Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

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The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-3-26-324-333 ◽

2020 ◽

Vol 26 (3) ◽

pp. 324-333

Author(s):

Nozimjon Teshaev ◽

Bunyod Mamadaliyev ◽

Azamjon Ibragimov ◽

Sayidjakhon Khasanov

Keyword(s):

Soil Salinity ◽

Information Technologies ◽

Vegetation Index ◽

Arable Land ◽

Salt Content ◽

Secondary Data ◽

Soil Surface ◽

Soil Salinization ◽

Arid Zones ◽

Agronomic Practices

Soil salinization, as one of the threats of land degradation, is the main environmental issue in Uzbekistan due to its aridic climate. One of the most vulnerable areas to soil salinization is Sirdarya province in Uzbekistan. The main human-induced causes of soil salinization are the insufficient operation of drainage and irrigation systems, irregular observations of the agronomic practices, and non-efficient on-farm water use. All of these causes considerably interact with the level of the groundwater, leading to an increase in the level of soil salinity. The availability of historical data on actual soil salinity in agricultural lands helps in formulating validated generic state-of-the-art approaches to control and monitor soil salinization by remote sensing and geo-information technologies. In this paper, we hypothesized that the Soil-Adjusted Vegetation Index-based results in soil salinity assessment give statistically valid illustrations and salinity patterns. As a study area, the Mirzaabad district was taken to monitor soil salinization processes since it is the most susceptible territory of Sirdarya province to soil salinization and provides considerably less agricultural products. We mainly formulated this paper based on the secondary data, as we downloaded satellite images and conducted an experiment against the in-situ method of soil salinity assessment using the Soil-Adjusted Vegetation Index. As a result, highly saline areas decreased by a factor of two during the studied period (2005–2014), while non-saline areas increased remarkably from a negligible value to over 10 000 ha. Our study showed that arable land suitability for agricultural purposes has been improving year by year, and our research held on this district also proved that there was a gradual decrease in high salt contents on the soil surface and land quality has been improved. The methodology has proven to be statistically valid and significant to be applied to other arid zones for the assessment of soil salinity. We assume that our methodology is surely considered as a possible vegetation index to evaluate salt content in arable land of either Uzbekistan or other aridic zones and our hypothesis is not rejected by this research.

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Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

Remote Sensing ◽

10.3390/rs12244043 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4043

Author(s):

Hongyi Li ◽

Xinlu Liu ◽

Bifeng Hu ◽

Asim Biswas ◽

Qingsong Jiang ◽

...

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Three Dimensions ◽

Soil Core ◽

Arid Areas ◽

Field Scale ◽

Natural Factors ◽

Secondary Salinity ◽

Spatio Temporal ◽

Semi Arid

Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.

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Electromagnetic Induction Measurements for Investigating Soil Salinization Caused by Saline Reclaimed Water

Atmosphere ◽

10.3390/atmos13010073 ◽

2021 ◽

Vol 13 (1) ◽

pp. 73

Author(s):

Lorenzo De Carlo ◽

Gaetano Alessandro Vivaldi ◽

Maria Clementina Caputo

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Electromagnetic Induction ◽

Reclaimed Water ◽

Soil Salinization ◽

Repeated Measurements ◽

Soil Electrical Conductivity ◽

Geophysical Research ◽

Soil Response ◽

Short Period

This paper focused on the use of electromagnetic induction measurements in order to investigate soil salinization caused by irrigation with saline reclaimed water. An experimental activity was carried out during the growing season of tomato crop in order to evaluate expected soil salinization effects caused by different saline agro-industrial wastewaters used as irrigation sources. Soil electrical conductivity, strictly related to the soil salinity, has been monitored for three months by means of Electromagnetic Induction (EMI) measurements, and evident differences in the soil response have been observed. The study highlighted two aspects that can improve soil investigation due to the utilization of geophysical tools. First, EMI data can map large areas in a short period of time with an unprecedented level of detail by overcoming practical difficulties in order to massively sample soil. At the same time, repeated measurements over time allow updating real-time soil salinity maps by using accurate correlations with soil electrical conductivity. This application points out how integrated agro-geophysical research approaches can play a strategic role in agricultural saline water management in order to prevent soil salinization risks in medium to long-term periods.

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An Investigation of the Relationship between the Electrical Conductivity of the Soil Saturated Paste Extract ECe with the Respective Values of the Mass Soil/Water Ratios 1:1 and 1:5 (EC1:1 and EC1:5)

Proceedings ◽

10.3390/proceedings2110661 ◽

2018 ◽

Vol 2 (11) ◽

pp. 661 ◽

Cited By ~ 3

Author(s):

George Kargas ◽

Iakovos Chatzigiakoumis ◽

Athanasios Kollias ◽

Dimitrios Spiliotis ◽

Petros Kerkides

Keyword(s):

Electrical Conductivity ◽

Soil Water ◽

Correlation Coefficient ◽

Soil Salinity ◽

Water Mass ◽

Soil Samples ◽

Standard Methodology ◽

Mass Ratios ◽

The Relationship

The standard methodology for the soil salinity assessment is provided through the determination of the electrical conductivity (EC) of the soil saturated paste extract, ECe. This approach is cumbersome and tedious. Instead of this, it appears easier to measure the EC of various soil over water mass ratios, (soil:water), such as 1:1, 1:5. In the present study an attempt is made to compare the ECe methodology with the methods providing the EC1:1 and EC1:5. ECe, and EC1:1 or EC1:5 values were obtained from 198 soil samples from 5 different locations in Greece. It was shown that the methods providing EC1:1 and EC1:5 values are linearly correlated to the ECe methodology with a high correlation coefficient (R2 > 0.93).

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

Remote Sensing ◽

10.3390/rs12162601 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2601

Author(s):

Jianli Ding ◽

Shengtian Yang ◽

Qian Shi ◽

Yang Wei ◽

Fei Wang

Keyword(s):

Electrical Conductivity ◽

Spatial Distribution ◽

Water Resources ◽

Soil Salinity ◽

Soil Salinization ◽

Mean Value ◽

Land Evaluation ◽

Bare Land ◽

Apparent Electrical Conductivity ◽

Southern Xinjiang

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

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Assessing soil salinity dynamics using time-lapse electromagnetic conductivity imaging

SOIL ◽

10.5194/soil-6-499-2020 ◽

2020 ◽

Vol 6 (2) ◽

pp. 499-511

Author(s):

Maria Catarina Paz ◽

Mohammad Farzamian ◽

Ana Marta Paz ◽

Nádia Luísa Castanheira ◽

Maria Conceição Gonçalves ◽

...

Keyword(s):

Electrical Conductivity ◽

Cross Sections ◽

Soil Salinity ◽

Sea Water ◽

Management Strategies ◽

Time Lapse ◽

Soil Salinization ◽

Agricultural System ◽

Prediction Ability ◽

Conductivity Imaging

Abstract. Lezíria Grande de Vila Franca de Xira, located in Portugal, is an important agricultural system where soil faces the risk of salinization due to climate change, as the level and salinity of groundwater are likely to increase as a result of the rise of the sea water level and consequently of the estuary. These changes can also affect the salinity of the irrigation water which is collected upstream of the estuary. Soil salinity can be assessed over large areas by the following rationale: (1) use of electromagnetic induction (EMI) to measure the soil apparent electrical conductivity (ECa, mS m−1); (2) inversion of ECa to obtain electromagnetic conductivity imaging (EMCI) which provides the spatial distribution of the soil electrical conductivity (σ, mS m−1); (3) calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (ECe, dS m−1), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity cross sections using the obtained calibration equation. In this study, EMI surveys and soil sampling were carried out between May 2017 and October 2018 at four locations with different salinity levels across the study area of Lezíria de Vila Franca. A previously developed regional calibration was used for predicting ECe from EMCI. Using time-lapse EMCI data, this study aims (1) to evaluate the ability of the regional calibration to predict soil salinity and (2) to perform a preliminary qualitative analysis of soil salinity dynamics in the study area. The validation analysis showed that ECe was predicted with a root mean square error (RMSE) of 3.14 dS m−1 in a range of 52.35 dS m−1, slightly overestimated (−1.23 dS m−1), with a strong Lin's concordance correlation coefficient (CCC) of 0.94 and high linearity between measured and predicted data (R2=0.88). It was also observed that the prediction ability of the regional calibration is more influenced by spatial variability of data than temporal variability of data. Soil salinity cross sections were generated for each date and location of data collection, revealing qualitative salinity fluctuations related to the input of salts and water either through irrigation, precipitation, or level and salinity of groundwater. Time-lapse EMCI is developing into a valid methodology for evaluating the risk of soil salinization, so it can further support the evaluation and adoption of proper agricultural management strategies, especially in irrigated areas, where continuous monitoring of soil salinity dynamics is required.

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Soil Salinity Assessment Using Saturated Paste and Mass Soil:Water 1:1 and 1:5 Ratios Extracts

Water ◽

10.3390/w10111589 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1589 ◽

Cited By ~ 6

Author(s):

George Kargas ◽

Iakovos Chatzigiakoumis ◽

Athanasios Kollias ◽

Dimitrios Spiliotis ◽

Ioannis Massas ◽

...

Keyword(s):

Soil Salinity ◽

Soil Salinization ◽

Soil Samples ◽

Ion Concentrations ◽

Fine Soil ◽

Energy Demands ◽

Linear Relationships ◽

Reliable Monitoring ◽

Highly Correlated

Soil salinization is directly related to the quantity and quality of food production, and often, to increased energy demands for high-quality irrigation water. Reliable monitoring of soil salinity based on a less laborious method than the soil saturated paste (SP) extract methodology is required. In the present study, an attempt is made to relate the electrical conductivity (EC) of the soil saturated paste (SP) extract (ECe) with the EC determined in the 1:1 and 1:5 soil over water mass ratios, (soil:water) extracts (EC1:1 and EC1:5). ECe, EC1:1, and EC1:5 values were obtained for 198 soil samples from five different locations in Greece. The results have shown that strong linear relationships exist between the ECe and the EC1:1 and EC1:5 values (R2 > 0.93), and that the slopes of these linear relationships decreased from coarse to fine soil types. For 123 soil samples, the concentrations of Κ+, Νa+, Ca2+, Mg2+, and Cl− were also determined in the extracts of the three applied methodologies. Ion concentrations in the 1:1 and 1:5 extracts were highly correlated with the respective ion concentrations in the SP extracts. These findings strongly suggest that EC1:1 and EC1:5 values can be safely used for the estimation of ECe.

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Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling

Water ◽

10.3390/w12030880 ◽

2020 ◽

Vol 12 (3) ◽

pp. 880 ◽

Cited By ~ 1

Author(s):

Nijat Kasim ◽

Balati Maihemuti ◽

Rukeya Sawut ◽

Abdugheni Abliz ◽

Cui Dong ◽

...

Keyword(s):

Soil Salinity ◽

Quantitative Estimation ◽

Three Dimensional ◽

Ground Truth ◽

Monitoring And Evaluation ◽

Soil Salinization ◽

Least Squares Regression ◽

First Derivative ◽

Major Factors ◽

Mean Square Errors

Soil salinity is one of the major factors causing land degradation and desertification on earth, especially its important damage to farming activities and land-use management in arid and semiarid regions. The salt-affected land is predominant in the Keriya River area of Northwestern China. Then, there is an urgent need for rapid, accurate, and economical monitoring in the salt-affected land. In this study, we used the electrical conductivity (EC) of 353 ground-truth measurements and predictive capability parameters of WorldView-2 (WV-2), such as satellite band reflectance and newly optimum spectral indices (OSI) based on two dimensional and three-dimensional data. The features of spectral bands were extracted and tested, and different new OSI and soil salinity indices using reflectance of wavebands were built, in which spectral data was pre-processed (based on First Derivative (R-FD), Second Derivative (R-SD), Square data (R-SQ), Reciprocal inverse (1/R), and Reciprocal First Derivative (1/R-FD)), utilizing the partial least-squares regression (PLSR) method to construct estimation models and mapping the regional soil-affected land. The results of this study are the following: (a) the new OSI had a higher relevance to EC than one-dimensional data, and (b) the cross-validation of established PLSR models indicated that the β-PLSR model based on the optimal three-band index with different process algorithm performed the best result with R2V = 0.79, Root Mean Square Errors (RMSEV) = 1.51 dS·m−1, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions.

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Monitoring soil salinity using time-lapse electromagnetic conductivity imaging

10.5194/soil-2019-99 ◽

2020 ◽

Author(s):

Maria Catarina Paz ◽

Mohammad Farzamian ◽

Ana Marta Paz ◽

Nádia Luísa Castanheira ◽

Maria Conceição Gonçalves ◽

...

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Management Strategies ◽

Time Lapse ◽

Soil Salinization ◽

Spatiotemporal Variability ◽

Coefficient Of Determination ◽

Agricultural System ◽

Soil Electrical Conductivity ◽

Salinity Levels

Abstract. Lezíria Grande of Vila Franca de Xira, located in Portugal, is an important agricultural system where soil faces the risk of salinization, being thus prone to desertification and land abandonment. Soil salinity can be assessed over large areas by the following rationale: (1) use of electromagnetic induction (EMI) to measure the soil apparent electrical conductivity (ECa, dS m−1); (2) inversion of ECa to obtain electromagnetic conductivity images (EMCI) which provide the spatial distribution of the soil electrical conductivity (σ, mS m−1); (3) calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (ECe, dS m−1), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity maps using the obtained calibration equation. In this study, EMI surveys and soil sampling were carried out between May 2017 and October 2018 at four locations with different salinity levels across the study area of Lezíria de Vila Franca. A previously developed regional calibration was used for predicting ECe from EMCI. This study aims to evaluate the potential of time-lapse EMCI and the regional calibration to predict the spatiotemporal variability of soil salinity in the study area. The results showed that ECe was satisfactorily predicted, with a root mean square error (RMSE) of 3.22 dS m−1 in a range of 52.35 dS m−1 and a coefficient of determination (R2) of 0.89. Results also showed strong concordance with a Lin’s concordance correlation coefficient (CCC) of 0.93, although, ECe was slightly overestimated with a mean error (ME) of −1.30 dS m−1. Soil salinity maps for each location revealed salinity fluctuations related to the input of salts and water either through irrigation, precipitation or groundwater level and salinity. Time-lapse EMCI has proven to be a valid methodology for evaluating the risk of soil salinization, and can further support the evaluation and adoption of proper agricultural management strategies, especially in irrigated areas, where continuous monitoring of soil salinity dynamics is required.

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Assessment of soil salinity indexes using electrical conductivity sensors

Scientia Horticulturae ◽

10.1016/j.scienta.2021.110171 ◽

2021 ◽

Vol 285 ◽

pp. 110171

Author(s):

Sebastián Bañón ◽

Sara Álvarez ◽

Daniel Bañón ◽

María Fernanda Ortuño ◽

María Jesús Sánchez-Blanco

Keyword(s):

Electrical Conductivity ◽

Soil Salinity

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