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.

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).

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
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.

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.

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.

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.

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.

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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