Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey

Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam's Mekong River Delta

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam's Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R² value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R² of 0.99 and a value of RMSE of 0.051, by XGR-GOA (R² = 0.931, RMSE = 0.055), XGR-MSA (R² = 0.928, RMSE = 0.06), XGR-BSA (R² = 0.926, RMSE = 0.062), XGR-SSA (R² = 0.917, 0.07), XGR-PSO (R² = 0.916, RMSE = 0.08), XGR (R² = 0.867, RMSE = 0.1), CatBoost (R² = 0.78, RMSE = 0.12), and RF (R² = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security.

Estimation of soil salt content in the Bosten Lake watershed, Northwest China based on a support vector machine model and optimal spectral indices

Low-cost and efficient dynamic monitoring of surface salinization information is critical in arid and semi-arid regions, we conducted a remote sensing inversion exercise for soil salinity in the Bosten Lake watershed in Xinjiang, Northwest China, with a total area of about 43,930 km2, a typical watershed in an arid area. Sentinel MSI and Landsat OLI data were combined with measured soil salinity data in July 2020, and optimal combination bands were selected based on characteristic bands to create a grid search-support vector machine (GS-SVM) inversion model of soil salt content. The maximum value of soil salt content in the Bosten Lake watershed was 11.8 g/kg. The minimum value was 0.41 g/kg, and the average value was 4.77 g/kg, soil salinization is serious. The results of previous studies were applied to the estimation of salt content in Bosten Lake watershed and could not meet the monitoring requirements of the study area, R2 < 0.3. The GS-SVM soil salinity monitoring model was established based on the optimal DI, RI, and NDI remote sensing indexes for the Bosten Lake watershed. After model verification, it was found that the optimal model of image data was the Landsat OLI first-derivative model with R2 of 0.64, RMSE of 3.12, and RPD of 1.64, indicating that the prediction ability of the model was high. We used the first-order derivative model of Landsat OLI data to map the soil salt content in the Bosten Lake watershed in arid area, and found that soil salt content in most of the study area was between 10 and 20 g/kg, indicating severe salinization. This study not only reveals the distribution characteristics of salinization in Bosten Lake watershed, but also provides a scientific basis for soil salinization monitoring in Central Asia to lay a foundation for further soil salinization monitoring in arid areas.

Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques

Assessment of salt-affected land (SAL) is still a major challenging task worldwide, especially in developing nations. The advancement of remotely sensed digital satellite images of different spectral bands has enabled the assessment of soil salinity. Sentinel-2 and Landsat 8 and 5 images of 2020, 2015 and 2009 and Shuttle Radar Topographical Mission data of 2014 were obtained from the Google Earth Engine data catalogue. Twenty spectral indices have been used which include four vegetation indices, twelve soil salinity indices, four topographical characteristics and their spectral bands. The Random Forest model was used to detect SAL. A total of 593 soil samples were used in the model. Of the electrical conductivity values of samples collected in the field, 70% of the soil samples were used for the model training, and the remaining 30% were used for validation. Also, fivefold cross-validation was carried out to validate the model prediction. The predicted SAL extent identified during 2020 was 134.4 sq. km with an overall accuracy of 93% using fivefold cross-validation. In 2015 and 2009, the total SAL was 128.42 and 120.41 sq. km, respectively. The total SAL has increased by 11.6% during the study period. The present study demonstrated the strength of remote sensing techniques to assess the SAL, which will help quantify the unproductive lands at the state or national level for reclamation or other productive use.

Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery

Salinity systems are well known as extreme environmental systems that occur either naturally or by certain human activities, in arid and semiarid regions, which may harm crop production. Soil salinity identification is essential for soil management and reclamation projects. Information derived from space data acquisition systems (e.g., Landsat, ASTER) is considered as one of the most rapid techniques in mapping Salt-Affected Soil (SAfSoil). The current study tested the previously proposed salinity indices on the northern Nile Delta region, Egypt. The results indicated that most of the indices were not suitable to detect the SAfSoil in the area, due to the interaction between the bare soils, salts, and urbanization. To resolve this issue, the current work suggested a new index for detecting and monitoring the SAfSoil in the Nile Delta region. The newly proposed index takes into consideration plant health, the salt crust at the surface of the soils, as well as urbanization. It facilitates the mapping processes of SAfSoil in the area compared to any other previously proposed index. In this respect, multi-temporal Landsat-7 and 8 satellite data, acquired in 2002, 2016, and 2021, were used. The new index was prepared using the 2002 data and verified using the 2016 and 2021 data. Field measurements and data collected during 2002, 2016, and 2021 were utilized as ground truth data to assess the accuracy of the results obtained from the proposed index. The evaluation of the results indicated that the accuracy assessment for 2002, 2016, and 2021 images was 94.58, 96.08, and 95.68%, respectively. Finally, the effectiveness of using remote sensing in detecting and mapping SAfSoil is outlined.