MZs within the studied region.

Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images

Accurately analyzing the spatial distribution of salinity at the regional scale is crucial for regional sustainable development. Currently, there are no reliable approaches for mapping salinity due to the substantial spatial variability of soil salinity. However, remote sensing technology has been successfully used to monitor soil properties in various locations. This research investigates an approach to choose the most appropriate remote sensing time window using a cloud computing platform and synthesizes multitemporal remote sensing images for high spatial resolution digital mapping of salinization at the regional scale. The research site for this experiment is the Werigan-Kuqa Oasis in Xinjiang, China. In the first stage, we screen the Landsat-8 surface reflectance datasets for four-time windows: April–October, May–September, June–August multitemporal, and July single-date images using the Google Earth Engine (GEE) cloud platform. Then, we synthesize images based on minimum, maximum, mean, and median values and construct multiple spectral indices. We completed model training and digital mapping in the second stage using a geemap. For the first time, we used geemap to integrate a local scikit-learn machine learning library with GEE to train a machine learning model. Next, we performed salinity prediction and mapping based on the best data set. This study demonstrates that (1) Various environmental variables contribute differently to modeling in the same time window. Additionally, these variables exhibit variation across different time windows. However, certain indices, including clay index (CLEX), near infrared (NIR), difference vegetation index (DVI), carbonate index (CAEX), green ratio vegetation index (GRVI), canopy response salinity (CRSI), and elevation, demonstrate stable contributions across different time windows. (2) Employing GEE to choose the suitable time window for synthesizing remote sensing images has a better prediction effect compared to single-date image prediction accuracy. Through experimental comparison, the best time window is May–September. (3) The mean synthesis approach from May to September exhibits the best performance in the model, while the median synthesis approach exhibits stable performance in the other two individual time windows. (4) Combining scikit-learn with GEE using Geemap improves model optimization and enhances the mapping performance. This study broadens the application of GEE in soil mapping and improves salinity prediction using multitemporal synthetic pictures with time frames.

Study on the Scale Effect of Spatial Variation in Soil Salinity Based on Geostatistics: A Case Study of Yingdaya River Irrigation Area

Soil salinization seriously restricts the development of agricultural economies in arid and semi-arid areas. Mastering the spatial variability characteristics of multi-scale soil salt in irrigated areas is of great significance for the improvement and utilization of saline soil and agricultural production. The middle and lower reaches of the Yingdaya River were selected as the study area, and the irrigation area was divided into three scales: the L scale (irrigation area), the M scale (township level) and the S scale (village level). A total of 131 data sets were obtained through field investigations and sampling, and the spatial variability characteristics and scale effects of the soil salt in multi-scale irrigated areas were analyzed using classical statistics, geostatistics and nested model methods. The results showed that the average soil salinities at the L, M and S scales were 1.664%, 0.263% and 0.217%, respectively, and the coefficients of variation were 2.564, 1.312 and 0.866, respectively. The soil salinities at different scales exhibited moderate spatial correlation and anisotropic characteristics, through which, the maximum variation directions for L and M were 113° and 139°, respectively, and the maximum variation direction of the S scale was 86°. The spatial distribution of the soil salinity is affected by the scale effect, but the accuracy of spatial estimations can be effectively improved by using a multi-scale nested model for interpolation. The high-value areas of soil salt in the irrigation areas were distributed in the southeastern regions of the study area, and weakened in small areas around the high-value areas. The influence of each influencing factor on the soil salinization at different scales also differed. Except for the slope, the correlations between other influencing factors and the soil salt content gradually decreased with decreases in the scale. This study provides a concise summary of the spatial variation analysis of soil characteristic variables, and also provides a scientific basis for the formulation and implementation of salinization control programs.

Analysis of the Characteristics and Cause Analysis of Soil Salt Space Based on the Basin Scale

The analysis of water-soluble salts in the soil is an important basic work for the development and research of the salinity monitoring and salinization control of saline soils, aiming at the complexity of the development of soil salinization in oases in arid and semi-arid areas. Based on the regionalization theory, GPS positioning technology was adopted in this paper to conduct fixed point sampling of the Weigan River Basin oasis from April to May 2020. Soil sampling levels were between 0 m and 0.25 m, between 1 m and 2 m, and between 2 m and 3 m, respectively, and soil physical and chemical properties were analyzed and tested from May to June 2022. The spatial variability of soil salinity at different depths in the Weigan River Basin oasis was quantitatively studied by GIS and geostatistics, and the results show that: (1) the soil salinity of each layer in the Weigan River Basin oasis was generally high, with an average value of 1.27%, 0.87%, and 0.79%, respectively, and a variation coefficient between 1.023 and 1.265, showing strong variability. (2) A reasonable number of soil sampling should be based on the 95% confidence level and 20% relative error. Therefore, the number of soil samples in the corresponding layers should not be less than 89, 70, and 91. (3) Optimal fitting models of soil salinity at the layers between 0 m and 0.25 m and between 1 m and 2 m were both spherical models, while the optimal fitting model of soil salinity at the layer between 2 m and 3 m was an exponential model. Soil salinity at different depths is affected by random factors and structural factors. Soil salinity at the layer between 0 m and 0.25 m showed moderate spatial correlation, while soil salinity at the layers between 1 m and 2 m and between 2 m and 3 m showed strong spatial correlation. (4) Three layers of soil salinity had obvious anisotropy, and the maximum variation direction was northwest–southeast. The nested model can overcome the influence of directionality on the fitting of soil salinity variation function in the layer between 0 m and 0.25 m and improve the spatial interpolation accuracy. (5) In terms of spatial pattern, the area of high soil salinity was located along the Tarim River and at the edge of oasis (i.e., desert–oasis ecotone). On the whole, the salinity shows a gradual increasing trend from the inner part of the oasis (i.e., cultivation area) to the edge of the oasis, and from the northwest to the southeast. (6) The spatial distribution pattern of soil salinity in the study area was mainly affected by the differences in topography, groundwater level, and water quality, while agricultural activities intensified the formation of the distribution pattern, reflecting the complexity of the development of soil physical and chemical properties in arid and semi-arid areas. This study provided a reference for the improvement, management, and rational utilization of saline soil in the Weigan River Basin oasis.

Spatial distribution and variability of soil salinity in film-mulched cotton fields under various drip irrigation regimes in southern Xinjiang of China

In addition to the shortage of water resources, the sustainable development of agriculture is also largely limited by soil salinization in arid and semi-arid regions of China. However, the spatial information of soil salinity at the farm scale in these regions is still poorly understood, which is the basis for the rational utilization of saline soils and allocation of crops. The main objective of this study was to explore the spatial distribution and variability of soil salinity in drip-irrigated cotton fields with film mulch under four irrigation levels (W0.6: 60% ETc, W0.8: 80% ETc, W1.0: 100% ETc and W1.2: 120% ETc, where ETc is the crop evapotranspiration) in northwest China. The results showed that the distribution of soil salinity was significantly affected by irrigation amount. The increase of irrigation amount reduced the total dissolved solids (TDS) in the soil, while it increased the area of the desalination zone around the dripper and the depth of salt accumulation below the soil surface. TDS in the 0–80 cm soil layer did not vary significantly after irrigation compared to that before irrigation, but the distribution of TDS differed from that before irrigation. The salt accumulation zone below the soil surface obviously moved downward after irrigation under sufficient irrigation conditions (W1.0 and W1.2). The variability of soil salinity was affected by irrigation amount, growth stage and soil depth. With the increase of irrigation amount and soil depth, the variability of soil salinity in the root zone decreased. The largest spatial variability of soil salinity occurred at the middle growth stage of cotton. The salt tolerance threshold of cotton (32 Mg ha−1 of TDS) and the optimal irrigation amount (W1.0) were obtained based on the relationships between irrigation amount, TDS and seed cotton yield.

Sources of spatial variability of soil salinity: the case of Beni Amir irrigated command areas in the Tadla Plain, Morocco

The origins and spatial variability of salinity within irrigated plots are a constraint to good long-term field management. Regional depletion of groundwater resources, as well as soil salinization, have been reported in the Tadla plain, a key production area in Morocco. In the northwestern part, water supply is provided only by the local water table, whose evolution in level and salinity over the last 24 years has been analyzed. A mapping of 17 plots was carried out by electromagnetic induction (ECa) after calibration of the measurements with salinity (R 2 = 0.94). These plots are cultivated with different crops (wheat, olive, sugar beet, alfalfa, carrots) with little or no crop rotation, or left fallow. The results highlight two main factors of salinization. On the one hand, the type of crop leads to salinization that increases according to water needs, which are rarely satisfied, thus favoring the accumulation of salts in the upper part of the soil profile. This first factor explained 80 to 85% of the variance in ECa measurements. On the other hand, the gravity irrigation method associated with numerous leaks is responsible for an unequal distribution of water within the plots, associated with 15–20% of the variance. These results confirm the current unsustainable management of this hydro cultural system and highlight the need for better access to quality water and improved irrigation technology. A development proposal is briefly presented.

Spatial Variability of Soil Salinity and its Influence on Rice Yield in Salt-Affected Areas using Remote Sensing Techniques

Spatial Variability of Soil Salinity and its Influence on Rice Yield in Salt-Affected Areas using Remote Sensing Techniques

Delineating reclamation zones for site-specific reclamation of saline-sodic soils in Dushak, Turkmenistan.

Soil salinization is the widespread problem seriously affecting the agricultural sustainability and causing income losses in arid regions. The major objective of the study was to quantify and map the spatial variability of soil salinity and sodicity. Determining salinity and sodicity variability in different soil layers was the second objective. Finally, proposing an approach for delineating different salinity and sodicity zones was the third objective. The study was carried out in 871.1 ha farmland in Southeast of Dushak town of Ahal Province, Turkmenistan. Soil properties, including electrical conductivity (EC), soil reaction (pH), sodium adsorption ratio (SAR), calcium carbonate and particle size distribution (clay, silt and sand fractions) in 0–30, 30–60, 60–90 and 90–120 cm soil layers were recorded. The EC values in different soil layers indicated serious soil salinization problem in the study area. The mean EC values in 0–90 cm depth were high (8 dS m-1), classifying the soils as moderate to strongly saline. Spatial dependence calculated by the nugget to sill ratio indicated a strong spatial autocorrelation. The elevation was the primary factor affecting spatial variation of soil salinity in the study area. The reclamation of the field can be planned based on three distinct areas, i.e., high (≥12 dS m-1), moderate (12–8 dS m-1) and low (<8 dS m-1) EC values. The spatial trend analyses of SAR values revealed similar patterns for EC and pH; both of which gradually decreased from north to the south-west. The amount of water needed to leach down the salts from 60 cm of soil profile is between 56.4–150.0 ton ha-1 and the average leaching water was 89.8 tons ha-1. The application of leaching water based on the amount of average leaching water will result in higher or lower leaching water application to most locations and the efficiency of the reclamation efforts will be low. Similar results were recorded for sulfur, sulfuric acid and gypsum requirements to remediate sodicity. The results concluded that the best management strategy in planning land development and reclamation schemes for saline and sodic soils require accurate information about the spatial distribution of salinity and sodicity across the target area.

USE OF REMOTE SENSING AND GIS FOR MODELING AND MAPPING THE SOIL SALINITY OF AGRICULTURAL LAND IN EL-FAYOUM GOVERNORATE, EGYPT

This study was conducted aiming to use remote sensing and GIS for modeling and mapping the soil salinity of agricultural land in El-Fayoum governorate. This included geomorphological and pedological characteristics as well as classification and land capability evaluation for the soils of the studied area. The interpretation of satellite multispectral images Landsat 8 indicated that, this area has five main geomorphic units with different landforms. These geomorphic units are: 1) Recent river terraces (High, Moderate and Low), occupies; 2) Basins (Overflow basins & Decantation basins),; 3) Old river terraces (High, moderately high, Moderate and Low),; 4) Former lake bed,; and 5) Water bodies (Fish ponds and Lake Qaroun). Thirty profiles were selected representing the soils of these units. Land and site features are observed and registered. Soil profiles were dug deep down to 150 cm or water table level, morphologically described, and samples were collected representing the vertical variations in each profile for integrated physiochemical analyses. Also, thirteen water samples were collected from the surface and groundwater resources in each landform for chemical analysis to use their properties in the land capability evaluation model and assess their quality for irrigation purpose. Moreover, forty-three surface soil samples were collected in addition to statistical regression models were developed based on remotely sensed indicators to predict and map spatial variation in soil salinity in the studied area. Most of studied soils of are deep and well drained having almost flat to gently sloping topography. Some soils that having moderately high-water table level, are moderately deep and imperfectly drained. Most of the studied soils have clay loam texture grade. All studied soils are slightly alkaline, non-to slightly saline. Some soils of old river terraces and all soils of Overflow basin as well as Former lake bed are moderately saline having sodicity effect. All studied soils have low Organic matter (OM) and gypsum contents. Most soils are moderately calcareous, and few are strongly calcareous. Statistical regression models were developed based on remotely sensed indicators to predict and map spatial variation in soil salinity in the studied area. There is a high correlation found between the Salinity indices SI5 and SI6 and estimated EC Model Validation. Most of the studied soils haven’t any diagnostic horizons and classified under Entisols order. Few soils are classified under Aridisols. The land capability evaluation indicated that, 10% from the studied soils are considered as very good (C1), 59% as good (C2), and 20% as fair (C3). The water samples taken from the Nile water canals have none sodium hazard, slight to moderate chloride and salinity hazard. Other collected water samples have severed salinity, sodicity and chloride hazards

Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests

Salinization and alkalization are predominant environmental problem world-wide which their accurate assessment is essential for determining appropriate ways to deal with land degradation, for better soil and crop management. In the current research, a combination of random forests and covariate data were used to assess spatial variability of soil salinity and sodicity in 436 km2 agricultural salt-affected land in Kurdistan Province, Iran. Using the conditioned Latin hypercube sampling method, 295 soil samples were sampled across the study area, and then soil reaction (pH), electrical conductivity (EC), and sodium adsorption ratio (SAR) were measured. Covariate data including terrain attributes, remotely-sensed data, groundwater table, and categorical maps were acquired. Random forest (RF) models were used to predict the spatial distribution of pH, EC, and SAR by making a relationship between soil data and covariates. Furthermore, three optimization algorithms (particle swarm optimization-PSO, genetic algorithm-GA, and bat algorithm-BAT) were used to explore if the hybridized RF works better than the standard RF. Results of 10-fold cross-validation with 100 replications indicated that the accuracy of RF + PSO was higher for predicting pH (RMSE = 0.52 and R2 = 0.67), EC (RMSE = 2.32 dSm−1 and R2 = 0.57), and SAR (RMSE = 8.98 and R2 = 0.54, respectively) in comparison to the other implemented models. Furthermore, the results disclosed that the most important covariates to predict pH, EC, and SAR were groundwater table, categorical maps, salinity index, and multi-resolution ridge top flatness. Besides, the results indicated that the mean values for pH, EC, and SAR in lowland and bare land were significantly different from the other physiographic units and land uses, respectively. Importantly, the classified map of salt-affected soils highlighted areas with a high risk of exceeding critical threshold values of pH, EC, and SAR, which is located in the center of the study area, and showed that 6.30%, 3.1%, and 4.6% of the study area are saline-sodic soil, saline soil, and sodic soil, respectively. These up to date spatial soil information on severity of soil salinity and sodicity is crucial for agricultural management of affected areas and the proposed method can be used to the other similar regions.

Evaluating the impact of flood irrigation on spatial variabilities of soil salinity and groundwater quality in an arid irrigated region

Abstract Soil salinization is a key problem limiting the sustainable development of agriculture in arid areas. To explore the quantitative influence of irrigation on soil salinity and groundwater quality, spatial variability of salt at different soil depths and total dissolved solids in groundwater before and after irrigation was analysed in the Hetao Irrigation District, China. The spatial variability of soil salinity before and after irrigation was moderate, with high coefficients of variation observed in shallow soil. After irrigation, amounts of salt were transferred into the groundwater, leading to the deterioration of groundwater quality, the average salt flux through each interface was −153.66, 169.12, 130.13 and 318.48 g/m2, for 20, 50, 100 and 150 cm soil depths, respectively. All salt moved downward vertically except for 0–20 cm soil layer, and the average soil salt flux of shallow layers was significantly lower than that of deep layers. Compared soils of different depths and land types, salt discharge occurred in cultivated land, while salt accumulation occurred in shallow soil in wasteland after irrigation. Irrigation could help remove salt from cultivated land and deeper soil; however, it had almost no effect on shallow soil of other land uses.

Soil salinity prediction based on scale-dependent relationships with environmental variables by discrete wavelet transform in the Tarim Basin

The variation in soil salinity is affected by environmental factors that occur at different scales with varying intensities. It is critical to adequately consider environmental variables under scale effects for digital soil mapping which has been minimally discussed in previous studies. The objectives of this research are to analyze the scale-dependent variability in soil salinity distribution under environmental controlling factors using discrete wavelet transform (DWT) techniques and to compare the differences between the accuracy of soil salinity predictions with and without multiple scale-specific relationships. Thirteen environmental factors related to soil salinity that included influencing environmental factors and indicative environmental factors involving climate, soil, terrain, and vegetation were extracted at 500 m intervals along four transects through farmland and salt-affected land situated at the oasis and oasis-desert ecotones of Xinjiang, China. Each spatial series of soil salinity and environmental variables along the four transects was separated into seven scale components (six details components, namely D1 through D6, and one approximation component, namely A6). A Hilbert transform was used to identify the specific spatial scales of each scale component in the DWT procedure. The results indicate that 21.77 km and >32 km were the dominant scales, which explained approximately 60–80% of the spatial variation of soil salinity throughout the oases. The prediction accuracy with wavelet reconstruction that depended on all the scale components of environmental variables is significantly improved compared with the accuracy of those with the stepwise multiple linear regression method at a single sampling scale. The generalized difference vegetation index (GDVI) was the major predictor of soil salinity on salt-affected land, while evapotranspiration and the terrain ruggedness index (TRI) were the major contributing factors in farmland inside the oases. This study demonstrated that specific scale-dependent relationships can reveal the scale control of soil salinity variation and had the potential to improve the prediction accuracy of soil properties.

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 &lt; 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p &lt; 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.

Quantitative Evaluation of Spatial and Temporal Variation of Soil Salinization Risk Using GIS-Based Geostatistical Method

Soil salinization is one of the environmental threats affecting the sustainable development of arid oases in the northwest of China. Thus, it is necessary to assess the risk of soil salinity and analyze spatial and temporal changes. The objective of this paper is to develop a temporal and spatial soil salinity risk assessment method based on an integrated scoring method by combining the advantages of remote sensing and GIS technology. Based on correlation coefficient analysis to determine the weights of risk evaluation factors, a comprehensive scoring system for the risk of salinity in the dry and wet seasons was constructed for the Ebinur Lake Wetland National Nature Reserve (ELWNNR), and the risk of spatial variation of soil salinity in the study area was analyzed in the dry and wet seasons. The results show the following: (1) The risk of soil salinity during the wet season is mainly influenced by the plant senescence reflectance index (PSRI), deep soil water content (D_wat), and the effect of shallow soil salinity (SH_sal). The risk of soil salinity during the dry season is mainly influenced by shallow soil salinity (SH_sal), land use and land cover change (LUCC), and deep soil moisture content (D_wat). (2) The wet season was found to have a high risk of salinization, which is mainly characterized by moderate, high, and very high risks. However, in the dry season, the risk of salinity is mainly characterized by low and moderate risk of salinity. (3) In the ELWNNR, as the wet season changes to dry season (from May to August), moderate-risk area in the wet season easily shifts to low risk and risk-free, and the area of high risk in the wet season easily shifts to moderate risk. In general, the overall change in salinity risk of the ELWNNR showed a significant relationship with changes in lake water volume, indicating that changes in water volume play an important role in the risk of soil salinity occurrence. Ideally, the quantitative analysis of salinity risk proposed in this study, which takes into account temporal and spatial variations, can help decision makers to propose more targeted soil management options.

VARIABILIDADE ESPACIAL DA UMIDADE E CONDUTIVIDADE ELÉTRICA DO SOLO EM VALE ALUVIAL UTILIZANDO TÉCNICAS GEOFÍSICAS

Os métodos de indução eletromagnética na agricultura de precisão possuem alto potencial em aplicações voltadas para estudos da variabilidade espacial da salinidade e umidade do solo. O objetivo deste trabalho foi verificar o desempenho do EM38® em vale aluvial no semiárido para fins de aplicação de agricultura de precisão, e avaliar a dinâmica e dependência espacial da umidade e da condutividade elétrica em diferentes profundidades do solo. A área de estudo está localizada no município de Parnamirim, Pernambuco, no Campus Avançado de Agricultura Irrigada de Parnamirim da Universidade Federal Rural de Pernambuco. Duas malhas regulares foram adotadas, uma de 10 x 10 m para amostragem de condutividade elétrica aparente do solo (CEa) estimada com o aparelho EM38® e outra de 20 x 10 m para amostragem de solo, para determinação da umidade, salinidade, silte e argila nas camadas de 0-0,3 m, 0,3-0,6 m e 0,6-0,9 m. Informações diretas e indiretas foram utilizadas nas análises geoestatísticas, construção de semivariogramas e krigagem de mapas. O Grau de Dependência Espacial dos atributos avaliados variou entre forte e moderado. Maior umidade na camada mais profunda do solo foi constatada, as maiores CEa foram encontradas na camada 0,3-0,6 m. O EM38® mostrou-se eficiente na estimativa da condutividade elétrica do solo, e possibilitou a validação de modelo de regressão previamente calibrado na mesma área, em período com restrição hídrica no solo.

Soil salinity detection and monitoring using Landsat data: a case study from Kot Addu, Pakistan

Despite decades of research in soil mapping, characterizing the spatial variability of soil salinity across certain area remains a crucial challenge. This research illustrates the potential use of Landsat 8 satellite reflectance data (30 m resolution) to facilitate soil salinity mapping. For this purpose, soil samples from surface soils of Kot Addu, Muzaffargarh District in Pakistan, were collected in the 3rd week of January 2017 (January 20, 2017, to January 27, 2017). Multi-temporal data (atmospherically corrected Landsat 8 images from the United States Geological Survey (USGS) website; January 19, 2017) and soil sample electrical conductivity (ECe) were obtained and processed for various indices’ calculation and supervised classification. Soil sample ECe and pixel index values were compared for correlation using linear regression. Normalized Differential Vegetation Index (NDVI) showed a coefficient of determination (R2) of 54.98%, Soil Adjusted Vegetation Index (SAVI) has R2 = 54.98%, Mosaic Simple Ratio (MSR) has R2 = 47.59%, and Moisture Stress Index (MSI) indicated R2 of 51.48%. These indices were stacked, and supervised classification of stacked images was performed to produce 3 salinity classes ( 10 dS m−1). The maximum area of Kot Addu falls at < 4 dS m−1 class, while in other two classes small patches of soil salinity were observed which were distributed across the map. It was concluded that remote sensing technology can be used for mapping the spatial distribution of the soil salinity. The results suggest that Landsat 8 image data with information of soil ECs can be used to identify spatial variations of soil salinity in arid areas.

Multi-scale spatial variability of soil salinity in typical fields of the Yellow River Delta in summer

Soil salinization severely hinders the development of agricultural economy in the Yellow River Delta. Clarifying the spatial variability of soil salinity at multiple scales in the field is of great significance for the improvement and utilization of saline soils and agricultural production. In this study, by dividing the three dimensions of field, plot and ridge, we collceted 152 sets of conducti-vity data through field survey sampling in a summer maize field in Kenli County of the Yellow River delta. The methods of classic statistics, geostatistics and Kriging interpolation were used to analyze the spatial variability and scale effects of multi-scale soil salt in the field. The results showed that soil in this area was moderately salinized, with the extent of soil salinity moderately varying at three scales. From the field, plot to the ridge scale, with the decreases of sampling scale, the variability of soil salinity increased and the standard deviation increased. The ridge and plot scales showed strong spatial correlation. The optimal model was Gaussian model, which was mainly affected by structural factors. The field scale was of medium spatial correlation, with exponential model as the optimal one, which was influenced by both random factors and structural factors. The spatial distribution characteristics of soil salinity at different scales were significantly different. The spatial chara-cteristics at small scale were masked at large scale, showing obvious scale effect. The distribution of soil salinity at the micro-ridge scale between ridges had obvious variation. Soil salt content gradually decreased with the micro-topography from high to low, while vegetation coverage changed from sparse to dense.

Spatio‐temporal variations of soil moisture and salinity and their effects on cotton growth in a mulched drip irrigation field*

AbstractExperiments were carried out to study the dynamics of soil moisture and salinity and the effect factors on cotton growth in the Xinjiang Uygur Autonomous Region of China in 2012 and 2013. Regular soil samples, germination rate, plant height and cotton yield rate were surveyed during the growing stage. Soil moisture and salinity in the inter‐film zone (IFZ) and near tape zone (NTZ) began to vary in mid‐April. The water content and salinity were lower in the IFZ and NTZ respectively, and the differences reached a significant level limited to the upper 5 cm layer of the soil. However, the difference increased from June to September, when drip irrigation was used and transpiration was enhanced. Spatial standard deviations of soil moisture and salinity increased with the relevant spatial mean, and the spatial variation of soil salinity was higher in the NTZ than in the IFZ after elution and evaporation of the growing seasons. Pearson correlation tests showed that soils with higher soil moisture, lower salinity and more fine particles were generally favourable for cotton growth and production, while germination rate and plant height were mainly influenced by soil properties in the upper 20 cm of soil. Cotton yield could be influenced by soil properties from 50 to 80 cm below the top surface. © 2020 John Wiley &amp; Sons, Ltd.

Spatial variability and agglomeration of soil salinity in Minjiang estuary wetland, Southeast China

Understanding the spatial variability and agglomeration of soil salinity is of great significance for the sustainable development of estuarine wetland. Landsat 8 OLI remote sensing image, digital elevation mode and soil surface samples of Minjiang estuary wetland of Fuzhou were used as the data sources. The correlation analysis and principal component analysis were combined to select significant environmental variables and to reduce their dimensions. We analyzed the spatial variability of soil salinity with support vector regression ordinary kriging model (SVROK) and regression kri-ging model (RK), and quantified spatial agglomeration of soil salinity by the spatial autocorrelation analysis. The results showed that three principal components (PCs) extracted by the principal component analysis could explain at least 85% of the total variance in the original dataset and reflected the comprehensive information of vegetation cover, soil properties and topography. Both soil salinity and its residuals were affected by structural factors and random factors. The SVROK model based on principal component (PCs) as input variables can more accurately reflect the spatial variability of soil salinity, with a trend of "higher in the north and lower in the south". The Moran's I of soil salinity was more than 0.5, with significant positive spatial autocorrelation and a higher spatial aggregation degree, displaying the spatial agglomeration characteristics of "high value agglomeration, high value widespread, high value surrounded by low value".