Salinity Mapping Research Articles

On the works of the Dokuchaev Soil Science Institute in Uzbekistan

The article considers the history of the work of the Dokuchaev Soil Science Institute researchers in Uzbekistan, conducted jointly with soil scientists from Uzbekistan. These works were started in 1930–1940s. They were especially active during the Great Patriotic War, when the Soil Institute was evacuated to Tashkent. During these years, both Russian and Uzbek soil scientists participated in joint work. Outstanding scientists can be named among Russian researchers: V.A. Kovda, A.A. Rode, A.N. Rozanov and many others who contributed to the study of Uzbekistan soils. The work of the researchers from the Dokuchaev Soil Science Institute, performed on the basis of a station in the Hungry Steppe (Mirzacho'l) in connection with the development of saline lands during the creation of new irrigation systems in Central Asia, is analyzed in particular detail. Various work areas of the station researchers are discussed in the article: issues of ameliorative development of saline soils, salinity mapping based on remote sensing methods, detailed study of the reclaimed soils properties using chemical, micromorphological, mineralogical and other research methods for the determination of salinity and gypsum content of soils in the New Irrigation Zone (NIZ) of Hungry and Jizzakh steppes. The results of the work were presented in a series of publications. In the 1990s, cooperation was temporarily ceased, but at the beginning of the 21st century it was resumed.

Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest

Hyperspectral remote sensing by unmanned aerial vehicle (UAV) is an important technical tool for rapid, accurate, and real-time monitoring of soil salinity in arid zone agroecosystems. However, the key to effective soil salinity (electrical conductivity, EC) prediction by UAV visible and near-infrared (Vis-NIR) spectroscopy depends on the selection of effective features selection techniques and robust prediction characteristics algorithms. Therefore, in this study, two advanced feature selection methods and two commonly used modeling methods were applied to predict and characterize the spatial patterns of soil salinity (EC). The aim of this study was to explore the predictive performance of different feature band selection methods and to identify a robust soil salinity mapping strategy. The results demonstrated that standard normal variate (SNV) pre-processing broadened the absorption characteristics of the spectrum. Compared with competitive adaptive reweighted sampling (CARS), the optimal band combination algorithm (OBCA) strengthened the correlation with soil salinity and had a higher variable importance in the modeling. Random forest (RF) was more stable in mapping the spatial pattern of surface soil salinity compared to the partial least squares regression model (PLSR). Our results confirm the effectiveness of OBCA and RF in the developing UAV remote sensing models for surface soil salinity estimation and mapping.

Spatiotemporal Investigation of Soil Salinity Using Geospatial Techniques: A Case Study of Tehsil Toba Tek Singh

ABSTRACT Soil salinity, being a major land degradation process in arid and semiarid regions, is spreading globally at a rate of two million hectares per year. Remote sensing data are widely used for detection and mapping of saline areas, hence was the objective of this study. Soil samples were collected from the field and analyzed in laboratory to check the salinity status. Results were interpolated and a salinity map of 2011 was developed. A satellite image of Landsat 5 Thematic Mapper of this area was classified, and a land cover map was developed containing different classes. Saline areas mapped from remote sensing (58.118 square kilometers (km2) and field data (highly saline 61 km2) were compared, which showed 65% similarity between these two methods. The same was applied on Landsat Images of 1992 and 2000 to calculate the change in the saline area over this time. The results showed an increasing trend (from 43.743 km2 in 1992 to 58.118 km2 in 2011) in saline area. A water table map of this area was generated from water table data and correlation (R2) between soil salinity and water table depth was calculated, which was fairly negative (R2 = 0.7273 and R2 = 0.6436) in moderately and highly saline categories based on electrical conductivity levels. It was concluded that remote sensing data are helpful only for detection and mapping highly saline areas (having salts on surface) and less suitable to detect where there is even slight vegetation cover.

Monitoring and Mapping of Soil Salinity on the Exposed Seabed of the Aral Sea, Central Asia

The incredible drying of the Aral Sea has resulted in a large area of exposed seafloor with saline soils, which has led to catastrophic consequences. This study investigated ground-truth soil salinity data and used Landsat data to map the soil salinity distribution of the exposed seabed of the Aral Sea from 1960 onwards. The soil salinity distribution, with the depth from 0 cm to 100 cm, was analyzed. The correlation analysis was applied to find the best performance index in describing soil salinity changes. The results showed that ground-truth data of topsoil salinity (depth of 0−5 cm) exhibited a significantly strong correlation with soil salinity index 4 (SI4) among seven indices, where the Pearson correlation coefficient (r) was up to 0.92. Based on the relationship between soil salinity sampling data and SI4, a linear regression model was employed to determine the capability of evaluating the soil salinity distribution of the Aral Sea with the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.84 and 0.86 dS m−1 and 2.36, respectively. The SI4 performed well and was used to predict the soil salinity distribution on the exposed seabed. The distribution showed that soil salinity increased from the former to current shoreline. In the North Aral Sea, compared to 1986, the water area remained stable, accounting for 50.3% in 2020, and the soil salinization level was relatively low. However, the moderately and slightly saline areas dominated 73.8% and 7.5% of the South Aral Sea in 2020, with an increase of 53% and 6% transformed from the water area. The area of salinized soils dramatically increased. The strongly and extremely saline areas were mainly located in the northeastern part of the eastern basin and western part of Vozrozhdeniya Island, respectively, and were the main source of salt-dust storms. These results support the dynamic monitoring and distribution patterns of soil salinization in the Aral Sea.

Internet of things (IoT) assisted soil salinity mapping at irrigation schema level

Soil salinity accumulates a high concentration of salts in soils that interfere with normal plant growth. Early detection and quantification of soil salinity are essential to effectively deal with soil salinity in agriculture. Soil salinity quantification and mapping at the irrigation scheme level are vital to evaluating saline soil's reclamation activity. Existing solutions of salinity mapping are costly, time-consuming, and inadequate for applications at the irrigation scheme level. Internet of Things (IoT) assisted salinity mapping at the irrigation scheme level is proposed to quantify and map the soil salinity in agriculture. The proposed IoT-assisted salinity mapping characterizes the soil salinity in terms of Electric Conductivity, pH, and Total Dissolved Salts. The proposed IoT-assisted salinity mapping effectively observes impacts of reclamation activities in saline soil by frequent observation of soil salinity cost-effectively. The accuracy of proposed IoT-assisted salinity mapping is evaluated against the standard method of salinity measurements. The proposed IoT-assisted salinity mapping is cost-effective, and portable, which is very useful for site-specific treatments and soil zones management in saline soils.

Electromagnetic sensing and infiltration measurements to evaluate turfgrass salinity and reclamation

Scarce freshwater resources in arid and semiarid regions means that recreational landscapes should use recycled or low-quality waters for irrigation, increasing the risk of salinity and infiltration problems. We map salinity distribution within turf fields using electromagnetic sensing, evaluate need for leaching and evaluate post leaching results for subsequent management decisions. Electromagnetic measurements were made with two EM38 instruments positioned vertically and horizontally in order to determine salinity distribution. Sensor readings were coupled to GPS data to create spatial salinity maps. Next, optimal calibration point coordinates were determined via Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. Soil samples from 0–15 and 15–30 cm depths were used for each calibration point. Laboratory soil saturation percentage, moisture content, electrical conductivity (ECe) and pHe of saturation extracts were determined for calibration to convert resistivity measurements to ECe. Next, ECe maps were created using ESAP software. Leaching for reclamation was performed by means of sprinkling. Treated municipal wastewater was utilized both for irrigation and for reclamation leaching. Low water content and high spatial variability of soil texture adversely affected the accuracy of the readings. Pre and post leaching surveys indicate that in one fairway there was only a 43% and 58% decrease in soil salinity at 0–15 and 15–30 cm depths, respectively which is very low relative to expected results considering the amount of water applied. This relatively low reduction in salinity and the lack of runoff during irrigation combined with infiltration measurements suggests that aeration techniques for healthier grasses led to water bypassing small pores thus limiting leaching efficiency. In this instance practices to improve infiltration lead paradoxically to less salinity reclamation than expected.

Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: a Case Study in Iran

Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: a Case Study in Iran

Updated soil salinity with fine spatial resolution and high accuracy: The synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches

Soil salinization is the main source of global soil degradation. It has impeded progress towards sustainable development goals (SDGs) by threatening 20% of irrigated areas. However, in many data-poor regions, accurate soil salinization information is unavailable. Thus, an updated soil salinity map with high accuracy and resolution is urgently needed to help local governments conduct precise management. In this study, a bootstrap hybrid machine learning framework was developed combine Sentinel-2 data and environmental covariates. The Boruta algorithm was applied to input the spectral information and environmental variables to identify the primary factors influencing soil salinity. By averaging 100 model iterations within a bootstrap framework, the soil salinity mapping outcomes were compared from four machine learning methods (bagging, classification and regression tree, random forest, and gradient boosting regression tree (GBRT)). The results showed that the models driven by spectral information and environmental covariates (strategy II) explained 68∼88% of the variability in soil salinity. The model accuracy of strategy II was improved by 5%-8% over that of the models driven only by spectral information (strategy I). The GBRT yielded the most appropriate averaging performance of the four machine learning approaches within strategy II, with an R2 of 0.88, a root mean square error (RMSE) of 6.33 dS m−1, a ratio of performance to interquartile distance (RPIQ) of 4.66 and model stability (ROB) of 0.44. The bootstrap averaging method was relatively stable and had high accuracy potential. The distribution of soil salinity in the Ebinur Lake region was the result of a combination of natural and human activity influences. The proposed approach provides a soil salinity mapping strategy with a fine resolution (10 m) and high accuracy in data-poor places. It may also aid in the restoration of biodiversity, the decrease in land degradation, and the avoidance of future food output reductions in the future.

Assessment and mapping of soil salinity using electromagnetic induction and Landsat 8 OLI remote sensing data in an irrigated olive orchard under semi-arid conditions

Salinisation threatens the sustainability of irrigated olive orchards in Tunisia. Electromagnetic induction measurements and soil spectral index calculations could help to survey the soil salinity. This study aimed to map changes in the soil salinity spatial pattern using geostatistical techniques and soil spectral index regression. The study area is located in Sminja, Tunisia. It is a 665 ha olive orchard, landscaped in ridges and furrows and managed following a very high-density planting system (1.5 × 4 m<sup>2</sup>). Electromagnetic readings measured in situ with an electromagnetic device (EM38) that was fitted, in turn, to the electrical conductivity of the saturated paste of five soil depths namely: 0–20, 20–40, 40–60, 60–80 and 80–100 cm and to the average electrical conductivity of the saturated paste of the 0–100 cm soil depth. Both the ordinary kriging and universal kriging performed similarly and well in mapping the soil salinity. (R<sup>2</sup>= 0.86 and 0.89 for the 0–20 cm and the 0–100 cm depths, respectively). Our results prove that mapping the soil salinity based on electromagnetic induction and kriging methods is an effective approach, which allows one to monitor the soil salinity within permanent croplands in semi-arid conditions. Salinisation that reaches intolerable values by olive trees, is especially accumulated on the top of the ridges, where the drippers are installed. Furthermore, based on two Landsat 8 images acquired on April 30, 2019 and May 16, 2019, respectively, we calculated seven soil spectral indices. Nevertheless, multiple regression models between the electromagnetic readings and various combinations of soil spectral indices were poor. In the coming investigations, under permanent land cover, spectral index regression models should integrate not only the soil, but also vegetation indices.

A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.

Landscape-Scale Mapping of Soil Salinity With Multi-Height Electromagnetic Induction and Quasi-3D Inversion in Saharan Oasis, Tunisia

Landscape-Scale Mapping of Soil Salinity With Multi-Height Electromagnetic Induction and Quasi-3D Inversion in Saharan Oasis, Tunisia

Performance Simulation of the Payload IMR and MICAP Onboard the Chinese Ocean Salinity Satellite

The Chinese Ocean Salinity Satellite is dedicated to global sea surface salinity (SSS) mapping with two payloads onboard, which are the interferometric microwave radiometer (IMR) and the microwave imager combined active and passive (MICAP), both using the interferometric aperture synthesis radiometry. One of the payloads is an L-band interferometric radiometer system with a Y-shaped antenna array. The other one, MICAP, is a 1-D passive/active combined system, where the L-, C-, and K-band interferometric radiometers’ antenna arrays are arranged in a line, and the active device is an L-band scatterometer. Based on the payload configurations, a series of simulations is applied to analyze the payloads performance, including the brightness temperature (TB) characteristic, the SSS accuracy, and the effects of Sun and land contamination. The TB simulation results show that the IMR possesses a finer spatial resolution compared to the MICAP L-band radiometer, whereas the latter achieves a better TB radiometric resolution. With the assistance of the C- and K-band radiometers and the L-band scatterometer, the combined retrieval accuracy improves compared with the SSS retrieved using only L-band TB and auxiliary parameters. The best accuracy of SSS is achieved when considering all the available measurements from two payloads together in a combined retrieval. Finally, the results related to Sun and land contamination that endanger the interferometric radiometer measurements are presented and discussed. The performance simulation in this article has been used as a reference for the design phase of two payloads and will be updated with the development of payload manufacture.

Lateral and Vertical Mapping of Salinity Along the Coast of Ghana Using Electrical Resistivity Tomography: The Case of Central Region

Lateral and Vertical Mapping of Salinity Along the Coast of Ghana Using Electrical Resistivity Tomography: The Case of Central Region

Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data

• Earth Engine platform facilitated the analysis of multi-sensor data for soil salinity mapping. • Landsat- 8 and Sentinel-2A derived soil salinity maps were compared. • Machine learning regression methods provided good results to map soil salinity. Soil salinization caused by natural and anthropogenic factors is an important environmental hazard especially in arid and semi-arid regions of the world. Accumulation of salts in the soil is a major threat to crop production and global agriculture; therefore, rapid and precise detection of salt-affected lands is highly critical for preserving soil sustainability and supporting food production. Advancement in remote sensing techniques and machine-learning algorithms has started to contribute to fast and large-scale monitoring and mapping of soil salinization throughout the world. This paper aims to analyze the performance of three different machine-learning algorithms to map soil salinity using Landsat-8 OLI, Sentinel-2A satellite images, and ground-based electrical conductivity (EC) measurements with the aid of Google Earth Engine (GEE) platform. Classification and regression trees (CART), random forest (RF), and support vector regression (SVR) methods are implemented to create a correlation between ground measurements and satellite-derived environmental variables or spectral indices. After selecting the optimum five variables including wetness band, three soil salinity indices, and one vegetation index, soil salinity maps are generated in three machine-learning algorithms. The output soil salinity map in RF algorithm demonstrated the most reliable spatial distribution of various soil salinity classes in the selected study area. Despite CART provided slightly better prediction of soil salinity with R-squared (R 2 ) of 0.98 for Sentinel-2A data, and 0.96 for Landsat-8 OLI data in comparison with accuracy results of RF technique with R 2 of 0.96 for Sentinel-2A data and 0.94 for Landsat-8 OLI data, the output map of RF model estimated more reliable salinity levels in salt crusts, agricultural lands, drainage areas, and swamps. The corresponding result highly matched with visual interpretation. Soil salinity maps derived from SVR algorithms by using various combinations of input variables displayed relatively poor estimation of soil EC values compared to the other two methods.

Soil Salinity Mapping with Landsat 8 Oli Imagery and Random Forest Algorithm

Soil Salinity Mapping with Landsat 8 Oli Imagery and Random Forest Algorithm

Orbit Design for a Satellite Swarm-Based Motion Induced Synthetic Aperture Radiometer (MISAR) in Low-Earth Orbit for Earth Observation Applications

Soil Moisture and Ocean Salinity mapping by Earth observation satellites has contributed significantly toward a better understanding of the Earth system, such as its hydrosphere or climate. Nevertheless, an increased spatial resolution below 10 km with a radiometric resolution in the range of 2 K–3 K of radiometric data could yield a more complete picture of global hydrological processes and climate change. Operational radiometers, such as SMOS, have already approached prohibitive sizes for spacecraft due to the required large antenna apertures. Therefore, radiometer concepts based on a large number of satellites flying in close proximity (swarms) have been proposed as a possible solution. This article investigates the orbit mechanics of placing a satellite swarm-based motion induced synthetic aperture radiometer (MISAR) in low Earth orbit for Earth observation applications. The aperture synthesis antenna array is formed by a large number of individual antennas on autonomously controlled nanosatellites (deputies) and a correlator antenna in the Y-configuration carried by a chief satellite. The proposed design methodology is based on the optimization of satellite positions within a plane and the subsequent translation of coordinates into initial conditions for general circular orbits (GCOs). This enables a more computationally efficient orbit optimization and ensures the time invariance of the antenna array response. Based on this methodology, simulations have been performed with swarms consisting of up to 96 satellites. Simulations show that the spatial resolution of an aperture synthesis radiometer can be increased to less than 10 km for applications where the requirements on radiometric sensitivity are more relaxed ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta T\sim 3$ </tex-math></inline-formula> K).

Digital soil mapping of soil salinity using EM38 and quasi‐3d modelling software (EM4Soil)

AbstractLand development in the form of irrigation has led to increased agricultural productivity, but natural stores of connate salt have led to salinization. To manage salinity, baseline information about the electrical conductivity of a saturated soil paste extract (ECe – dS/m) is necessary. To value add to the limited ECe that can be collected, proximal sensed data from electromagnetic (EM) induction instruments (e.g. EM38) are increasingly being used because the measured apparent soil electrical conductivity (ECa – mS/m) can be correlated with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m), subsoil (0.6–0.9 m) and deep subsoil (0.9–1.2 m) ECe. However, errors may be introduced in prediction, given an EM38 measures ECa at depths of 0–1.5 m in vertical (EM38v) and 0–0.75 m in horizontal (EM38h) mode. To overcome this, we develop a linear regression (LR) between estimates of electrical conductivity (σ – mS/m), inverted from EM38v and EM38h using a quasi‐3d algorithm and measured ECe at the same depths. First, the LR was established (using R2) between estimates of σ inverted from ECa at heights of 0.5 (EM38v0.5 and EM38h0.5) and 0 m (EM38v0 and EM38h0), either alone or in combination, as well as in vertical mode (i.e. EM38v0.5 and EM38v0). ECa were also degraded (100%, 80%, 60% and 40%) to compare loss of prediction agreement (Lin's concordance) and accuracy (root mean square error). We use Normalized Difference Vegetation Index (NDVI) to qualitatively indicate crop growth. Moderate coefficient of determination (R2) was achieved between σ and ECe when we use the EM38v0.5 and EM38h0.5 (0.65) and EM38v0.5 and EM38v0 (0.69), but strong R2 was achieved using EM38v0 and EM38h0 (0.78) and in combination with the EM38v0.5 and EM38h0.5 (0.71). However, while good agreement (Lin's &gt; 0.8) was achieved, during a leave‐one‐out cross‐validation for most EM38 combinations, the best result was achieved using EM38v0 and EM38h0 (Lin's = 0.87). There was also loss in prediction agreement and accuracy using any of the degraded ECa data sets, however. The final 3d map of ECe, as well as NDVI, showed where highly saline (&gt;8 dS/m) areas in the west of most fields resulted as a function of leakage from the Kham‐rean Canal and topography (i.e. lower lying areas). We conclude the approach has broad application to map, potentially manage and monitor large areas of north‐east Thailand.

Upgrading the Salinity Index Estimation and Mapping Quality of Soil Salinity Using Artificial Neural Networks in the Lower-Cheliff Plain of Algeria in North Africa

Since decades ago, the Lower Cheliff plain is under the continuous influence of soil salinization induced by mismanagement of the groundwater resources. The main purpose of this study was to estimate and map soil salinity using both Salinity Index (SI) and Artificial Neural Networks (ANN). In doing so, a total of 796 soil samples of Electrical Conductivity (EC, dS.m–1) measured in laboratory combined to spectral parameters data of Landsat-8 OLI, by applying a Salinity Index (SI) and used also to training the ANN model (80% of total data), the rest of the dataset (20%) was retained for validation with both methods. The results of applying an ANN estimator based on the reflectance values of three bands: green (B3), red (B4) and near-infrared (B5) as learning input neurons, proved their interest in the estimation of EC given a high determination coefficient (R2 = 0.80) between the values of simulated truth and ground, compared to the results obtained using only the SI method giving a moderate precision (R2 = 0.42). Regarding the soil salinity mapping, the two methods generated contrasting results, the SI estimates that 68.5% of the total area is affected by salinity (underestimation) meanwhile the ANN gave an estimation of 78.8%. In a conclusion, the estimation and mapping of soil salinity using the SI method has been upgraded significantly when ANN was involved.

Soil salinity mapping using remote sensing and GIS

The monitoring of soil salinity plays a vital role in agricultural society. Soil salinity causes land degradation processes, especially in arid and semi-arid regions, which influence soil properties, reduce yield production of crops, and affect infrastructure. This research produces soil salinity mapping of the East Delta in Egypt in 1995 using remote sensing technology. A Landsat 5 image taken on 26 September 1995 was used. Radiometric and atmospheric corrections for satellite data were applied. Different salinity indices (SIs) were used, such as the normalized difference salinity index, SI1, SI2, SI3, SI4, SI5, SI6, and SI7, in addition to the normalized difference vegetation index, which was used for data filtration. The field’s electrical conductivity was measured during the period from 22 to 26 September 1995 by the Japanese International Cooperation Agency. These data were used as ground truth for the correlation analysis with different indices image bands values. Simple linear regression and mean relative error were used to find the best index, which was SI5 with a 0.87 correlation with field truth data and mean relative error equal 22.7%. This index was used to produce a salinity map of the Eastern Delta with acceptable accuracy. Finally, it is concluded that using remote sensing in salinity detection and mapping is highly appreciated.

Digital Mapping of Surface Soil Salinity in Khuzestan Province, Using Regression Kriging

Digital Mapping of Surface Soil Salinity in Khuzestan Province, Using Regression Kriging