Advances in Hyperspectral Data Analysis for Vegetation and Soil Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 25 November 2025 | Viewed by 3
Special Issue Editor
Interests: hyperspectral remote sensing; temporal spectrum; precision agriculture; multidimensional analysis technology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral remote sensing can capture detailed spectral information across contiguous bands, which makes it indispensable for applications in precision agriculture, soil health assessment, and ecosystem monitoring. This capability enables the precise identification of biochemical and biophysical properties in vegetation (e.g., chlorophyll content, water stress, and disease detection) and soil (e.g., moisture, organic carbon, and contaminant levels). Recent advancements in data acquisition platforms—including UAVs, airborne sensors, and satellite constellations—and in new data mining technologies such as deep learning provide opportunities for the efficient processing of large amounts of hyperspectral images, opening new avenues for large-scale monitoring. These tools enable automated feature extraction, classification, and anomaly detection with unprecedented accuracy, even in heterogeneous landscapes. Additionally, the fusion of hyperspectral data with complementary sources—such as LiDAR, multispectral imagery, or IoT-based ground sensors—promises to improve model interpretability and decision-making. However, challenges still remain, such as hyperspectral data dimensionality reduction, complex interactions between vegetation and soil, and model universality and robustness.
This Special Issue aims at bringing together advanced research, innovative methodologies, and applications of hyperspectral remote sensing for vegetation and soil monitoring.
This Special Issue welcomes high-quality original research articles and reviews that explore recent advancements in hyperspectral remote sensing for vegetation and soil monitoring, including, but not limited to, the following:
- Machine learning and deep learning approaches for hyperspectral data processing;
- Hyperspectral dimensionality reduction techniques;
- Hyperspectral analysis for vegetation health and stress detection;
- Vegetation phenology monitoring;
- Estimation of soil properties (e.g., organic matter, moisture, and texture);
- Soil degradation detection;
- Soil–vegetation interactions and their implications for ecosystem health.
Prof. Dr. Lifu Zhang
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- hyperspectral remote sensing
- vegetation phenology
- soil properties
- vegetation health
- soil degradation
- precision agriculture
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