Soil Salinity Mapping Research Articles

Remote sensing is a viable tool for mapping soil salinity in agricultural lands

Soil salinity negatively impacts the productivity and profitability of western San Joaquin Valley (WSJV) farmland. Many factors, including drought, climate change, reduced water allocations, and land-use changes could worsen salinity conditions there, and in other agricultural lands in the state. Mapping soil salinity at regional and state levels is essential for identifying drivers and trends in agricultural soil salinity, and for developing mitigation strategies, but traditional soil sampling for salinity does not allow for accurate large-scale mapping. We tested remote-sensing modeling to map root zone soil salinity for farmland in the WSJV. According to our map, 0.78 million acres are salt affected (i.e., ECe > 4 dS/m), which represents 45% of the mapped farmland; 30% of that acreage is strongly or extremely saline. Independent validations of the remote-sensing estimations indicated acceptable to excellent correspondences, except in areas of low salinity and high soil heterogeneity. Remote sensing is a viable tool for helping landowners make decisions about land use and also for helping water districts and state agencies develop salinity mitigation strategies.

Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques.

Traditional surveying methods of soil properties over landscapes are dramatically cost and time-consuming. Thus, remote sensing is a proper choice for monitoring environmental problem. This research aims to study the effect of environmental factors on soil salinity and to map the spatial distribution of this salinity over the southern east part of Tunisia by means of remote sensing and geostatistical techniques. For this purpose, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer data to depict geomorphological parameters: elevation, slope, plan curvature (PLC), profile curvature (PRC), and aspect. Pearson correlation between these parameters and soil electrical conductivity (ECsoil) showed that mainly slope and elevation affect the concentration of salt in soil. Moreover, spectral analysis illustrated the high potential of short-wave infrared (SWIR) bands to identify saline soils. To map soil salinity in southern Tunisia, ordinary kriging (OK), minimum distance (MD) classification, and simple regression (SR) were used. The findings showed that ordinary kriging technique provides the most reliable performances to identify and classify saline soils over the study area with a root mean square error of 1.83 and mean error of 0.018.

Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

Satellite Thermography for Soil Salinity Assessment of Cropped Areas in Uzbekistan

AbstractA change of canopy temperature can indicate stress in vegetation. Use of canopy temperature to assess salt stress in specific plant species has been well studied in laboratory and greenhouse experiments, but its potential for use in landscape‐level studies using remote sensing techniques has not yet been explored. Our study investigated the application of satellite thermography to assess soil salinity of cropped areas at the landscape level. The study region was Syrdarya Province, a salt‐affected, irrigated semi‐arid province of Uzbekistan planted mainly to cotton and wheat. We used moderate‐resolution imaging spectroradiometer satellite images as an indicator for canopy temperature and the provincial soil salinity map as a ground truth dataset. Using analysis of variance, we examined relations among the soil salinity map and canopy temperature, normalized difference vegetation index, enhanced vegetation index, and digital elevation model. The results showed significant correlations between soil salinity and canopy temperature, but the strength of the relation varied over the year. The strongest relation was observed for cotton in September. The calculated F values were higher for canopy temperature than for the other indicators investigated. Our results suggest that satellite thermography is a valuable landscape‐level approach for detecting soil salinity in areas under agricultural crops. © 2016 The Authors. Land Degradation & Development Published by John Wiley & Sons Ltd.

Mapping Soil Salinity and Evaluation of Water Quality in Siwa Oasis Using GIS

Siwa Oasis represents a promising area for agricultural expansion projects in the western desert of Egypt due to the availability of water resources. However, these areas are suffering from land degradation problems, especially soil salinity. Accordingly, the main objectives of this work were to evaluate and map soil salinity as well as groundwater quality in Siwa Oasis. Therefore, twenty representative soil profiles were randomly distributed throughout the studied area. These profiles were sampled based on the development of soil horizons. A total of 46 soil-samples were collected and these samples were analyzed for their physical and chemical properties. Groundwater samples were collected from irrigation-wells and they were analyzed for their chemical parameters. Landsat images were also acquired at three different periods to monitor the changes in vegetation cover, soil salinity and water logging. The obtained results indicated a wide variability within soil physical and chemical properties in the Oasis. Most of the suited soils were very saline, non-sodic based on the EC and ESP values (EC= 77 dSm-1 and ESP= 14.62 in average). Agricultural areas were increased from 22 km2 in 1992 to 81 km2 in 2015. On the other hand, saline soils were increased from 35 km2 to 64 km2 and water logged areas were also increased from 19 to 51 km2 from 1992 to 2015, respectively. These results could be due to the expansion in reclamation-projects, increase in crop irrigation and poor drainage. The salinity of groundwater varied from 2.28 to 5.45 dSm-1 (3.76 in average), which indicates a degradation in its quality. In summary, soils in Siwa Oasis are salt-affected and they need a proper land reclamation program and development of effective irrigation and drainage systems.

Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.

Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed ECe values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high ECe values and provide a simple and effective operational alternative for saline topsoil detection and mapping.

Mapping soil salinity and a fresh-water intrusion in three-dimensions using a quasi-3d joint-inversion of DUALEM-421S and EM34 data

In order to understand the drivers of topsoil salinization, the distribution and movement of salt in accordance with groundwater need mapping. In this study, we described a method to map the distribution of soil salinity, as measured by the electrical conductivity of a saturated soil-paste extract (ECe), and in 3-dimensions around a water storage reservoir in an irrigated field near Bourke, New South Wales, Australia. A quasi-3d electromagnetic conductivity image (EMCI) or model of the true electrical conductivity (σ) was developed using 133 apparent electrical conductivity (ECa) measurements collected on a 50m grid and using various coil arrays of DUALEM-421S and EM34 instruments. For the DUALEM-421S we considered ECa in horizontal coplanar (i.e., 1mPcon, 2mPcon and 4mPcon) and vertical coplanar (i.e., 1mHcon, 2mHcon and 4mHcon) arrays. For the EM34, three measurements in the horizontal mode (i.e., EM34-10H, EM34-20H and EM34-40H) were considered. We estimated σ using a quasi-3d joint-inversion algorithm (EM4Soil). The best correlation (R2=0.92) between σ and measured soil ECe was identified when a forward modelling (FS), inversion algorithm (S2) and damping factor (λ=0.2) were used and using both DUALEM-421 and EM34 data; but not including the 4m coil arrays of the DUALEM-421S. A linear regression calibration model was used to predict ECe in 3-dimensions beneath the study field. The predicted ECe was consistent with previous studies and revealed the distribution of ECe and helped to infer a freshwater intrusion from a water storage reservoir at depth and as a function of its proximity to near-surface prior stream channels and buried paleochannels. It was concluded that this method can be applied elsewhere to map the soil salinity and water movement and provide guidance for improved land management.

Mapping Slight and Moderate Saline Soils in Irrigated Agricultural Land Using Advanced Land Imager Sensor (EO-1) Data and Semi-Empirical Models

ABSTRACTAround the world, especially in semi-arid regions, millions of hectares of irrigated agricultural land are abandoned each year because of the adverse effects of irrigation, mainly secondary salinity and sodicity. Accurate information about the extent, magnitude, and spatial distribution of salinity and sodicity will help create the sustainable development of agricultural resources. In Morocco, south of the Mediterranean region, the growth of the vegetation and potential yield are limited by the joint influence of high temperatures and water deficit. Consequently, the overuse of surface and ground water, coupled with agricultural intensification, generates secondary soil salinity. Knowing when, where, and how salinity may occur is very important to the sustainable development of any irrigated production system. Remedial actions require reliable information to help set priorities and to choose the type of action that is most appropriate in each situation. Ground-based electromagnetic measurements of soil electrical conductivity (EC) are generally accepted as the most effective method for quantification of soil salinity. Unfortunately, these methods are expensive, time consuming, and need considerable human resources for land surveying. Moreover, the dynamic nature of soil salinity in space and time makes it more difficult to use conventional methods for comparisons over large areas. A major challenge of remote sensing, as a potential alternative technique, is to detect different levels of soil salinity. The main aim of this research is to assess the potential of the Advanced Land Imager (ALI) sensor on board the Earth Observing-1 (EO-1) satellite, with its rich infrared bands, for the discrimination and mapping of slight and moderate soil salinity in the Tadla’s irrigated agricultural perimeter in Morocco. To achieve this goal, semi-empirical predictive models developed in a previous study using second order regression analysis between the EC of salt-affected soils and different spectral salinity indices were applied to the ALI image. This was atmospherically corrected and the radiometric sensor drift was calibrated. Visual comparisons and statistical validation of these models using ground truth were undertaken in order to identify the best semi-empirical model for slight and moderate salinity mapping. The obtained results show that the model based on the Normalized Difference Salinity Index (NDSI) does not give any results. The model based on the Salinity Index-1 (SI-1) and the SI-Advanced Space-borne Thermal Emission and Reflection Radiometer (SI-ASTER) confuses vegetation with high soil salinity, although the model does bring out areas of lower salinity. Both R2 of 0.67 for the SI-1 and 0.65 for the SI-ASTER further reinforce that these models cause too much confusion to be used with accuracy for salt-affected soil detection. The semi-empirical model based on Soil Salinity and Sodicity Index-1 (SSSI-1) performs better than the two last models. However, there is a relative confusion between the classes in the slight and moderate salinity and in areas that are shown by the validation map; the higher class of salinity does not appear to contain higher levels of salinity. The statistical validation of this model reinforces what is seen on the derived map with only an R2 = 0.68. The model based on the SSSI-2 clearly provides the best results in comparison to the ground truth. Its derived map gives the closest overall visual approximation of the EC map, with a whole range of values. With a statistical validation of R2 = 0.97 to the ground truth, it is by far the best performance of any of the other models, and the different classes are statistically well separated, which further reinforces the accuracy of the visual analysis.

GIS-mapping spatial distribution of soil salinity for Eco-restoring the Yellow River Delta in combination with Electromagnetic Induction

Soil salinization is one of serious ecological problems around the world, which seriously restricts the stability of ecosystem and the economic development of agriculture. Mapping and monitoring spatial distribution of soil salinity is important for management of ecology and agriculture. This study was carried out to explore the spatial distribution of soil salinity in the Yellow River Delta using the portable device EM38-MK2 with geostatistical analysis. Apparent soil electrical conductivities were measured under four kinds of measurement modes (0.5H, 0.5V, 1.0H and 1.0V, respectively). The results revealed that electrical conductivity of 1:5 soil to water extract (ECe) varied from 0.965 to 1.872dSm−1 for all sampled soil profiles, and the salinity of topsoil was the highest, which indicated that soil soluble salts accumulated to the surface. The salinity in the top layer showed strong spatial variability while salinities of other layers were moderate. Soil salinity displayed a significant zonal distribution, gradually decreasing with the increase of distance to the coastline. The regions with high ECa values were located in the north and the east of the study area, whereas regions with low ECa values were located in the south and the west parts. The correlation coefficient showed that salinities of adjacent two soil layers reached a significant level of 0.01, and gradually decreased with increasing soil depth. The linear interpretation models with ECa as independent variables and ECe as dependent variables for each depth were with R2 between 0.828 and 0.919. The interpretation models, taking ECa and ECe of 0- 15cm depth as independent variables, and ECe of each layer in 15–100cm depth as dependent variables, were with higher R2 between 0.930 and 0.953. The mean error (ME) showed that there was small positive deviation in 40–100cm whereas a high positive deviation in the topsoil (0–40cm). The scatter plots also indicated that the models had better accuracy of salinity estimation in the top soil layers (0–80cm). The results provides solid basis for eco-restoring in the Yellow River delta.

SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM&amp;lt;sup&amp;gt;+&amp;lt;/sup&amp;gt; satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

Abstract. Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM+ satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

Towards decadal soil salinity mapping using Landsat time series data

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985–2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985–2000 (−0.74gkg−1/10a, p<0.001), and increased within 2000–2015 (0.79gkg−1/10a, p<0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys.

Reliable and real-time information on soil and crop properties is important for the development of management practices in accordance with the requirements of a specific soil and crop within individual field units. This is particularly the case in salt-affected agricultural landscape where managing the spatial variability of soil salinity is essential to minimize salinization and maximize crop output. The primary objectives were to use linear mixed-effects model for soil salinity and crop yield calibration with horizontal and vertical electromagnetic induction (EMI) measurements as ancillary data, to characterize the spatial distribution of soil salinity and crop yield and to verify the accuracy of spatial estimation. Horizontal and vertical EMI (type EM38) measurements at 252 locations were made during each survey, and root zone soil samples and crop samples at 64 sampling sites were collected. This work was periodically conducted on eight dates from June 2012 to May 2013 in a coastal salt-affected mud farmland. Multiple linear regression (MLR) and restricted maximum likelihood (REML) were applied to calibrate root zone soil salinity (ECe) and crop annual output (CAO) using ancillary data, and spatial distribution of soil ECe and CAO was generated using digital soil mapping (DSM) and the precision of spatial estimation was examined using the collected meteorological and groundwater data. Results indicated that a reduced model with EMh as a predictor was satisfactory for root zone ECe calibration, whereas a full model with both EMh and EMv as predictors met the requirement of CAO calibration. The obtained distribution maps of ECe showed consistency with those of EMI measurements at the corresponding time, and the spatial distribution of CAO generated from ancillary data showed agreement with that derived from raw crop data. Statistics of jackknifing procedure confirmed that the spatial estimation of ECe and CAO exhibited reliability and high accuracy. A general increasing trend of ECe was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone ECe coincided with those of daily rainfall, water table and groundwater data. Long-range EMI surveys and data collection are needed to capture the spatial and temporal variability of soil and crop parameters. Such results allowed us to conclude that, cost-effective and efficient EMI surveys, as one part of multi-source data for DSM, could be successfully used to characterize the spatial variability of soil salinity, to monitor the spatial and temporal dynamics of soil salinity, and to spatially estimate potential crop yield.

Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia

Soil salinization is one of the most common land degradation processes, especially in arid and semi-arid regions, where precipitation exceeds evaporation. Under such climatic conditions, soluble salts are accumulated in the soil, influencing soil properties with ultimate decline in productivity. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The present study presents a model to map soil salinity using remote sensing and geographic information systems. Different spectral indices were calculated from original bands of landsat images. Statistical correlation between field measurements of electrical conductivity (ECe) and remote sensing spectral indices showed that salinity index (SI) had the highest correlation with ECe. Combining these remotely sensed and ECe variables into one model yielded the best fit with R2=0.78. The result obtained from SI was not only area-wise, but also with its intensity. Out of the total area, 18.8% and 23% were identified as moderately and slightly saline, respectively. This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity in irrigation areas.

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

ABSTRACTSpatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECav and horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted ECe over the study area accurately (R2 = 0.87, ME = −1.04 and RMSE = 16.36 dSm−1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.

Evaluation of Different Soil Salinity Mapping Using Remote Sensing Techniques in Arid Ecosystems, Saudi Arabia

Land covers in Saudi Arabia are generally described as salty soils with sand dunes and sand sheets. Waterlogging and higher soil salinity are major challenges to sustaining agricultural practices in Saudi Arabia principally within closed drainage basins. Agricultural practices in Saudi Arabia were flourishing in the last two decades. The newly reclaimed lands were added annually and distributed all over the country. Irrigation techniques are mostly modernized to fulfill water saving strategies. Nevertheless, water resources in Saudi Arabia are under stress and groundwater levels are depleted rapidly due to heavy abstraction that may exceed crop water requirements in most of the cases due to high evaporation rates. The excess use of irrigational water leads to severe soil salinity problems. Applications of remote sensing technique in agricultural practices became widely distinctive and cover multidisciplinary principal interests on both local and regional levels. The most important remote sensing applications in agricultural practices are vegetation indices which are related to vegetation and water especially in an arid environment. Soil salinity mapping in an arid ecosystem using remote sensing data is a demanding task. Several soil salinity indices were implemented and evaluated to detect soil salinity effectively and quantitatively. Thematic maps of soil salinity were satisfactorily produced and assessed.

История изучения и основные направления развития методов оценки и картографирования засоленности почв аридных и семиаридных территорий

The national and foreign literature was reviewed in terms of milestones and prospects of development of soil salinity assessment and mapping in arid regions. The contribution of the department of salt-affected soils of V.V. Dokuchaev Soil Science Institute into the development of methods for assessment and mapping of salt-affected soils in Russia and Central Asia during the second half of the XXth century was illustrated. The analysis of the state of assessment and inventory of salt-affected soils in Russia by the beginning of the XXIst century has shown that the latest data given in the published literature on properties and distribution of salt-affected soils are outdated as they are based on the 30-year-old studies. Undoubtedly, these data should be revised with the use of modern methods and approaches widely adopted worldwide. A uniform guideline for the assessment of salt-affected soils of Russia should be developed with the due account for international approaches. Such a guideline will be necessary for inventory and creation of database of salt-affected soils compatible with international databases. The methodology of mapping and inventory of salt-affected soils based on the uniform approach to the assessment of salt-affected soils and specified methodology of large-scale mapping with the use of remote sensing data and proximal soil sensing should be developed.

Predicting Soil Salt Content Over Partially Vegetated Surfaces Using Non-Negative Matrix Factorization

Remote sensing has been widely applied to map soil salinity in the last few decades. However, a notable decrease in the accuracy of soil salt content (SSC) predictions occurred when the soil surfaces were partially vegetated. To minimize the influence of partial vegetation cover on spectral reflectance, we applied a spectral separation method, non-negative matrix factorization (NMF), to extract soil spectral information from a controlled field experiment with three varying factors [vegetation coverage, soil moisture content (SMC), and SSC]. The method was applied without prior knowledge of, or restrictions on the mixed and source spectra. Soil samples and spectral reflectance collected on three periods were used to determine the effectiveness of NMF-extracted soil spectra with partial least squares regression (PLSR). The results indicated that SSC can be predicted by bare soil spectra. NMF effectively separated soil spectra from the observed spectra, and the SSC was successfully predicted from the extracted soil spectra within a wide range of vegetation cover (0%–64.7%) within defined moisture levels ( ${ by weight). The approach proposed in this study will improve the prediction accuracy of SSC for partially vegetated surfaces and will expand the application of remote sensing.

Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains

Hyperspectral remote sensing is widely used for analyzing and estimating the severity of soil salinity in arid and semi-arid regions, throughout the world. The present study is an attempt to map the various soil salinity severity classes using different hyperspectral indices generated using EO-1 Hyperion data and Support Vector Machine (SVM) method, in the Mathura region of Indo-Gangetic plain of India. Various hyperspectral indices such as Soil Adjusted Vegetation Index (SAVI), Desertification Soil Index (DSI), Salinity Index (SI) and Normalized Difference Water Index (NDWI) were chosen, generated and effectively used for characterizing and mapping soil salinity severity. Salt infestation in the study area was categorized into four classes of normal, slight, moderate, high soil salinity. Hyperspectral indices helped in identification of various features like vegetation, waterlogged area and soil areas under various classes of soil salinity. The salinity index and desertification soil indices were found to respond well to varying degrees of soil salinity. The SVM technique generated soil salinity map with overall classification accuracy of 78.13 percent, with a kappa statistic of 0.71. The results indicated highest accuracy in high soil salinity class in comparison to other classes, attaining producers and users accuracies of 85.71% and 90.0% respectively. Slight saline class showed poor producers and users accuracy. The result showed high accuracy for mapping soil salinity severity with machine learning classifier like SVM using various indices generated from hyperspectral remote sensing data. These generated images can be effectively used in planning of various management practices and effective reclamation measures of salt affected soils.