Study on Soil Salinization Based on GIS and RS

2013 ◽  
Vol 333-335 ◽  
pp. 81-85 ◽  
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.

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

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.

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

Soil Research ◽  
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.

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

2020 ◽  
Vol 6 (4) ◽  
pp. 2487-2493 ◽  
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.

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

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.

scholarly journals Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 4825
Author(s):  
Salman Naimi ◽  
Shamsollah Ayoubi ◽  
Mojtaba Zeraatpisheh ◽  
Jose Alexandre Melo Dematte
Keyword(s):  
Machine Learning ◽  
Soil Salinity ◽  
Crop Production ◽  
Soil Salinization ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Agricultural Activity ◽  
Ensemble Modeling ◽  
Least Squares Regression ◽  
Environmental Covariates

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.

SALINITY AND ALFALFA YIELD UNDER EFFLUENT IRRIGATION IN SOUTHWESTERN SASKATCHEWAN

1984 ◽  
Vol 64 (3) ◽  
pp. 323-332 ◽  
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

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

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 880 ◽  
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.

scholarly journals Linking Long-Term Changes in Soil Salinity to Paddy Land Abandonment in Jaffna Peninsula, Sri Lanka

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 211
Author(s):  
Tharani Gopalakrishnan ◽  
Lalit Kumar
Keyword(s):  
Sri Lanka ◽  
Soil Salinity ◽  
Agricultural Land ◽  
Sea Water ◽  
Land Use Changes ◽  
Soil Salinization ◽  
Landsat Images ◽  
Paddy Land ◽  
Semi Arid Region

Soil salinity is a serious threat to coastal agriculture and has resulted in a significant reduction in agricultural output in many regions. Jaffna Peninsula, a semi-arid region located in the northern-most part of Sri Lanka, is also a victim of the adverse effects of coastal salinity. This study investigated long-term soil salinity changes and their link with agricultural land use changes, especially paddy land. Two Landsat images from 1988 and 2019 were used to map soil salinity distribution and changes. Another set of images was analyzed at four temporal periods to map abandoned paddy lands. A comparison of changes in soil salinity with abandoned paddy lands showed that abandoned paddy lands had significantly higher salinity than active paddy lands, confirming that increasing salts owing to the high levels of sea water intrusion in the soils, as well as higher water salinity in wells used for irrigation, could be the major drivers of degradation of paddy lands. The results also showed that there was a dramatic increase in soil salinity (1.4-fold) in the coastal lowlands of Jaffna Peninsula. 64.6% of the salinity-affected land was identified as being in the extreme saline category. In addition to reducing net arable lands, soil salinization has serious implications for food security and the livelihoods of farmers, potentially impacting the regional and national economy.

scholarly journals Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1535
Author(s):  
Tonggang Fu ◽  
Hui Gao ◽  
Jintong Liu
Keyword(s):  
Soil Salinity ◽  
Subsurface Layer ◽  
Salt Content ◽  
Spatial Prediction ◽  
Northern China ◽  
Sodium Adsorption Ratio ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Total Salt Content ◽  
Inverse Distance

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.

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