Elnashar, A., H. Zeng, B. Wu, A. A. Fenta, M. Nabil, and R. Duerler, "Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework", Science of The Total Environment, pp. 148466, 2021. AbstractWebsite

Assessment of soil loss and understanding its major drivers are essential to implement targeted management interventions. We have proposed and developed a Revised Universal Soil Loss Equation framework fully implemented in the Google Earth Engine cloud platform (RUSLE-GEE) for high spatial resolution (90 m) soil erosion assessment. Using RUSLE-GEE, we analyzed the soil loss rate for different erosion levels, land cover types, and slopes in the Blue Nile Basin. The results showed that the mean soil loss rate is 39.73, 57.98, and 6.40 t ha−1 yr−1 for the entire Blue Nile, Upper Blue Nile, and Lower Blue Nile Basins, respectively. Our results also indicated that soil protection measures should be implemented in approximately 27% of the Blue Nile Basin, as these areas face a moderate to high risk of erosion (>10 t ha−1 yr−1). In addition, downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km spatial resolution significantly impacts rainfall erosivity and soil loss rate. In terms of soil erosion assessment, the study showed the rapid characterization of soil loss rates that could be used to prioritize erosion mitigation plans to support sustainable land resources and tackle land degradation in the Blue Nile Basin.

Wang, L., B. Wu, A. Elnashar, H. Zeng, W. Zhu, and N. Yan, "Synthesizing a Regional Territorial Evapotranspiration Dataset for Northern China", Remote Sensing, vol. 13, issue 6, pp. 1076, 2021. AbstractWebsite

As a vital role in the processes of the energy balance and hydrological cycles, actual evapotranspiration (ET) is relevant to many agricultural, ecological and water resource management studies. The available global or regional ET products provide ET estimations with various temporal ranges, spatial resolutions and calculation methods (algorithms, inputs and parameterization, etc.), leading to varying degrees of introduced uncertainty. Northern China is the main agriculturally productive region supporting the whole country; thus, understanding the spatial and temporal changes in ET is essential to ensure water resource and food security. We developed a synthesis ET dataset for Northern China at a 1000 m spatial resolution, with a monthly temporal resolution covering a period ranging from 1982 to 2017, using an in-depth assessment of several ET products. Specifically, assessments were performed using in situ measured ET from eddy covariance (EC) observation towers at the site-pixel scale over interannual months under the conditions of different land cover types, climatic zones and elevation levels to select the most optimally performing ET products to be used in the synthesized ET dataset. Eight indicators under 21 conditions were involved in the assessment sheet, while the statistics of the different ET product occurrences and corresponding ratios were analyzed to select the best-performing ET products to build the synthesis ET dataset using the weighted mean method. The weights were determined by the Taylor skill score (TSS), calculated with ET products and EC ET observation data. Based on the assessment results, the Penman–Monteith–Leuning (PML_v2), ETWatch and Operational Simplified Surface Energy Balance (SSEBop) datasets were selected for implementation in the synthesis ET dataset from 2003 to 2017, while Global Land Evaporation Amsterdam Model (GLEAM) v3.3a, complementary relationship (CR) ET, and Numerical Terradynamic Simulation Group (NTSG) datasets were chosen for the synthesis ET dataset from 1982 to 2002. The weighted mean synthesized results from 2003 to 2017 performed well when compared to the in situ measured EC ET values produced under all of the above conditions, while the synthesized results from 1982 to 2002 performed well through the water balance method in Heihe River Basin. These results can provide more stable ET estimations for Northern China, which can contribute to relevant agricultural, ecological and hydrological studies.

Kheir, A. M. S., Z. Ding, T. Ali, Marwa Gamal Mohamed Feike, A. I. N. Abdelaal, and A. Elnashar, "Wheat Crop Modelling for Higher Production", Systems Modeling: Springer Singapore, pp. 179–202, 2020. Abstract

Due to quick growth of population, climate change and diminished natural resources, food security and nutrition issues face major challenges. Crop models successfully proved crop yield simulation under diverse environments, biotic constraints, gene factors and climate change impacts and adaptation. But, the accuracy of crop models for yield estimates needs to be improved with other limitation factors affecting yield growth and production to ensure global food security. These factors include short-term severe stresses (i.e. cold and heat), pest and diseases, soil dynamic changes due to climate changes, soil nutrient balance, grain quality (i.e. protein, iron and zinc) as well as the potential integration between genotype and phenotype in crop models. Here, we outlined the potential and limitation of wheat crop models to assist breeders, researchers, agronomists and decision-makers to address the current and future challenges linked with global food security.

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Kheir, A. M. S., G. Z. Medhat, A. A. El Baroudy, H. A. Shams El-Din, R. H. Sahar, and A. Elnashar, "Integrating Remote Sensing, GPS, and GIS to Monitor Land Use Change in the Nile Delta of Egypt", Merit Research Journal of Agricultural Science and Soil Sciences, vol. 6, issue 6, pp. 050-064, 2018. AbstractWebsite

Urbanization-associated land use changes lead to modifications of agricultural land including crop area estimation. The goal of the paper is to improve the estimation of main crop areas using high resolution satellite images, image classification, and dynamic GPS to produce land use map of year 2013, and to detect the changes of land use between 1991, 2007 and 2013. In this study, a suitable methodology is developed for estimating crop area and land use by integrating remote sensing with GIS. RapidEye is used in the agricultural field to provide up to date crop information for better production management and monitoring the agricultural areas. Different observation points cover Meet Yazeed command area were collected in the field using dynamic GPS, these points present as the sampling area of cultivated crops and other features in the study area. Three land use maps dated in 1991, 2007 and 2013 were used to monitor the changes in land use classes in the command area. The results indicated that the loss of agricultural areas increased from old date (1991) to the modern date (2013) due to increasing the urban areas.

Liu, C., Q. Zhang, S. Tao, J. Qi, M. Ding, Q. Guan, B. Wu, M. Zhang, M. Nabil, F. Tian, et al., "A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication", Remote Sensing of Environment, vol. 251, issue 15, pp. 112095, 2020. AbstractWebsite

Accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned with the ongoing efforts to achieve sustainable development goals (SDGs) under diminishing natural resources. The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner. However, challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution. In this study, we developed a 30-m planetary-scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index (NDVI) from multiple satellite images. Using a binary crop phenophase profile indicating growing and non-growing periods, we estimated pixel-by-pixel CI by enumerating the total number of valid cropping cycles during the study years. Based on the Google Earth Engine cloud computing platform, we implemented the framework to estimate CI during 2016–2018 in eight geographic regions across continents that are representative of global cropping system diversity. Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices. Spatially, overall accuracies based on validation samples range from 80.0% to 98.9% across different regions worldwide. Regarding the CI classes, single cropping systems are associated with more robust and less biased estimations than multiple cropping systems. Finally, our CI estimates reveal high agreement with two widely used land surface phenology products, including Vegetation Index and Phenology V004 (VIP4) and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics (MCD12Q2), meanwhile providing much more spatial details. Due to its robustness, the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications.

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.

Elnashar, A., M. Abbas, H. Sobhy, and M. Shahba, "Crop Water Requirements and Suitability Assessment in Arid Environments: A New Approach", Agronomy, vol. 11, issue 2, pp. 260, 2021. AbstractWebsite

Efficient land and water management require the accurate selection of suitable crops that are compatible with soil and crop water requirements (CWR) in a given area. In this study, twenty soil profiles are collected to represent the soils of the study area. Physical and chemical properties of soil, in addition to irrigation water quality, provided data are utilized by the Agriculture Land Evaluation System for Arid and semi-arid regions (ALES-Arid) to determine crop suitability. University of Idaho Ref-ET software is used to calculate CWR from weather data while the Surface Energy Balance Algorithms for Land Model (SEBAL) is utilized to estimate CWR from remote sensing data. The obtained results show that seasonal weather-based CWR of the most suitable field crops (S1 and S2 classes) ranges from 804 to 1625 mm for wheat and berssem, respectively, and ranges from 778 to 993 mm in the vegetable crops potato and watermelon, respectively, under surface irrigation. Mean daily satellite-based CWR are predicted based on SEBAL ranges between 4.79 and 3.62 mm in Toshka and Abu Simbel areas respectively. This study provides a new approach for coupling ALES-Arid, Ref-ET and SEBAL models to facilitate the selection of suitable crops and offers an excellent source for predicting CWR in arid environments. The findings of this research will help in managing the future marginal land reclamation projects in arid and semi-arid areas of the world.

Mumtaz, F., Y. Tao, G. de Leeuw, L. Zhao, C. Fan, A. Elnashar, B. Bashir, G. Wang, L. L. Li, S. Naeem, et al., "Modeling Spatio-temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST)", Remote Sensing, vol. 13, issue 1, no. 18: Multidisciplinary Digital Publishing Institute, pp. 61, 2021. AbstractWebsite

Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

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., L. Wang, B. Wu, W. Zhu, and H. Zeng, "Synthesis of global actual evapotranspiration from 1982 to 2019", Earth System Science Data, vol. 13, issue 2: Copernicus GmbH, pp. 447-480, 2021. AbstractWebsite

As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with uncertainty associated with various assumptions regarding their algorithms, parameters, and inputs. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel evaluation of 12 global ET products over different time periods, land surface types, and conditions, the high-performing products were selected for the synthesis of the new dataset using a high-quality flux eddy covariance (EC) covering the entire globe. According to the study results, Penman–Monteith–Leuning (PML), the operational Simplified Surface Energy Balance (SSEBop), the Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105), and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum relative mean error (RME) of 13.94 mm (17.13 %) and a maximum relative root mean square error (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet (last access: 21 January 2021).