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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.

Zeng, H., A. El Nashar, B. Wu, W. Zhu, F. Tian, and Z. Ma, "A framework for separating natural and anthropogenic contributions to evapotranspiration of human-managed land covers in watersheds based on machine learning", Science of The Total Environment, vol. 823, issue 1, pp. 153726, 2022. AbstractWebsite

Actual EvapoTranspiration (ET) represents the water consumption in watersheds; distinguishing between natural and anthropogenic contributions to ET is essential for water conservation and ecological sustainability. This study proposed a framework to separate the contribution of natural and anthropogenic factors to ET of human-managed land cover types using the Random Forest Regressor (RFR). The steps include: (1) classify land cover into natural and human-managed land covers and then divide ET, meteorological, topographical, and geographical data into two parts corresponding to natural and human-managed land cover types; (2) construct a natural ET (ETn) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ETn prediction model is used to predict the ETn of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ETh) by subtracting the natural ET from the total ET (ETt) for human-managed land cover types. Take 2017 as an example, ETn and ETh for rainfed agriculture, mosaic agriculture, irrigated agriculture, and settlement in Colorado, Blue Nile, and Heihe Basin were separated by the proposed framework, with R2 and NSE of predicted ETn above 0.95 and RB within 1% for all three basins. In the semi-arid Colorado River Basin and arid Heihe Basin, human activities on human-managed land cover types tended to increase ET higher than humid Blue Nile Basin. The anthropogenic contribution to total water consumption is approaching 53.68%, 66.47%, and 6.14% for the four human-managed land cover types in Colorado River Basin, Heihe Bain and Blue Nile Basin, respectively. The framework provides strong support for the disturbance of water resources by different anthropogenic activities at the basin scale and the accurate estimation of the impact of human activities on ET to help achieve water-related sustainable development goals.

Wu, B., F. Tian, M. Zhang, S. Piao, H. Zeng, W. Zhu, J. Liu, A. Elnashar, and Y. Lu, "Quantifying global agricultural water appropriation with data derived from earth observations", Journal of Cleaner Production, vol. 358, pp. 131891, 2022. AbstractWebsite

Agricultural water appropriation has undergone rapid changes in recent decades, but estimates of global water appropriation have not been updated with the latest data and consistent methods. Documenting these changes is challenging given the heterogeneous water use landscape and the growing influence of human activities worldwide, and this complexity cannot be well addressed with the existing methodology, which is subject to large model uncertainties. Here, a spatial analysis and aggregated method was proposed to quantify and refine estimates of global agricultural water appropriation (GAWA) in terms of consumptive freshwater use, with data derived from Earth observations, independent of estimates from hydrological models. The results show the global water appropriation at the pixel scale, in agroecological zones and in the main water-consuming countries, including global maps of rainfed and irrigated cropland evapotranspiration (ET), net water consumption due to irrigation, natural ET and renewable freshwater resources (RFWR), and indicate that agriculture remains the largest user in terms of both water consumption and withdrawals worldwide, representing 87% of global water consumption, with approximately 60% of global freshwater withdrawals devoted to irrigation circa 2015. The percentage of withdrawals devoted to irrigation has decreased in recent decades when compared to the previous estimate of 70%. The results reveal the actual global crop consumptive use (8053.6 km3/yr) of blue and green water and the total human water consumption (8442 km3/yr), which represents the part of the water cycle affected by human intervention, mainly (95.4%) by agriculture. This study reveals that high-resolution irrigated croplands are essential for accurate estimations of water use appropriation and demonstrates that earth observation-derived data can provide a new understanding of global water use landscape. The study can support decision making in sustaining food and water security, and implementing water-adapted sustainable agricultural policies.

Wang, L., B. Wu, A. Elnashar, W. Zhu, N. Yan, Z. Ma, S. Liu, and X. Niu, "Incorporation of Net Radiation Model Considering Complex Terrain in Evapotranspiration Determination with Sentinel-2 Data", Remote Sensing, vol. 14, issue 5, pp. 1191, 2022. Website
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.

Tuvdendorj, B., H. Zeng, B. Wu, A. Elnashar, M. Zhang, F. Tian, M. Nabil, L. Nanzad, A. Bulkhbai, and N. Natsagdorj, "Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia", Remote Sensing, vol. 14, issue 8, pp. 1830, 2023. AbstractWebsite

Accurate and early crop-type maps are essential for agricultural policy development and food production assessment at regional and national levels. This study aims to produce a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) and Sentinel-2 (S2) images with the Google Earth Engine (GEE) environment. A total of three satellite data combination scenarios are set, including S1 alone, S2 alone, and the combination of S1 and S2. In order to avoid the impact of data gaps caused by clouds on crop classification, this study reconstructed the time series of S1 and S2 with a 10-day interval using the median composite method, linear moving interpolation, and Savitzky–Golay (SG) filter. Our results indicated that crop-type classification accuracy increased with the increase in data length to all three data combination scenarios. S2 alone has higher accuracy than S1 alone and the combination of S1 and S2. The crop-type map with the highest accuracy was generated using S2 data from 150 days of the year (DOY) (11 May) to 260 DOY (18 September). The OA and kappa were 0.93 and 0.78, respectively, and the F1-score for spring wheat and rapeseed were 0.96 and 0.80, respectively. The classification accuracy of the crop increased rapidly from 210 DOY (end of July) to 260 DOY (August to mid-September), and then it remained stable after 260 DOY. Based on our analysis, we filled the gap of the crop-type map with 10 m spatial resolution in northern Mongolia, revealing the best satellite combination and the best period for crop-type classification, which can benefit the achievement of sustainable development goals 2 (SDGs2).

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.

Nawaz, Z., X. Li, Y. Chen, N. Nawaz, R. Gull, and A. Elnashar, Spatio-Temporal Assessment of Global Precipitation Products Over the Largest Agriculture Region in Pakistan, , vol. 12, issue 21: Multidisciplinary Digital Publishing Institute, pp. 3650, 2020. AbstractWebsite

Spatial and temporal precipitation data acquisition is highly important for hydro-meteorological applications. Gridded precipitation products (GPPs) offer an opportunity to estimate precipitation at different time and resolution. Though, the products have numerous discrepancies that need to be evaluated against in-situ records. The present study is the first of its kind to highlight the performance evaluation of gauge based (GB) and satellite based (SB) GPPs at annual, winter, and summer monsoon scale by using multiple statistical approach during the period of 1979–2017 and 2003–2017, respectively. The result revealed that the temporal magnitude of all the GPPs was different and deviate up to 100–200 mm with overall spatial pattern of underestimation (GB product) and overestimation (SB product) from north to south gradient. The degree of accuracy of GB products with observed precipitation decreases with the increase in the magnitude of precipitation and vice versa for SB precipitation products. Furthermore, the observed precipitation revealed the positive trend with multiple turning points during the period 1979–2005. However, the gentle increase with no obvious break point has been detected during the period of 2005–2017. The large inter-annual variability and trends slope of the reference data series were well captured by Global Precipitation Climatology Centre (GPCC) and Tropical Rainfall Measuring Mission (TRMM) products and outperformed the relative GPPs in terms of higher R2 values of ≥ 0.90 and lower values of estimated RME ≤ 25% at annual and summer monsoon season. However, Climate Research Unit (CRU) performed better during winter estimates as compared with in-situ records. In view of significant error and discrepancies, regional correction factors for each GPPs were introduced that can be useful for future concerned projects over the study region. The study highlights the importance of evaluation by the careful selection of potential GPPs for the future hydro-climate studies over the similar regions like Punjab Province

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.

Mumtaz, F., T. Yu, G. de Leeuw, L. Zhao, C. Fan, A. Elnashar, B. Bashir, G. Wang, and L. L. N. Li, "Modeling Spatio-temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST)", Remote Sensing, vol. 12, issue 18, pp. 2987, 2020. 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.

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.

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., A. A. Alrajhi, A. M. Ghoneim, E. F. Ali, A. Magrashi, M. G. Zoghdan, S. A. M. Abdelkhalik, A. E. Fahmy, and A. Elnashar, "Modeling deficit irrigation-based evapotranspiration optimizes wheat yield and water productivity in arid regions", Agricultural Water Management, vol. 256, issue 1, pp. 107122, 2021. AbstractWebsite

Climate change and water scarcity have put food security and sustainable development in arid regions at risk. Irrigation based actual evapotranspiration (ETc) has recently been added as a new tool in the Decision Support System for Agrotechnology Transfer (DSSAT) models and might improve irrigation water management, thus more research is needed. For this purpose, three Wheat models (CERES, CROPSIM and N-Wheat) in the latest version of DSSAT (v. 4.7.5) were calibrated and evaluated using experimental field data across three growing seasons. Field data included irrigation by different fractions of ETc as 80%, 100% and 120%. The calibrated models were then employed to predict wheat grain yield (GY), biomass yield (BY), irrigation, evapotranspiration, water use efficiency-based evapotranspiration (WUE_ET), and water use efficiency-based irrigation (WUE_Irri) for 10 locations represent Nile delta in long term simulation (1991–2020). The models showed robust simulations of ETc compared to observed values under all corresponding treatments, demonstrating high calibration accuracy and the ability to predict yield and water for other locations in the long term. Simulation treatments included automatic irrigation with different fractions of 50%, 60%, 70%, 80%, 90% and 100% from ETc. Hereinafter, the simulated GY and WUE_ET were compared with those obtained by farmers in all locations to specify the recommended treatment achieving higher yield and water productivity. In all locations, simulated GY and BY ranged (4000–9000 kg ha-1), and (10,500–18,000 kg ha-1), respectively with associated uncertainty between treatments and locations. Averaged over ten locations, and 30 years, the simulated GY under full irrigation treatment (100% ETc), showed the superiority with an increase of 27.5%, 13.0%, 5.0%, 1.5%, and 0.4% relative to irrigation with 50%, 60%, 70%, 80%, and 90% ETc, respectively. Deficit irrigation-based ET decreased WUE_Irri, whilst increased WUE_ET, achieving the higher value (20.0 kg ha-1 mm-1) with irrigation based 90% ETc. However, deficit irrigation with 90% ETc (I5) produced higher WUE values than full irrigation (100% ETc), with increases of 0.08% and 10.6% for WUE_ET and WUE_irri, respectively. Comparing simulated GY and WUE_ET with farmers values in all locations, simulated values under irrigation based 90% ETc increased by 1.7% and 63%, respectively, confirming the importance of irrigation scheduling based 90% ETc in maximizing wheat yield and water productivity in arid regions.

Fan, C., Y. Li, J. Guang, Z. Li, A. Elnashar, M. Allam, and G. de Leeuw, "The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China", Remote Sensing, vol. 12, issue 10, no. 10: Multidisciplinary Digital Publishing Institute, pp. 1613, 2020. AbstractWebsite

The outbreak of the COVID-19 virus in Wuhan, China, in January 2020 just before the Spring Festival and subsequent country-wide measures to contain the virus, effectively resulted in the lock-down of the country. Most industries and businesses were closed, traffic was largely reduced, and people were restrained to their homes. This resulted in the reduction of emissions of trace gases and aerosols, the concentrations of which were strongly reduced in many cities around the country. Satellite imagery from the TROPOspheric Monitoring Instrument (TROPOMI) showed an enormous reduction of tropospheric NO2 concentrations, but aerosol optical depth (AOD), as a measure of the amount of aerosols, was less affected, likely due to the different formation mechanisms and the influence of meteorological factors. In this study, satellite data and ground-based observations were used together to estimate the separate effects of the Spring Festival and the COVID-19 containment measures on atmospheric composition in the winter of 2020. To achieve this, data were analyzed for a period from 30 days before to 60 days after the Spring Festivals in 2017–2020. This extended period of time, including similar periods in previous years, were selected to account for both the decreasing concentrations in response to air pollution control measures, and meteorological effects on concentrations of trace gases and aerosols. Satellite data from TROPOMI provided the spatial distributions over mainland China of the tropospheric vertical column density (VCD) of NO2, and VCD of SO2 and CO. The MODerate resolution Imaging Spectroradiometer (MODIS) provided the aerosol optical depth (AOD). The comparison of the satellite data for different periods showed a large reduction of, e.g., NO2 tropospheric VCDs due to the Spring Festival of up to 80% in some regions, and an additional reduction due to the COVID-19 containment measures of up to 70% in highly populated areas with intensive anthropogenic activities. In other areas, both effects are very small. Ground-based in situ observations from 26 provincial capitals provided concentrations of NO2, SO2, CO, O3, PM2.5, and PM10. The analysis of these data was focused on the situation in Wuhan, based on daily averaged concentrations. The NO2 concentrations started to decrease a few days before the Spring Festival and increased after about two weeks, except in 2020 when they continued to be low. SO2 concentrations behaved in a similar way, whereas CO, PM2.5, and PM10 also decreased during the Spring Festival but did not trace NO2 concentrations as SO2 did. As could be expected from atmospheric chemistry considerations, O3 concentrations increased. The analysis of the effects of the Spring Festival and the COVID-19 containment measures was complicated due to meteorological influences. Uncertainties contributing to the estimates of the different effects on the trace gas concentrations are discussed. The situation in Wuhan is compared with that in 26 provincial capitals based on 30-day averages for four years, showing different effects across China.

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.

Elnashar, A., H. Zeng, B. Wu, T. G. Gebremicael, and K. Marie, "Assessment of environmentally sensitive areas to desertification in the Blue Nile Basin driven by the MEDALUS-GEE framework", Science of The Total Environment, vol. 815, issue 1, pp. 152925, 2022. AbstractWebsite

Assessing environmentally sensitive areas (ESA) to desertification and understanding their primary drivers are necessary for applying targeted management practices to combat land degradation at the basin scale. We have developed the MEditerranean Desertification And Land Use framework in the Google Earth Engine cloud platform (MEDALUS-GEE) to map and assess the ESA index at 300 m grids in the Blue Nile Basin (BNB). The ESA index was derived from elaborating 19 key indicators representing soil, climate, vegetation, and management through the geometric mean of their sensitivity scores. The results showed that 43.4%, 28.8%, and 70.4% of the entire BNB, Upper BNB, and Lower BNB, respectively, are highly susceptible to desertification, indicating appropriate land and water management measures should be urgently implemented. Our findings also showed that the main land degradation drivers are moderate to intensive cultivation across the BNB, high slope gradient and water erosion in the Upper BNB, and low soil organic matter and vegetation cover in the Lower BNB. The study presented an integrated monitoring and assessment framework for understanding desertification processes to help achieve land-related sustainable development goals.

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, vol. 793, 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.

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): (Elnashar et al., 2020), as well as on the Google Earth Engine (GEE) application through this link: (last access: 21 January 2021).

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

Bofana, J., M. Zhang, B. Wu, H. Zeng, M. Nabil, N. Zhang, A. Elnashar, F. Tian, J. M. da Silva, A. Botão, et al., "How long did crops survive from floods caused by Cyclone Idai in Mozambique detected with multi-satellite data", Remote Sensing of Environment, vol. 269, pp. 112808, 2021. AbstractWebsite

Floods are causing massive losses of crops and agricultural infrastructures in many regions across the globe. During the 2018/2019 agricultural year, heavy rains from Cyclone Idai caused flooding in Central Mozambique and had the greatest impact on Sofala Province. The main objectives of this study are to map the flooding durations, evaluate how long crops survived the floods, and analyse the dynamics of the affected crops and their recovery following various flooding durations using multi-source satellite data. Our results indicate that Otsu method-based flooding mapping provides reliable flood extents and durations with an overall accuracy higher than 90%, which facilitates the assessment of how long crops can survive floods and their recovery progress. Croplands in both Buzi and Tica administrative units were the most severely impacted among all the regions in Sofala Province, with the largest flooded cropland extent at 23,101.1 ha in Buzi on 20 March 2019 and the most prolonged flooding duration of more than 42 days in Tica and Mafambisse. Major summer crops, including maize and rice, could survive when the fields were inundated for up to 12 days, while all crops died when the flooding duration was longer than 24 days. The recovery of surviving crops to pre-flooding status took a much longer time, from approximately 20 days to as long as one month after flooding. The findings presented herein can assist decision making in developing countries or remote regions for flood monitoring, mitigation and damage assessment.