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

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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A simple approach to estimate coastal soil salinity using digital camera images

Soil Research ◽

10.1071/sr20009 ◽

2020 ◽

Vol 58 (8) ◽

pp. 737

Author(s):

Lu Xu ◽

Raphael A. Viscarra Rossel ◽

Juhwan Lee ◽

Zhichun Wang ◽

Hongyuan Ma

Keyword(s):

Soil Salinity ◽

Precision Agriculture ◽

Mean Squared Error ◽

Rapid Development ◽

Salt Content ◽

Digital Camera ◽

Soil Surface ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

High Concentration

Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and sampled the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.

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Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia

Modeling Earth Systems and Environment ◽

10.1007/s40808-020-00829-3 ◽

2020 ◽

Vol 6 (4) ◽

pp. 2487-2493 ◽

Cited By ~ 2

Author(s):

Hazem T. Abd El-Hamid ◽

Guan Hong

Keyword(s):

Remote Sensing ◽

Spectral Data ◽

Vegetation Index ◽

Spectral Characteristics ◽

Salt Content ◽

Soil Salinization ◽

Northern Region ◽

Semiarid Region ◽

First Order ◽

Remote Sensing Monitoring

Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.

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Evaluation of Soil Degradation Based on High Resolution Remote Sensing Data

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/16/3303 ◽

2005 ◽

pp. 145-148

Author(s):

Péter Burai ◽

János Tamás

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Soil Salinity ◽

Strong Correlation ◽

Soil Degradation ◽

Vegetation Index ◽

Near Infrared ◽

Salt Content ◽

Remote Sensing Data ◽

Test Site

Soil salinity is the main problem of soil degradation in the Grate Plain with cultivated area of 20% affected. Its influence is accelerated on the water managed and irrigated lands. Remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. We have chosen a farm where intensive crop cultivation takes place as a test site as soil degradation can be intensive as a result of land use and irrigation. In order to evaluate soil salt content and biomass analysis, we gathered detailed data from an 100x250 m area. We analyzed the salinity property of the samples. In our research we used a TETRACAM ADC multispectral camera to take high resolution images (0,2-0,5 m) of low altitude (300-500 m). A Normalized Vegetation Index was computed from near infrared (750-950 nm) and red (620-750 nm) bands. This data was compared with the samples of investigated area. Analyzing the images, we evaluated image reliability, and the connection between the bands and the soil properties (pH, salt content). A strong correlation observed between NDVI and soil salinity (EC) makes the multispectral images suitable for construction of salinity map. A further strong correlation was determined between NDVI and yield.

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A PLSR model to predict soil salinity using Sentinel-2 MSI data

Open Geosciences ◽

10.1515/geo-2020-0286 ◽

2021 ◽

Vol 13 (1) ◽

pp. 977-987

Author(s):

Ghada Sahbeni

Keyword(s):

Soil Salinity ◽

Spectral Characteristics ◽

Management Strategies ◽

Salt Content ◽

Soil Surface ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Remotely Sensed Data ◽

Sampling Campaign ◽

Sentinel 2

Abstract Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R 2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.

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SALINITY AND ALFALFA YIELD UNDER EFFLUENT IRRIGATION IN SOUTHWESTERN SASKATCHEWAN

Canadian Journal of Soil Science ◽

10.4141/cjss84-034 ◽

1984 ◽

Vol 64 (3) ◽

pp. 323-332 ◽

Cited By ~ 5

Author(s):

Y. W. JAME ◽

V. O. BIEDERBECK ◽

W. NICHOLAICHUK ◽

H. C. KORVEN

Keyword(s):

Steady State ◽

Soil Salinity ◽

Root Zone ◽

Salt Content ◽

High Water ◽

Soil Salinization ◽

Wastewater Irrigation ◽

State Condition ◽

Steady State Condition ◽

Effluent Irrigation

The effect of wastewater irrigation on soil salinity and crop yield was determined in a study at Swift Current, Saskatchewan. A toposequence consisting of the Orthic Regosol series, Calcareous Brown series, Orthic Brown series and Cumulic Orthic Brown series of the Birsay Association was seeded to alfalfa and irrigated with effluent from a secondary sewage lagoon. Since the applied effluent had a mean EC of 2.6 dS/m, soil salinization was a major concern. For 8 yr the four soils were sampled for salinity to a depth of 150 cm each fall. During the 8-yr period, alfalfa was sampled for dry matter yield twice each year from each soil. The results indicate that irrigation by 10–15% more than the normal recommended application rate (i.e., when low saline surface water is used for irrigation) will ensure sufficient leaching to maintain salt content in the root zone at a level not deleterious to plant growth. After 8 yr of effluent irrigation, new steady state salinity profiles were developed in the Orthic Regosol, Calcareous Brown and Orthic Brown series. At this steady state condition the salt contents in the upper 60 cm of the root zone in these three soil series were generally similar. They increased from the initial low ECe value of 0.6 dS/m to 2.5 dS/m. Salinity increased with depth toward the bottom of the root zone where the Orthic Brown series had an ECe value of 4.0 dS/m, while the Orthic Regosol and Calcareous Brown series had values of 6.0 dS/m. Effluent irrigation resulted in a small net reduction of salts in the 150-cm profile of the Orthic Regosol and the Calcareous Brown series, but caused an increase of salts in the Orthic Brown profile. In the Cumulic Orthic Brown series the salt content throughout the profile increased continually from a very low initial level, and had not reached a steady state condition after 8 yr of irrigation. The development of a high water table in this area resulted in salt movement into the root zone of the Cumulic Orthic Brown series that was distinctly higher than those of the other three series and caused alfalfa yields to decline from being highest at the start of this study to only about 80% of yields on the Orthic Brown series at the end. The observed yield reductions emphasize the importance of having adequate drainage to effect salt removal by leaching when crops are irrigated with saline sewage effluent. Key words: Wastewater irrigation, soil salinity, alfalfa yield

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Study on Soil Salinization Based on GIS and RS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.81 ◽

2013 ◽

Vol 333-335 ◽

pp. 81-85 ◽

Cited By ~ 1

Author(s):

Chi Ma

Keyword(s):

Soil Salinity ◽

Research Result ◽

Atmospheric Correction ◽

Salt Content ◽

Soil Salinization ◽

Least Squares Regression ◽

Correction Model ◽

First Order ◽

Gis And Rs ◽

Songliao Plain

This paper obtained the surface reflectance image by using FLAASH atmospheric correction model to conduct atmospheric correction to the HSI image. After the reflectance image was processed with multi mathematical manipulations, the analytical method of geographic information system (GIS) was applied to carry out the partial least squares regression (PLSR) analysis with the measured value of soil salinity, thus quantitatively inverting the soil salinity in Songliao Plain. The research result indicated that the reflectance of HSI hyperspectral imaging can increase the related coefficient R2 of salt content in saline alkali soil significantly after being processed with reciprocal (1/R) and first-order differentiation (R), reaching 0.818 and 0.851 respectively with a root mean square error (RMSE) of 0.77 and 0.694 respectively, quantitatively and quickly gaining the soil salinity in Songliao Plain as well as salinization prevention.

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Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-08958-7 ◽

2021 ◽

Vol 193 (5) ◽

Author(s):

Sophie Thiam ◽

Grace B. Villamor ◽

Laurice C. Faye ◽

Jean Henri Bienvenue Sène ◽

Badabate Diwediga ◽

...

Keyword(s):

Land Use ◽

Soil Salinity ◽

Land Use Planning ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Salt Content ◽

Salinity Changes ◽

Salinity Management ◽

Gains And Losses

AbstractSoil salinity is a major issue causing land degradation in coastal areas. In this study, we assessed the land use and soil salinity changes in Djilor district (Senegal) using remote sensing and field data. We performed land use land cover changes for the years 1984, 1994, 2007, and 2017. Electrical conductivity was measured from 300 soil samples collected at the study area; this, together with elevation, distance to river, Normalized Difference Vegetation Index (NDVI), Salinity Index (SI), and Soil-Adjusted Vegetation Index (SAVI), was used to build the salinity model using a multiple regression analysis. Supervised classification and intensity analysis were applied to determine the annual change area and the variation of gains and losses. The results showed that croplands recorded the highest gain (17%) throughout the period 1984–2017, while forest recorded 3%. The fastest annual area of change occurred during the period 1984–1994. The salinity model showed a high potential for mapping saline areas (R2 = 0.73 and RMSE = 0.68). Regarding salinity change, the slightly saline areas (2 < EC < 4 dS/m) increased by 42% whereas highly saline (EC > 8 dS/m) and moderately saline (4 < EC < 8 dS/m) areas decreased by 23% and 26%, respectively, in 2017. Additionally, the increasing salt content is less dominant in vegetated areas compared with non-vegetated areas. Nonetheless, the highly concentrated salty areas can be restored using salt-resistant plants (e.g., Eucalyptus sp., Tamarix sp.). This study gives more insights on land use planning and salinity management for improving farmers’ resilience in coastal regions.

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Spatiotemporal Changes of Soil Salinization in the Yellow River Delta of China from 2015 to 2019

Sustainability ◽

10.3390/su13020822 ◽

2021 ◽

Vol 13 (2) ◽

pp. 822

Author(s):

Lingling Bian ◽

Juanle Wang ◽

Jing Liu ◽

Baomin Han

Keyword(s):

Vegetation Index ◽

Yellow River ◽

Salt Content ◽

Feature Space ◽

Yellow River Delta ◽

Soil Salinization ◽

River Delta ◽

The Yellow River ◽

The Yellow River Delta ◽

Soil Salt Content

Soil salinization poses a significant challenge for achieving sustainable utilization of land resources, especially in coastal, arid, and semi-arid areas. Timely monitoring of soil salt content and its spatial distribution is conducive to secure efficient agricultural development in these regions. In this study, to address the persistent problem of soil salinization in the Yellow River Delta in China, the feature space method was used to construct multiple feature spaces of surface albedo (Albedo)–modified soil-adjusted vegetation index (MSAVI), salinity index (SI)–Albedo, and SI–normalized difference vegetation index (NDVI), and an optimal inversion model of soil salinity was developed. Based on Landsat 8 Operational Land Imager (OLI) image data and simultaneous field-measured sampling data, an optimal model from 2015 to 2019 was used to obtain the soil salt content in the region at a 30 m resolution. The results show that the proportion of soil salinization in 2015 and 2019 was approximately 76% and 70%, respectively, and overall soil salinization showed a downward trend. The salinization-mitigated areas are primarily distributed in the southwest of the Yellow River Delta, and the aggravated areas are distributed in the northeast and southeast. In general, the spatial variation characteristics show an increasing trend from the southwest to the eastern coastal areas, corresponding to the formation mechanism of salt accumulation in the region. Further, corresponding sustainable development countermeasures and suggestions were proposed for different salinity levels. Meanwhile, this study revealed that the SI–Albedo feature space model is the most suitable for inversion of salinization in coastal areas.

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Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Sensors ◽

10.3390/s22020546 ◽

2022 ◽

Vol 22 (2) ◽

pp. 546

Author(s):

Xinyang Yu ◽

Chunyan Chang ◽

Jiaxuan Song ◽

Yuping Zhuge ◽

Ailing Wang

Keyword(s):

Soil Salinity ◽

Yellow River ◽

Pearson Correlation ◽

Arable Land ◽

Salt Content ◽

Yellow River Delta ◽

River Delta ◽

Support Vector ◽

Optimal Method ◽

Sensitive Parameter

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

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Use of Landsat EVI in Determining the Degradation of Agroirriation Landscapes Adjacent to the Aydar-Arnasay Lakes System

10.21203/rs.3.rs-638840/v1 ◽

2021 ◽

Author(s):

Nilufar Sabirova ◽

Michael GROLL ◽

Subkhan ABBASOV

Keyword(s):

Water Level ◽

Vegetation Index ◽

Agricultural Land ◽

Arable Land ◽

Degradation Process ◽

Soil Salinization ◽

Drainage Water ◽

Landsat 8 ◽

Lake System ◽

Tier 1

Abstract The Arnasay depression in Central Uzbekistan received large quantities of drainage water leading to the formation of the Aydarkul-Arnasay Lake System (AALS). The water level of the AALS drastically increased in 1969, when a flood in the nearby Syrdarya River basin could not be contained in the Chardarya reservoir, and today it occupies an area of 4000 km² of the Mirzachul and Kyzylkum desert. Increasing the lake’s water level also affects the surrounding agricultural land, further enhancing the level of groundwater and soil salinization. But the irrigated farming areas also influence the lake system due to the pollution of the drainage water discharged into the lake. As a result, both the arable land and the lake system are in a process of degradation, leading to reduced productivity and a variety of ecological problems. We used more of the remote sensing method in determining the degradation process in agroirrigation landscapes. Landsat EVI (Enhanced vegetation index) extremely resistant to various atmospheric resistances (aerosols). It monitors plants with very high sensitivity even in low biomass areas. Landsat has 4,5,7,8 series programs. Herein, we used Landsat-5 TM Collection 1 Tier 1 32-Day EVI and Landsat-8 ETM + Collection 1 Tier 1 32-Day EVI. To classify the degradation process in agroirrigation landscapes around the lake, we compared Landsat EVI images from March-April, May-June, June-July, Ilyul-August, August-September. We selected July-August as the optimal month to determine the perennial degradation process.

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