Taher M. H. Yossif

Accurate prediction and mapping of soil properties are essential in environmentally sustainable land use. Traditional approaches are laborious and costly, however. This article demonstrates the use of Prismatic Imaging Sensor (PRISMA) with a cost-effective, rapid, and environmental-friendly approach of forecasting and mapping the soil pH, electrical conductivity (EC), and calcium carbonate (CaCO₃) of New Delta region in Egypt’s Western Desert based on hyperspectral data and machine learning (ML) techniques. The study integrates soil wet chemistry data, hyperspectral reflectance, multivariate regression, and ML models to improve soil property estimation. PRISMA hyperspectral imagery was acquired, processed, and classified to map land use and land cover (LULC). Seventy-four representative bare soil profiles were collected and their pH, EC, and CaCO₃ content were determined. Hyperspectral reflectance data for the samples were reaped, and noisy spectral bands were eliminated to improve data quality and prediction accuracy. Partial least squares regression (PLSR), random forest (RF), multivariate adaptive regression splines (MARS), and support vector regression (SVR) were the different ML models attempted. Spectral band selection was improved by the implementation of competitive adaptive reweighted sampling (CARS); and multiple linear regression (MLR) was used for the development of prediction equations to map soil properties over PRISMA image. Soil condition was found highly variable, ranging from slightly to extremely alkaline pH, non-calcareous to extremely calcareous in CaCO₃ content, and non-saline to highly saline soils. Of the models, PLSR provided the best fit estimates for surface soil pH (R² = 0.1186, RMSE = 3.115, RPD = 0.7902), surface soil EC (R² = 0.2281, RMSE = 1.196 dS/m, RPD = 1.132), surface CaCO₃ content (R² = 0.5984, RMSE = 2.73%, RPD = 1.817), subsurface soil EC (R² = 0.2557, RMSE = 1.7481 dS/m, RPD = 1.124), and subsurface CaCO₃ content (R² = 0.6092, RMSE = 2.32%, RPD = 1.779). RF yielded the highest performance for predicting subsurface soil pH (R² = 0.1517, RMSE = 2.876, RPD = 0.1777). Identification of relevant spectral bands, calibration of the prediction models, and their use in PRISMA imagery resulted in high-resolution maps of soil parameters. These findings are extremely helpful to improve land reclamation planning and the effectiveness of their application.

Ctivity Dynamics (LPD) in Egypt, during the 2000 - 2019 period as well as identifying drivers and hotspots of land degradation aiming at realizing the promotion of the land’s sustainable development and degradation neutrality. The study is based on remotely sensed images (SPOT Vegetation NDVI) aggregated / composited to observation every 10 days at a spatial resolution of 30 m calculated by the Joint Research Centre. Results showed that trends of the net LPD for each of the degraded land cover categories were 0.4%, accounting for about 11415 sq. km (1.14% of Egypt’s land area). On the other hand, the increasing trend of LP has been attained for both of the shrubs and grassland together with croplands, accounting for around 2077 and 10921 sq. km respectively representing together about 1.3 % of the country’s land area. During the study period, mismanagement of soils and water resources has been the chief reasons for the changes in LP. Worthy mentioning, that RS, NDVI and GIS are considered essential tools for LPD monitoring and assessment.