Ganzour, S. K., T. K. Ghabour, and A. Elnashar, "Integrating geospatial analysis and the suitability-resources quality index to optimize crop suitability for sustainable agriculture", Frontiers in Sustainable Food Systems, vol. Volume 9 - 2025, 2025. AbstractWebsite

This study develops a framework for sustainable agriculture in an arid Egyptian region, enhancing food security through advanced assessments of crop suitability. By integrating freely accessible Google Earth Engine (GEE) data with field observations and laboratory analyses, the framework employs GEE, Microsoft Excel, and ArcGIS for geospatial analysis and visualization. In resource-scarce environments impacted by climate change, selecting crops with high suitability and minimal Crop Water Requirements (CWR) and Crop Fertilizer Requirements (CFR) is vital for sustainable practices. Hence, this study proposes the Suitability-Resources Quality Index (SRQI), a decision-support metric designed to optimize crop selection. Results indicated that the SRQI index-guided optimized selection of the top two highly suitable crops reduced CWR by 14–34% and CFR by 34–88% across various crop types and seasons. The unoptimized crop suitability analysis suggested that sunflower and cotton are optimal summer crops, while alfalfa and faba bean are prioritized for winter cropping. For year-round vegetable production, cabbage, watermelon, and pepper are top choices. For fruit trees, deciduous options such as apple and fig, alongside evergreen banana and olive trees, are suggested. However, the SRQI endorses sunflower and soybean for summer cropping systems, while prioritizing faba bean and sugar beet for winter. Across seasons, watermelon, cabbage, and pea are the top vegetable choices, and for fruit trees, deciduous grape and fig, alongside evergreen olive and date palm, are recommended. This integrated monitoring and assessment framework prioritizes crops with lower CWR and CFR, supporting water- and land-related Sustainable Development Goals (SDGs) and promoting resilient agricultural systems in arid environments to mitigate climate change.

Abebe, A. K., X. Zhou, T. Lv, Z. Tao, Y. Bayissa, H. Zhang, and A. Elnashar, "Advancing basin-scale drought monitoring: Development of a regional combined drought index using precipitation, soil moisture, and vegetation data", Agricultural Water Management, vol. 318, pp. 109734, 2025. AbstractWebsite

Drought remains a critical challenge in Ethiopia’s Awash River Basin (ARB), where rainfed agriculture is highly sensitive to climate variability. This study presents a regional Combined Drought Index (rCDI), integrating the Standard Precipitation Index (SPI-3), soil moisture anomaly (SMA), and Vegetation anomaly (VA) using a Principal Component Analysis (PCA)-based weighting approach. Monthly gridded data from 2001 to 2023 were used to generate dynamic, grid-specific weights, capturing spatiotemporal drought variability across the basin. The rCDI was validated against independent station-based SPI-3 data, detrended crop yields (maize and sorghum), and documented drought events for both the Belg (short rainy) and Kiremt (long rainy) seasons. Analysis combined Google Earth Engine (GEE) with Python via Google Colab. Strong correlations (r > 0.70) were observed with SPI-3 were observed in most areas, though weaker (r > 0.45) in arid Belg Zones. Crop yield analysis revealed stronger rCDI sensitivity to maize in the upper ARB and sorghum in upland/northwestern areas, reflecting crop-climate adaptation. The rCDI effectively captured major droughts (2002/2003, 2008–2012, 2015, and 2022), consistent with reported socio-economic impacts. Seasonal patterns showed Belg experiencing more frequent and severe droughts than Kiremt. Statistical trend analysis confirmed rCDI’s strength in monitoring evolving drought conditions, supporting early warning and sustainable resource management. A statistical downscaling using Artificial Neural Network (ANN) enhanced soil moisture resolution from 10 km to 1 km, improving rCDI's accuracy. By integrating meteorological, agricultural, and ecological dimensions, the rCDI provides a comprehensive tool for basin-scale drought assessment and monitoring in data-scarce, climate-sensitive regions.

Abebe, A. K., X. Zhou, T. Lv, A. Elnashar, A. Kebede, C. Wang, and H. Zhang, "Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya", Remote Sensing, vol. 17, issue 10, pp. 1763, 2025. AbstractWebsite

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m3/m3, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m3/m3 and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions.

Shojaeezadeh, S. A., A. Elnashar, and T. K. D. Weber, "A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning", Science of Remote Sensing, vol. 11, pp. 100227, 2025. AbstractWebsite

Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Earth observation data ubiquity, attempts have been made to accurately detect crop phenology using Remote Sensing (RS) and high resolution weather data. However, most studies have focused on large scale predictions of phenology or developed methods which are not adequate to help crop modeler communities on leveraging Sentinel-1 and Sentinal-2 data and fusing them with high resolution climate data, using a novel framework. For this, we trained a Machine Learning (ML) LightGBM model to predict 13 phenological stages for eight major crops across Germany at 20 m scale. Observed phenologies were taken from German national phenology network (German Meteorological Service; DWD) between 2017 and 2021. We proposed a thorough feature selection analysis to find the best combination of RS and climate data to detect phenological stages. At national scale, predicted phenology resulted in a reasonable precision of R2 > 0.43 and a low Mean Absolute Error of 6 days, averaged over all phenological stages and crops. The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. The results indicated that combining radar sensors with climate data yields a very promising performance for a multitude of practical applications. Moreover, these improvements are expected to be useful to generate highly valuable input for crop model calibrations and evaluations, facilitate informed agricultural decisions, and contribute to sustainable food production to address the increasing global food demand.

Wang, L., B. Wu, W. Zhu, A. Elnashar, N. Yan, and Z. Ma, "Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units", Remote Sensing, vol. 17, issue 7, pp. 1201, 2025. AbstractWebsite

This study proposes an evapotranspiration (ET) disaggregation model based on slope units. Different slope units are first delineated based on digital elevation model data with high spatial resolution. Key factors influencing ET variability across topographies, such as radiation, vegetation, and moisture, are integrated using Sentinel-2 and DEM data to construct an indicating factor. A slope-scale ET disaggregation model is developed using ETWatch data (1 km resolution) and the integrated factor, yielding reliable 10 m resolution ET data that reflect slope-scale variations. The validation in Huairou and Baotianman shows coefficients of determination of 0.9 and 0.91, respectively, and root mean square errors of 0.45 mm and 0.47 mm. Compared to the original 1 km resolution ET data, the disaggregated results show improved accuracy, with R2 values increasing by 1% (Huairou) and 2% (Baotianman) and RMSE decreasing by 21% and 13%, respectively. This model offers a novel approach for estimating forest evapotranspiration in mountainous areas and significant potential for water resource management and sustainable land–water allocation.

Kheir, A. M. S., A. Govind, V. Nangia, M. A. El-Maghraby, A. Elnashar, M. Ahmed, H. Aboelsoud, R. Gamal, and T. Feike, "Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality", Computers and Electronics in Agriculture, vol. 234, pp. 110317, 2025. AbstractWebsite

Ensuring accurate predictions of wheat yield and nutritional content is vital for enhancing agricultural productivity and food security. This study aims to improve wheat yield prediction by integrating process-based models (PBM), machine learning (ML), and remote sensing (RS) techniques. Three Decision Support System for Agrotechnology Transfer (DSSAT) wheat models were calibrated and evaluated using field data from three wheat cultivars grown over three seasons in diverse environments. We developed a hybrid PBM-ML-RS approach using polynomial regression to generate iron (Fe) and zinc (Zn) content from nitrogen predictions. The DSSAT wheat models slightly overestimated wheat yield but accurately predicted nitrogen content. The hybrid PBM-ML-RS approach closely estimated Fe and Zn content with a root mean square error (RMSE) of 0.42 t/ha for yield and 0.89 % for nitrogen content. The integration of ML and RS improved the prediction accuracy for Fe and Zn, achieving RMSE values of 0.35 % and 0.28 % respectively. Spatial simulations provided detailed geographic estimations of wheat yield and nutrient content, supporting site-specific management practices. This study demonstrates the potential of combining PBM, ML, and RS for comprehensive yield and nutrition prediction. The findings indicate a modest decrease in protein, Fe, and Zn concentrations with increasing grain yield, exhibiting high variability across different sites and cultivars. Future research should integrate additional data sources to enhance model robustness and applicability to other crops and regions, contributing to sustainable agriculture and food security.

Elnashar, A., S. A. Shojaeezadeh, and T. K. D. Weber, "A Multi-Model Approach for Remote Sensing-Based Actual Evapotranspiration Mapping using Google Earth Engine (ETMapper-GEE)", Journal of Hydrology, vol. 657, pp. 133062, 2025. AbstractWebsite

Accurate estimation of actual evapotranspiration (ETa) through remote sensing (RS) is essential for effective large-scale water management. We developed an EvapoTranspiration Mapper in the Google Earth Engine environment (ETMapper-GEE) to estimate RS-ETa using Landsat satellite data employing four models: Surface Energy Balance Algorithm for Land (SEBAL), Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), surface temperature-vegetation-based triangle (TriAng), and Operational Simplified Surface Energy Balance (SSEBop). The estimation integrates extrapolation approaches (Evaporative Fraction (EF) and EvapoTranspiration Fraction (ETF)), reference ET types (grass (ETo) and alfalfa (ETr)), and climate forcing datasets (the fifth generation of the European ReAnalysis (ERA5-Land) and the Climate Forecast System version 2 (CFSv2)). The ETMapper was evaluated against observed data from flux towers in Germany for the period 2020 to 2022. The results showed that EF outperformed the ETF approach, with a more than an 8% higher correlation of determination (R²) and 35% lower Root Mean Square Error (RMSE) compared to the other approaches. Among the EF approaches, TriAng (RMSE = 1.38 mm d-1) exhibited the best performance, followed by METRIC (1.69 mm d-1) and SEBAL (2.07 mm d-1). Using ETMapper with ETo resulted in at least 4% higher R² and reduction in RMSE by at least 29% compared to ETr. Forcing ETMapper with ERA5 yielded better accuracy (R² >4%, RMSE <12%) than when using CFSv2. This study provides an integrated framework for RS-ETa estimation, supporting water-related Sustainable Development Goals, especially in agricultural contexts. [The ETMapper framework is designed to function seamlessly as a Google Earth Engine (GEE) application. To explore its features and capabilities over Germany in central Europe, please follow this link:

https://elnashar.users.earthengine.app/view/etmapper]

Zeng, H., Y. Xie, A. Elnashar, S. Wang, H. Zhao, J. Li, and B. Wu, "Increasing Cropland Area and its Associated Human-induced Water Consumption Puts Ebinur Lake at Risk of Drying up", Journal of Remote Sensing, 2025. AbstractWebsite

Increasing Cropland Area and its Associated Human-induced Water Consumption Puts Ebinur Lake at Risk of Drying up

Mooselu, M. G., M. R. Nikoo, L. Helge, M. S. Bjørkenes, A. Elnashar, S. A. Shojaeezadeh, and T. K. D. Weber, "Assessing road construction effects on turbidity in adjacent water bodies using Sentinel-1 and Sentinel-2", Science of The Total Environment, vol. 957, issue 2, pp. 177554, 2024. AbstractWebsite

Road construction significantly affects water resources by introducing contaminants, fragmenting habitats, and degrading water quality. This study examines the use of Remote Sensing (RS) data of Sentinel-1 (S1) and Senitnel-2 (S2) in Google Earth Engine (GEE) to do spatio-temporal analysis of turbidity in adjacent water bodies during the construction and operation of the E18 Arendal-Tvedestrand highway in southeastern Norway from 2017 to 2021. S1 radiometric data helped delineate water extents, while S2-Top of Atmosphere (TOA) multispectral data, corrected using the Modified Atmospheric correction for INland waters (MAIN), used to estimate turbidity levels. To ensure a comprehensive time series of RS data, we utilized S2-TOA data corrected with the MAIN algorithm rather than S2-Bottom Of Atmosphere (BOA) data. We validated the MAIN algorithm's accuracy against GLORIA (Global Observatory of Lake Responses to Interventions and Drivers) observations of surface water reflectance in lakes, globally. Subsequently, the corrected S2 data is used to calculate turbidity using the Novoa and Nechad retrieval algorithms and compared with GLORIA turbidity observations. Findings indicate that the MAIN algorithm adequately estimates water-leaving surface reflectance (Pearson correlation > 0.7 for wavelengths between 490 and 705 nm) and turbidity (Pearson correlation > 0.6 for both algorithms), determining Nechad as the more effective algorithm. In this regard, we used S2 corrected images with MIAN to estimate turbidity in the study area and evaluated with local gauge data and observational reports. Results indicate that the proposed framework effectively captures trends and patterns of turbidity variation in the study area. Findings verify that road construction can increase turbidity in adjacent water bodies and emphasis the employing RS data in cloud platforms like GEE can provide insights for effective long-term water quality management strategies during construction and operation phases.

Mooselu, M. G., M. R. Nikoo, H. Liltved, M. S. Bjørkenes, A. Elnashar, S. A. Shojaeezadeh, and T. K. D. Weber, "Assessing road construction effects on turbidity in adjacent water bodies using Sentinel-1 and Sentinel-2", Science of The Total Environment, vol. 957, issue 20, pp. 177554, 2024. AbstractWebsite

Road construction significantly affects water resources by introducing contaminants, fragmenting habitats, and degrading water quality. This study examines the use of Remote Sensing (RS) data of Sentinel-1 (S1) and Senitnel-2 (S2) in Google Earth Engine (GEE) to do spatio-temporal analysis of turbidity in adjacent water bodies during the construction and operation of the E18 Arendal-Tvedestrand highway in southeastern Norway from 2017 to 2021. S1 radiometric data helped delineate water extents, while S2-Top of Atmosphere (TOA) multispectral data, corrected using the Modified Atmospheric correction for INland waters (MAIN), used to estimate turbidity levels. To ensure a comprehensive time series of RS data, we utilized S2-TOA data corrected with the MAIN algorithm rather than S2-Bottom Of Atmosphere (BOA) data. We validated the MAIN algorithm's accuracy against GLORIA (Global Observatory of Lake Responses to Interventions and Drivers) observations of surface water reflectance in lakes, globally. Subsequently, the corrected S2 data is used to calculate turbidity using the Novoa and Nechad retrieval algorithms and compared with GLORIA turbidity observations. Findings indicate that the MAIN algorithm adequately estimates water-leaving surface reflectance (Pearson correlation > 0.7 for wavelengths between 490 and 705 nm) and turbidity (Pearson correlation > 0.6 for both algorithms), determining Nechad as the more effective algorithm. In this regard, we used S2 corrected images with MIAN to estimate turbidity in the study area and evaluated with local gauge data and observational reports. Results indicate that the proposed framework effectively captures trends and patterns of turbidity variation in the study area. Findings verify that road construction can increase turbidity in adjacent water bodies and emphasis the employing RS data in cloud platforms like GEE can provide insights for effective long-term water quality management strategies during construction and operation phases.