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In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation's indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]