I am an Assistant Professor at the University of Isfahan, Faculty of Civil Engineering and Transportation, Department of Geomatics Engineering, Isfahan, Iran. I received my Ph.D. degree in Remote Sensing Engineering in 2018, at the University of Tehran. From 2018 to 2019, I was employed as a Postdoctoral Researcher at the Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Tehran, Iran. Please search on my website to find more about my resume.
- Optical and radar remote sensing
- Land-cover and land-use classification
- Agricultural crop mapping
- Resource management and environmental hazards monitoring
Journal Reviewer:
- Remote Sensing of Environment
- ISPRS Journal of Photogrammetry and Remote Sensing
- IEEE Transactions on Geoscience and Remote Sensing
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- IEEE Geoscience and Remote Sensing Letters
- Photogrammetric Engineering and Remote Sensing
- International Journal of Remote Sensing
- Canadian Journal of Remote Sensing
- GIScience & Remote Sensing
- Remote Sensing
- Geocarto International
- ISPRS International Journal of Geo-Information
- Journal of the Indian Society of Remote Sensing
- Arabian Journal of Geosciences
- Geo-spatial Information Science
- Sensors
- SoftwareX
- Agriculture
- Agronomy
- Mathematics
- Applied Sciences
- Forests
- Sustainability
- AgriEngineering
- Surveying & Geomatics
- Remote Sensing
- Photogrammetry
- Digital Image Processing
- Pattern Recognition, Machine Learning, Data Mining
Articles
Remote Sensing Applications: Society and Environment (23529385)38
Crop mapping, vital for informed decision-making in agricultural and food planning, relies on accurate and current information about the distribution of agronomic lands. Remote Sensing and Earth Observation technologies have emerged as indispensable tools, providing up-to-date data and images in diverse spatial and temporal resolutions, offering a practical and cost-effective alternative to traditional methods. This paper surveys over 400 publications spanning four decades, with a notable increase in studies after 2010, focusing on crop mapping and monitoring using remote sensing imagery. Categorizing these studies based on the type of remote sensing data utilized—optical, radar, or a combination thereof—it also delves into the diverse strategies employed, including attributes used, processing units, and classification algorithms. To date, there has not been a comprehensive review study specifically focused on crop mapping. This paper emphasizes the innovations and advancements in remote sensing technologies and their applications in crop mapping. It highlights the integration of cutting-edge deep learning techniques, the utilization of high-resolution satellite data, and the development of hybrid models that combine multiple data sources for enhanced accuracy. Furthermore, this review identifies emerging trends and future directions in the field, offering insights into the potential of new technologies and methodologies. Through this comprehensive overview of crop mapping studies published in reputable scientific journals between 1980 and 2024, we illuminate the dynamic landscape of this field and underscore the unique contributions of our review to the existing body of literature. © 2025 Elsevier B.V.
European Journal of Remote Sensing (22797254)58(1)
The Ministry of Agriculture-Jihad (MAJ) and the Iranian Space Agency (ISA) aim to accurately estimate the cultivated area of strategic crops and evaluate their annual yield through meticulous crop mapping. However, Iran lacks a comprehensive, integrated approach using remote sensing and machine learning for this purpose. This study addressed this gap by developing a versatile, user-friendly crop mapping framework for Iran, utilizing Landsat-8 time series data and classical machine learning algorithms. Marvdasht in Fars province was selected as the pilot area due to its high diversity of agricultural crop types and its status as a significant agricultural hub in Iran. Furthermore, the most widely used and flexible methods available in crop mapping studies such as decision tree (DT), random forest (RF), rotation forest (RoF), support vector machine (SVM), and dynamic time warping (DTW) were used in this study. The results showed that the DTW and RF methods outperformed others, achieving approximately 96% accuracy and improving overall accuracy by 8% in creating the crop map for the pilot area. Additionally, this study demonstrated the effectiveness of Landsat-8 bands 2 to 5 along with the normalized difference vegetation index (NDVI) in reliably identifying all crops in the region. The proposed framework shows promise for significantly advancing crop mapping practices in Iran. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ISPRS International Journal of Geo-Information (22209964)12(11)
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with the traditional data generation methods. These methods have been employed to tackle the imbalanced nature of training data. Nevertheless, they still face limitations in effectively handling minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that the traditional methods struggle to overcome. In this research, we explore the effectiveness of a conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, for addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with a higher quality, which can significantly increase the number of samples for minority classes, leading to a better performance of crop classifiers. For instance, according to the G-mean metric, we observed notable improvements in the performance of the XGBoost classifier of up to 5% for minority classes. Furthermore, the statistical characteristics of the synthetic data were similar to real data, demonstrating the fidelity of the generated samples. Thus, CTGAN can be employed as a solution for addressing the scarcity of training data for minority classes in crop classification using SAR–optical data. © 2023 by the authors.
