Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Soil organic carbon (SOC) is a crucial factor influencing soil quality and fertility. In this particular investigation, we aimed to explore the possibility of using diffuse reflectance infrared fourier transform spectroscopy (DRIFT-FTIR) in conjunction with machine-learning models, such as partial least squares regression (PLSR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF), to estimate SOC in Sohag, Egypt. To achieve this, we collected a total of ninety surface soil samples from various locations in Sohag and estimated the total organic carbon content using both the Walkley-Black method and DRIFT-FTIR spectroscopy. Subsequently, we used the spectral data to develop regression models using PLSR, ANN, SVR, and RF. To evaluate the performance of these models, we used several evaluation parameters, including root mean square error (RMSE), coefficient of determination (R²), and ratio of performance deviation (RPD). Our survey results revealed that the PLSR model had the most favorable performance, yielding an R² value of 0.82 and an RMSE of 0.006%. In contrast, the ANN, SVR, and RF models demonstrated moderate to poor performance, with R² values of 0.53, 0.27, and 0.18, respectively. Overall, our study highlights the potential of combining DRIFT-FTIR spectroscopy with multivariate analysis techniques to predict SOC in Sohag, Egypt. However, additional studies and research are needed to improve the accuracy or predictability of machine-learning models incorporated into DRIFT-FTIR analysis and to compare DRIFT-FTIR analysis techniques with conventional soil chemical measurements.

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.

Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil’s physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid regions (Wadi Elkobaneyya valley, located in the northwest of Aswan Governorate, Upper Egypt). The study objectives were to characterize and develop prediction models for soil salinity, pH and calcium carbonate (CaCO₃) using integration soil analysis and spectral reflectance vis-NIR spectroscopy. To achieve the study objectives, three multivariate regression models: Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and Least Square-Support Vector Regression (LS-SVR)); and two machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) were used. Ninety-six surface soil samples were collected from the study area at depths 0–5 cm. The data were divided into a calibration dataset (70% of the total) and a validation dataset (30% of the total dataset). The obtained results represent that the PLSR model was the best model for soil pH parameters where R² of calibration and validation predictability = 0.68 and 0.52, respectively. The LS-SVR model was the best model to predict soil Electrical Conductivity (EC) and soil Calcium Carbonate (CaCO₃) content, with R² 0.70 and 0.74 for calibration and R² 0.26 and 0.47 for validation, respectively. On the other hand, the results of the implemented machine learning algorithm model showed that RF was the best model to predict soil pH and CaCO₃, as the R² was 0.82 for calibration and 0.57 for validation, respectively. Nevertheless, the best model for predicting soil EC was ANN, with an R² of 0.96 for calibration and 64 for validation. The results show the advantages of machine learning models for predicting soil EC, pH and CaCO₃ by Vis-NIR spectroscopy. Therefore, Vis-NIR spectroscopy is considered faster and more cost-efficient and can be further used in environmental monitoring and precision farming.

Soils are one of the most valuable non-renewable natural resources, and conserving them is critical for agricultural development and ecological sustainability because they provide numerous ecosystem services. Soil erosion, a complex process caused by natural forces such as rainfall and wind, poses significant challenges to ecosystems, agriculture, infrastructure, and water quality, necessitating advanced monitoring and modeling techniques. It has become a global issue, threatening ecological systems and food security as a result of climatic changes and human activities. Traditional soil erosion field measurement methods have limitations in spatial and temporal coverage. The integration of new techniques such as remote sensing (RS), geographic information systems (GIS), and artificial intelligence (AI) has revolutionized our approach to understanding and managing soil erosion. RS technologies are widely applicable to soil erosion investigations due to their high efficiency, time savings, and comprehensiveness. In recent years, advancements in RS sensor technology and techniques have resulted in fine spatial-resolution images and increased the accuracy of soil erosion detection for spatial mapping purposes. Satellite imagery provides critical data on land cover and soil properties, whereas digital elevation models (DEMs) provide detailed elevation information required to assess slope and flow accumulation, which are important factors in erosion modeling. GIS enhances soil erosion analysis by integrating multiple spatial datasets, making it easier to identify erosion hot spots and utilizing models like the Revised Universal Soil Loss Equation (RUSLE) to estimate soil loss and guide land management decisions. Furthermore, AI techniques, particularly machine learning (ML) and deep learning (DL), significantly improve the accuracy of erosion predictions by analyzing historical data and extracting relevant features from RS imagery. These techniques use convolutional neural networks (CNNs) and data augmentation, as well as integrating spatial data from GIS to identify erosion hot spots and risk factors. Additionally, innovative methods, including biodegradable materials, hydroseeding, and autonomous vehicles for precision agriculture, are being developed to prevent and mitigate soil erosion effectively. Although specific case studies demonstrate the successful implementation of this integrated framework in a variety of landscapes, ongoing challenges such as data availability and model validation must be addressed. Ultimately, the collaboration of RS, GIS, and AI not only enhances our understanding of soil erosion but also paves the way for innovative and effective erosion control strategies, underscoring the importance of continued research in this vital area. This chapter addresses the basic concerns related to the application of RS technology in soil erosion: concepts, data acquisition, tools, data types, data quality, data management, data visualization, and challenges to provide an overview of this technology type and its role in soil erosion.