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Shokr, M. S., M. A. Abdellatif, A. A. El Baroudy, A. Elnashar, E. F. Ali, A. A. Belal, W. Attia, M. A. Ahmed, A. A., Z. Szantoi, et al., "Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions", Sustainability, vol. 13, issue 5, pp. 2893, 2021. AbstractWebsite

Food security has become a global concern for humanity with rapid population growth, requiring a sustainable assessment of natural resources. Soil is one of the most important sources that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km2 (22.7%), high-quality class occupies 441.72 km2 (25.87%), the moderate-quality class represents 208.57 km2 (12.21%), slightly moderate-quality class represents 231.10 km2 (13.5%), as well as, a low-quality class covering an area of 233 km2 (13.60%), and very low-quality class occupies about 206 km2 (12%). The Agricultural Land Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km2 (13.50%), 291.94 km2 (17%), 767.39 km2 (44.94%), and 416.07 km2 (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong significant positive correlation with the land capability and a positive correlation with NDVI at R2 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily be re-applied in similar environments, allowing decision-makers and regional governments to use the quantitative results achieved to ensure sustainable development.

Elnashar, A., H. Zeng, B. Wu, N. Zhang, F. Tian, M. Zhang, W. Zhu, N. Yan, Z. Chen, and Z. Sun, "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing", Remote Sensing, vol. 12, issue 23: Multidisciplinary Digital Publishing Institute, pp. 3860, 2020. AbstractWebsite

Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution

Zeng, H., B. Wu, M. Zhang, N. Zhang, A. Elnashar, L. Zhu, W. Zhu, F. Wu, N. Yan, and W. Liu, Dryland ecosystem dynamic change and its drivers in Mediterranean region, , vol. 48: Elsevier, pp. 59 - 67, 2020. AbstractWebsite

This review describes the latest progress of dryland ecosystem dynamic change in the Mediterranean region. Recent findings indicate that extent of dryland in the Mediterranean region has been expanding in the past decades and will continue to expand in the coming decades due to the stronger warming effect than other regions. The warming trend with intensified human activities has generated a series of negative impacts on productivity, biodiversity, and stability of the dryland ecosystem in Mediterranean region. Increased population, overgrazing and, grazing abandonment intensified the land degradation and desertification. The coverage, richness, and abundance of biological soil crust have been reduced due to the decline of soil water availability and increased animals. Future studies are required to further our understanding of the process and mechanism of the dryland dynamics, including the identification of essential variables, discriminating human and climate-induced changes, and modeling future trajectories of dryland changes.

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