Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This research, conducted in Siwa Oasis, Egypt, aimed to assess the effectiveness of remote sensing techniques in detecting and monitoring changes in soil salinity. Using Landsat 5 and Landsat 7 satellite images, the researchers evaluated various soil salinity indices based on 56 on-site ground measurements. The study aimed to improve the correlation between electrical conductivity (EC) and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months. Different approaches were employed, including stacking the data, categorizing EC measurements into salinity levels, analyzing data temporally, and conducting spatial correlation analysis. The initial approach revealed a weak correlation, due to substantial variation in EC values. However, the salinity index SI demonstrated the highest correlation coefficient of 0.38. In the second scenario, the salinity index 2 S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the salinity index 1S1 achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds, with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients than those of both the first and fourth scenarios. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This investigation, carried out in Siwa Oasis, Egypt, aimed to assess the efficacy of several soil salinity indices derived from Landsat 5 and 7 satellite images. To achieve this, 56 on-site ground measurements were utilized for evaluation purposes. The study aimed to improve the correlation between EC and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months from August to December. The first approach involved stacking the data to identify the index with the strongest correlation with ground EC values, without the need for complex analysis or EC value ranging. In the second approach, the ground EC measurements were classified into seven different salinity categories, and the correlation coefficient was calculated between each index and each salinity category. The third approach analyzed the data temporally, considering different salinity levels. Lastly, a spatial correlation analysis was conducted between EC and index values in the fourth scenario. The initial approach revealed a weak correlation due to substantial variation in EC values. However, the SI index demonstrated the highest correlation coefficient of 0.38. In the second scenario, the S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the S1 index achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients compared to both the first and fourth scenarios. The study highlighted significant variations in EC levels related to land cover types and pond elevation. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

Soil salinization induced by shallow saline groundwater in coastal areas can be managed using subsurface pipe drainage (SPD) for agricultural land reclamation. However, a reasonable SPD system layout should comprehensively consider local hydrological conditions and crop physiological characteristics based on long-term model evaluations. The objectives of this study were to test the applicability of a crop growth model (AquaCrop) for simulating winter wheat growth in SPD-applied fields by employing the water table behaviors predicted by the soil hydrologic model HYDRUS. Model calibration and validation based on field observations suggested that HYDRUS accurately predicted the distributions of soil water–salt dynamics, and the seasonal variations of canopy cover and biomass production predicted by AquaCrop were close to the measured values. The simulation scenarios considering the long-term effect of groundwater salinity (10.53, 21.06, and 31.59 g L⁻¹ for low, medium, and high levels), drain spacing (10, 20, 30, 40 m, and no-SPD), and precipitation category (dry, normal, and wet year) on soil solute transport, grain yield (GY), water productivity (WP), and groundwater supply (GS) were further explored using a combination of HYDRUS and AquaCrop. The simulation results indicated that narrowing the drain spacing could improve the desalination performance of SPD, but there was no continuous downward trend of soil solute concentration during the long-term application of SPD when groundwater salinity was constant. The SPD application could improve grain yield by 0.81–1.65 t ha⁻¹, water productivity by 0.13–0.35 kg m⁻³, and groundwater supply by 6.06–31.03 mm compared to the no-SPD scenarios, but such increases would be less pronounced in dry years with groundwater salinity at the low level. This study demonstrated that the co-application of hydrologic and crop growth models is a feasible method for revealing the effects of SPD on agricultural land reclamation in coastal areas.