AI-Driven Hyperspectral and Multimodal Remote Sensing for Agriculture and Vegetation Monitoring

Dear Colleagues,

Hyperspectral remote sensing (HRS) plays a critical role in precision agriculture and environmental monitoring by capturing unique biochemical and biophysical properties of vegetation and soil. With active missions such as EMIT, DESIS, EnMAP, and PRISMA, and anticipated launches including NASA’s SBG and the commercial Planet Tanager constellation, spatial and temporal coverage is rapidly expanding. This rich, fine-grained spectral information enables detailed characterization of crop, vegetation, and soil conditions, supporting applications such as early detection of plant stress and disease, crop phenotyping, nutrient and water status assessment, soil characterization, biomass estimation, and carbon monitoring. Furthermore, multimodal remote sensing that integrates hyperspectral imagery with multispectral, thermal, LiDAR, and radar data acquired from UAVs, airborne platforms, and satellite platforms provides complementary structural, biochemical, and temporal information. Together, these technologies are transforming agricultural and environmental monitoring by enabling applications such as optimizing crop water productivity, tracking carbon sequestration, identifying drought-resistant varieties, estimating nitrogen use efficiency, and supporting sustainable land management. At the same time, advances in cloud computing, deep learning, foundation models, and multimodal artificial intelligence are creating unprecedented opportunities to extract actionable information from these large, heterogeneous datasets. However, robust, scalable, and field-validated AI methods that generalize across sensors, crops, regions, and environmental conditions remain a significant challenge. This Special Issue invites contributions that advance AI-driven hyperspectral and multimodal remote sensing for agriculture and environmental applications, fostering innovative methodologies and real-world solutions that support resilient food systems, sustainable resource management, and climate adaptation.

This Special Issue aims to advance the state of the art in AI-driven hyperspectral and multimodal remote sensing for agriculture and environmental monitoring. It welcomes contributions that develop novel sensing strategies, data fusion frameworks, and artificial intelligence methods—including deep learning, multimodal learning, and foundation model adaptation—for applications such as crop and vegetation monitoring, plant stress and disease detection, crop phenotyping, soil characterization, biomass and carbon estimation, water and nutrient assessment, and sustainable land management. The Special Issue also encourages studies that integrate data from hyperspectral, multispectral, thermal, LiDAR, and radar sensors across UAV, airborne, and satellite platforms. The topic aligns directly with the scope of the Remote Sensing in Agriculture and Vegetation Section by bridging methodological advances in remote sensing and AI with practical applications that support precision agriculture, ecosystem monitoring, and climate-resilient resource management.

Suggested themes include: hyperspectral and multispectral early stress detection; multimodal/multi-sensor data fusion (UAV, satellite, ground-based); deep learning and foundation models for spectral–temporal analysis; explainable AI for interpretable stress diagnostics; benchmark datasets and open tools; crop- and disease-specific case studies; and scalability from plot to landscape level. We welcome original research articles, review papers, technical notes, and dataset papers.

Dr. Felipe Alencar Lopes
Dr. Alifu Haireti
Dr. Itiya P. Aneece
Guest Editors

Manuscript Submission Information

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