The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating soil salinity. Although the long-standing laboratory method of measuring salinity composition is accurate for determining soil salinity parameters, its use is frequently constrained by the high expense and difficulty of long-term in situ measurement. Soil salinity in the northern Nile Delta of Egypt severely affects agriculture sustainability and food security in Egypt. Understanding the spatial distribution of soil salinity is a critical factor for agricultural development and management in drylands. This research aims to improve soil salinity prediction by using a combined data collection method consisting of Sentinel-1 C radar data and Sentinel-2 optical data acquired simultaneously via integrated radar and optical sensor variables. The modelling approach focuses on feature selection strategies and regression learning. Feature selection approaches that include the filter, wrapper, and embedded methods were used with 47 selected variables depending on a genetic algorithm to scrutinize whether regions of the spectrum from optical indices and SAR texture choose the optimum combinations of selected variables. The sub-setting variables resulting from each feature selection method were used to train the regression learners’ random forest (RF), linear regression (LR), backpropagation neural network (BPNN), and support vector regression (SVR). Combining the BPNN feature selection method with the RF regression learner better predicted soil salinity (RME 0.000246; sub-setting variables = 18). Integrating different remote sensing data and machine learning provides an opportunity to develop a robust prediction approach to predict soil salinity in drylands. This research evaluated the performances of various machine learning models, overcame the limitations of conventional techniques, and optimized the variable input combinations. This research can assist farmers in soil-salinization-affected areas in better managing planting procedures and enhancing the sustainability of their lands.

Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO₃). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.

The presence of a noticeable rate of degradation in the land of the Nile Delta reduces the efficiency of crop production and hinders supply of the increasing demand of its growing population. For this purpose, knowledge of soil resources and their agricultural potential is important for determining their proper use and appropriate management. Thus, we investigated the state of soil fertility by understanding the effect of the physical and chemical properties of the soil and their impact on the state of land degradation for the years 1985, 2002 (ancillary data), and 2021 (our investigation). The study showed that there are clear changes in the degree of soil salinity as a result of agricultural management, water conditions, and climatic changes. The soil fertility is obtained in four classes: Class one (I) represents soils of a good fertility level with an area of about 39%. Class two (II) includes soils of an average fertility level, on an area of about 7%. Class three (III) includes soils with a poor level of fertility, with an area of about 17%. Class four (IV) includes soils of a very poor level of fertility with an area of about 37% of the total area. Principal component analysis (PCA) has revealed that the parameters that control fertility in the studied soils are: C/N, pH, Ca, CEC, OM, P, and Mg. Agro-pedo-ecological units are important units for making appropriate agricultural decisions in the long term, which contribute to improving soil quality and thus increasing the efficiency of soil fertility processes.

Land evaluation is imperative for its efficient use in agriculture. Therefore, this study aimed at assessing the suitability of a region in West El-Minia for cultivating some of the major crops using the geographical information system (GIS). The results focus on allocating space for cultivating sugar beet and utilizing the free period of sugar beet in other crops. This exploitation helps to maintain the quality of the land and increase its fertility by using crop rotation with integrated agricultural management. A machine learning technique was implemented using the random forest algorithm (RF) to predict soil suitability classes for sugar beet using geomorphology, terrain attribute and remote sensing data. Fifteen major crops were evaluated using a suitability multicriteria approach in GIS environment for crop rotation decisions. Soil parameters were determined (soil depth, pH, texture, CaCO₃, drainage, ECe, and slope) to characterize the land units for soil suitability. Soils of the area were found to be Entisols; Typic Torrifluvents, Typic Torripsamments and Typic Torriorthents and Aridsols; Typic Haplocacids, Calcic Haplosalids and Sodic Haplocalcids. Overall, the studied area was classified into four suitability classes: high “S1”, moderate “S2”, marginal “S3”, and not suitable “N”. The area of each suitability class changed depending on the crop tested. The highest two crops that occupied S1 class were barley with 471.5 ha (representing 6.8% of the total study area) and alfalfa with 157.4 ha (2.3%). In addition, barley, sugar beet, and sorghum occupied the highest areas in S2 class with 6415.3 ha (92.5%), 6111.3 ha (88.11%) and 6111.3 ha (88.1%), respectively. Regarding the S3 class, three different crops (sesame, green pepper, and maize) were the most highly represented by 6151.8 ha (88.7%), 6126.3 ha (88.3%), and 6116.7 ha (88.2%), respectively. In the end, potato and beans occupied the highest areas in N class with 6916.9 ha (99.7%) and 6853.5 ha (98.8%), respectively. The results revealed that the integration of GIS and soil suitability system consists of an appropriate approach for the evaluation of suitable crop rotations for optimized land use planning and to prevent soil degradation. The study recommends using crop rotation, as it contributes to soil sustainability and the control of plant pests and diseases, where the succession of agricultural crops on a scientific basis aims at maintaining the balance of nutrients and fertilizers in the soil.

Soil survey system is the key word for sustainable soil use and management. This study aimed to generate a digital soil mapping (DSM) using various spatial resolutions of freely available digital elevation models (DEMs) i.e., (30 m ALOS “Advanced Land Observing Satellite”, 12.5 m ALOS, 30 m SRTM “Shuttle Radar Topography Mission”, and 90 m SRTM) to assess the spatial variability of soil physio-chemical characteristics in El-Beheira Province, Western Nile Delta. A total of 196 geo-referenced soil profiles were collected from agricultural fields, and the soil's contents of total nitrogen (TN), potassium (K), available phosphorous (P), calcium (Ca), sulfur (S), magnesium (Mg), zinc (Zn), copper (Cu), manganese (Mn), and boron (B) were analyzed. The study revealed that the soil's contents of TN, P, S, B, and Cu were low, and Mg was suspected to be higher than K, leading to an Mg-induced K shortage. The study also found wide-ranging spatial variability structures of inverse distance weight (IDW) interpolated maps for several soil physio-chemical characteristics. The DSM further revealed that poor status was present in 85.4% of TN, 91.7% of P, 76.9% of S, and 87% of both B and Cu. The conclusion is that soil variability, as demonstrated in DSM, outweighs uniform treatment, and this discovery will assist extension workers, scientists, and decision-makers in implementing site-specific nutrient management strategies. Validation of the DSM is recommended for crop and variety-specific nutrient treatment rates. Understanding the geographical variance in the soil is critical for long-term nutrient management and higher output.

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.