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AgriEngineering
Special Issues
The Application of Remote Sensing for Agricultural Monitoring
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The Application of Remote Sensing for Agricultural Monitoring

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 562

Special Issue Editors

Dr. Wei Xue

E-Mail Website
Guest Editor
Colleague of Ecology and Environment, Xinjiang University, Urumqi 830017, China
Interests: retrieval of biophysical traits of crops by using remote sensing data; reconstruction of Landsat EVI trajectory
Special Issues, Collections and Topics in MDPI journals
Dr. Ruyin Cao

E-Mail Website
Guest Editor
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: algorithm development; time-series remote sensing; vegetation phenology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing techniques are powerful tools for monitoring and evaluating soil water and nutrient conditions, crop growth, and other environmental indicators. In addition to satellite remote sensing, new devices like chlorophyll fluorescence imagers/meters, close-range drones/unmanned aerial vehicles, and mobile ground robots equipped with various sensors provide vast amounts of data. This data presents significant challenges in processing, analysis, and assimilation for practical agricultural applications. With the help of advanced non-parametric regression algorithms such as machine learning and artificial intelligence, new and effective information hidden in remote sensing big data could be unraveled, allowing for the optimization of crop production at unprecedented spatial and temporal scales.

To advance and optimize technology, analysis, and monitoring methods, this Special Issue welcomes original research and comprehensive reviews on the following topics: (1) advancements in remote sensing equipment and application scenarios, (2) enhanced fusion of multi-source remote sensing data for retrieving biophysical and biochemical variables of crops and soils, (3) innovative methodologies and image analysis tools for agricultural monitoring using remote sensing data, and (4) expanding the scope of remote sensing applications in agricultural monitoring.

Dr. Wei Xue
Dr. Ruyin Cao
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. AgriEngineering is an international peer-reviewed open access monthly 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 1800 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

  • remote sensing
  • crop monitoring
  • algorithms
  • machine learning
  • artificial intelligence
  • field management

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Published Papers (1 paper)

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Research

37 pages, 28225 KB  
Article
Hierarchical Spectral Modelling of Pasture Nutrition: From Laboratory to Sentinel-2 via UAV Hyperspectral
by Jason Barnetson, Hemant Raj Pandeya and Grant Fraser
AgriEngineering 2026, 8(4), 143; https://doi.org/10.3390/agriengineering8040143 - 7 Apr 2026
Viewed by 252
Abstract
This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring [...] Read more.
This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation R2 of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation R2 was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s km2) using freely available satellite imagery and open-source machine learning frameworks. Full article
(This article belongs to the Special Issue The Application of Remote Sensing for Agricultural Monitoring)
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