Applied Remote Sensing Technology in Agriculture and Environment
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".
Deadline for manuscript submissions: 20 September 2026 | Viewed by 505
Special Issue Editors
Interests: agriculture; optical image time series; phenology; digital processing image; land use and land cover mapping
Interests: remote sensing; change detection; land use; crop; irrigation; deforestation
Special Issue Information
Dear Colleagues,
In recent decades, remote sensing has experienced significant technological progress, particularly with the emergence of new satellite missions launched by both governmental and commercial entities. These missions have led to the availability of increasingly diverse and high-resolution datasets, fostering the development of new standards for data interoperability and integration.
Concurrently, advances in computational capabilities, such as cloud-based platforms, high-performance computing infrastructures, and the use of graphical processing units (GPUs), have facilitated the efficient processing and analysis of massive volumes of Earth observation (EO) data. These computational improvements have enhanced the scalability and feasibility of remote sensing applications across various domains.
At the algorithmic level, remote sensing has increasingly intersected with computer vision and artificial intelligence (AI), enabling the development of advanced image processing, classification, and modeling techniques. In particular, deep learning, explainable AI (XAI), and spatiotemporal analysis have opened new avenues for extracting meaningful information from multi-source EO data.
This Special Issue aims to showcase recent advances and innovative applications of remote sensing technologies in agriculture and environmental sciences. We invite original research articles, technical notes, and review papers that focus on the integration of remote sensing data with novel computational techniques to address current and emerging challenges in these fields.
Dr. Hugo Do Nascimento Bendini
Dr. Leila Maria Garcia Fonseca
Guest Editors
Manuscript Submission Information
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Keywords
- applications of optical, SAR, thermal, and LiDAR data for environmental and agricultural monitoring
- integration of multi-source and multi-temporal EO data for time series analysis
- development and application of AI and deep learning models for land cover classification and pattern recognition
- explainable AI and interpretable machine learning approaches for decision support
- use of new satellite constellations and sensor technologies for precision agriculture and ecosystem assessment
- modeling of biophysical and environmental variables using remote sensing-derived products
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