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.

Kheir, A. M. S., Z. Ding, M. G. M. Ali, T. Feike, A. I. N. Abdelaal, and A. Elnashar, "Wheat Crop Modelling for Higher Production", Wheat Crop Modelling for Higher Production: Springer Singapore, 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.

Kheir, A. M. S., K. A. Ammar, A. Attia, A. Elnashar, S. Ahmad, S. F. El-Gioushy, and M. Ahmed, "Cereal Crop Modeling for Food and Nutrition Security", Global Agricultural Production: Resilience to Climate Change: Springer International Publishing, 2023. Abstract

Rapid population growth, climate change, and limited natural resources have widened the gap between food production and consumption, contributing to global hunger. Improving cereal crop production is a critical hot spot challenge for closing this gap and ensuring global food security and nutrition. Previous data and findings from published literature demonstrated that cereal crop models have been applied and developed globally over the last 30 years under a wide range of climate, soil, genotype, and management conditions. However, when the models are applied to pests, diseases, phosphorus fertilization, potassium fertilization, iron, and zinc, further improvements are required. Furthermore, the integration of genotypes and phenotypes is critical for food security, necessitating careful consideration in crop models. We examined about 31 cereal crop models for increasing crop production and ensuring food and nutrition security. Furthermore, we discussed the current limitations in crop model application, as well as the critical need to integrate with other cutting-edge sciences, such as remote sensing, machine learning, and deep learning. This will undoubtedly improve crop model accuracy and reduce uncertainty, assisting agronomists and decision makers in ensuring food and nutrition security. In this chapter, we discussed the current and further improvements of cereal crop models in assisting breeders, researchers, agronomists, and policy makers in addressing current and future challenges related to global food security and nutrition.

Nabil, M., M. Zhang, B. Wu, J. Bofana, and A. Elnashar, "Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets", Big Earth Data, vol. 6, issue 1, pp. 54-76, 2022. AbstractWebsite

Despite its essential importance to various spatial agriculture and environmental applications, the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products. Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping, which leads to high spatial discrepancies among remote sensing cropland products. Since no dataset could cope with all limitations, multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers. Here, in the current study, four cropland products, produced initially from multiple sensors (e.g. Landsat-8 OLI, Sentinel-2 MSI, and PROBA–V) to cover the period (2015–2017), were integrated based on their cropland mapping accuracy to build a more accurate cropland layer. The four cropland layers’ accuracy was assessed at Agro-ecological zones units via an intensive reference dataset (17,592 samples). The most accurate cropland layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa. As a result, the new layer was produced in higher cropland mapping accuracy (overall accuracy = 91.64% and cropland’s F-score = 0.75). The layer mapped the African cropland area as 282 Mha (9.38% of the Continent area). Compared to earlier cropland synergy layers, the constructed cropland mask showed a considerable improvement in its spatial resolution (30 m instead of 250 m), mapping quality, and closeness to official statistics (R2 = 0.853 and RMSE = 2.85 Mha). The final layer can be downloaded as described under the “Data Availability Statement” section.

M Zhang, B. Wu, H. Zeng, G. He, C. Liu, S. Tao, Q. Zhang, M. Nabil, F. Tian, J. Bofana, et al., "GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery", Earth System Science Data, vol. 13, issue 10, pp. 4799-4817, 2021. AbstractWebsite

The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28×106 km2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: (Zhang et al., 2020).

Kheir, A. M. S., K. A. Ammar, A. Amer, M. G. M. Ali, Z. Ding, and A. Elnashar, "Machine learning-based cloud computing improved wheat yield simulation in arid regions", Computers and Electronics in Agriculture, vol. 203, pp. 107457, 2022. AbstractWebsite

Combining machine learning (ML) with dynamic models is recommended by recent research for creating a hybrid approach for robust simulations but has received less attention thus far. Herein, we combined multi- ML algorithms with multi-crop models (CMs) of the DSSAT platform to develop a hybrid approach for wheat yield simulation over 40 years in different locations. The simulation analysis included temperatures (minimum and maximum), solar radiation, and precipitation as important key ecological factors in wheat production that varied across sites and years. Detailed observed datasets of wheat yield from 1981 to 2020 were used for training and testing Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest Regressor (RFR), and Support Vector Regressor (SVR) using Google Colaboratory (Colab). Such models were built to create four main approaches, including two approaches as hybrid (CMs-ML) and benchmark (pure ML), as well as two testing methods for each approach such as default (75 % training and 25 % testing) and warmest years (2001, 2006, 2009, 2010, and 2018). In addition to wheat yield simulations, ML approaches were used to identify the important features, improve accuracy, and reduce overfitting. We developed ML approaches by novel cells on the built models (i.e., pure ML and hybrid) to eliminate less important features from permutation. Our results revealed that ANN and RFR outperformed other ML algorithms (SVR and KNN) in wheat yield simulation accuracy. Application of ML algorithms reduced yield change from 31.7 % under DSSAT simulations to 8.1 % and uncertainty from 12.8 % to 7.2 % relative to observed wheat yield over the last four decades (1981–2020). Our novel approach, which includes a hybrid CMs-ML model, cloud computing, and a new permutation tool, could be effectively used for robust crop yield simulation on a regional and global scale, contributing to better aid decision-making strategies.

Oad, V. K., A. Szymkiewicz, N. A. Khan, S. Ashraf, R. Nawaz, A. Elnashar, S. Saad, and A. H. Qureshi, "Time series analysis and impact assessment of the temperature changes on the vegetation and the water availability: A case study of Bakun-Murum Catchment Region in Malaysia", Remote Sensing Applications: Society and Environment, vol. 29, pp. 100915, 2023. AbstractWebsite

The Bakun-Murum (BM) catchment region of the Rajang River Basin (RRB), Sarawak, Malaysia, has been under severe threat for the last few years due to urbanization, global warming, and climate change. The present study aimed to evaluate the time series analysis and impact assessment of the temperature changes on the vegetation/agricultural lands and the water availability within the BM region. For this purpose, the Landsat data for the past thirty years (1990–2020) were used. Remote sensing techniques for estimating the surface temperatures and variation within the vegetation and water bodies were utilized, and validation was done using on-ground weather stations. Google Earth Engine (GEE) and other RS & GIS tools were used for analyzing the time series trends of land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The results exposed an overall rise of 1.06 °C in the annual mean temperatures over the last thirty years. A maximum annual mean NDVI of 0.48 was recorded for 2018 and 2019. The lowest annual mean NDVI (0.27) was observed in 2005. The annual mean NDWI increased to 0.48 in 2018 and 2019, respectively. The statistical correlation results revealed the coefficient of determination (R2) of 0.09 and 0.13 for the annual mean LST and annual mean NDVI and the annual mean LST and annual mean NDWI, respectively. Moreover, the Mann-Kendall trend test for the annual mean temperature series indicates a slightly increasing trend with Sen's slope of 0.03 °C/year. It is found that there is a positive trend in the annual mean rainfall patterns, as Sen's slope indicates a yearly increase of 50.58 mm/year. This study found significant changes in the LST, NDVI, and NDWI of the BM catchment region during the last thirty years, demanding the concerned authorities' instant attention to alleviate the adverse effects of such changes to protect the ecosystem.

Bachagha, N., A. Elnashar, M. Tababi, F. Souei, and W. Xu, "The Use of Machine Learning and Satellite Imagery to Detect Roman Fortified Sites: The Case Study of Blad Talh (Tunisia Section)", Applied Sciences, vol. 13, issue 4, pp. 2613, 2023. AbstractWebsite

This study focuses on an ad hoc machine-learning method for locating archaeological sites in arid environments. Pleiades (P1B) were uploaded to the cloud asset of the Google Earth Engine (GEE) environment because they are not yet available on the platform. The average of the SAR data was combined with the P1B image in the selected study area called Blad Talh at Gafsa, which is located in southern Tunisia. This pre-desert region has long been investigated as an important area of Roman civilization (106 BCE). The results show an accurate probability map with an overall accuracy and Kappa coefficient of 0.93 and 0.91, respectively, when validated with field survey data. The results of this research demonstrate, from the perspective of archaeologists, the capability of satellite data and machine learning to discover buried archaeological sites. This work shows that the area presents more archaeological sites, which has major implications for understanding the archaeological significance of the region. Remote sensing combined with machine learning algorithms provides an effective way to augment archaeological surveys and detect new cultural deposits.

Wu, B., F. Tian, M. Nabil, J. Bofana, Y. Lu, A. Elnashar, A. N. Beyene, M. Zhang, H. Zeng, and W. Zhu, "Mapping global maximum irrigation extent at 30m resolution using the irrigation performances under drought stress", Global Environmental Change, vol. 79, pp. 102652, 2023. AbstractWebsite

Accurate global irrigation information is essential for managing water scarcity and improving food security. However, the mapping of high-resolution irrigation at the global scale is challenging due to the wide range of climate conditions, crop types and phenology, ambiguous and heterogeneous spectral features, and farming practices. Here, a robust method is proposed using irrigation performance under drought stress as a proxy for crop productivity stabilization and crop water consumption. For each irrigation mapping zone (IMZ), dry months in the 2017–2019 period and the driest months in the 2010–2019 period were identified over the growing season. The thresholds of the normalized difference vegetation index (NDVI) in the dry months from 2017 to 2019 and the NDVI deviation (NDVIdev) in the driest month were identified to separate irrigated and rainfed cropland with samples. The final threshold from either the NDVI or the NDVIdev of the IMZ was determined with a higher overall accuracy in separating irrigated and non-irrigated areas. The results show that the global maximum irrigation extent (GMIE) at a 30-m resolution was 23.38% of global cropland in 2010–2019, with an overall accuracy of 83.6% globally and significant regional differences in irrigation proportions ranging from 1.1% in western Africa to 100% in Old World deserts among the 110 IMZs and from 0.4% in Belarus to 80.2% in Pakistan and 100% in Egypt among 45 countries. The study quantitatively distinguished annually and intermittently irrigated regions, which had values of 42% and 58% of global cropland, respectively, by applying indicators. This method, using the NDVI and NDVIdev thresholds, is simple, concrete and reproducible and better for zones with homogeneous weather conditions. The study offers independent, consistent and comparable information for defining the baseline, tracking changes in irrigation infrastructure, and leading future changes in how stakeholders plan and design irrigation systems.

Fenta, A. A., A. Tsunekawa, N. Haregeweyn, H. Yasuda, M. Tsubo, P. Borrelli, T. Kawai, A. Sewale Belay, K. Ebabu, M. Liyew Berihun, et al., "Improving satellite-based global rainfall erosivity estimates through merging with gauge data", Journal of Hydrology, vol. 620, pp. 129555, 2023. AbstractWebsite

Rainfall erosivity is a key factor that influences soil erosion by water. Rain-gauge measurements are commonly used to estimate rainfall erosivity. However, long-term gauge records with sub-hourly resolutions are lacking in large parts of the world. Satellite observations provide spatially continuous estimates of rainfall, but they are subject to biases that affect estimates of rainfall erosivity. We employed a novel approach to map global rainfall erosivity based on a high-temporal-resolution (30-min), long-term (2001–2020) satellite-based precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG)—and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). We used a residual-based merging scheme to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression (GWR). The accuracy of the GWR-based merging scheme was evaluated with a 10-fold cross-validation against GloREDa stations. Based on GPM-IMERG-only, the global mean annual rainfall erosivity was estimated to be 1173 MJ mm ha−1 h−1 yr−1 with a standard deviation of 1736 MJ mm ha−1 h−1 yr−1. The mean value estimated via GPM-IMERG merged with GloREDa was 2020 MJ mm ha−1 h−1 yr−1 with a standard deviation of 3415 MJ mm ha−1 h−1 yr−1. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The underestimations were greatest in areas of high rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = −2.4%, and root mean square error = 1122 MJ mm ha−1 h−1 yr−1) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm ha−1 h−1 yr−1). Improving satellite-based global rainfall erosivity estimates through integrating with gauge data is relevant as it can contribute to enhancing soil erosion modeling and, in turn, support land degradation neutrality programs.