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Remote Sensing
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Advances in Remote Sensing for Smart Agriculture and Digital Twins
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Advances in Remote Sensing for Smart Agriculture and Digital Twins

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 February 2027 | Viewed by 2484

Editors

Dr. Lei Hu

E-Mail Website
Guest Editor
Faculty of Resources and Environmental Science, Hubei University, 368 Youyi Road, Wuhan 430062, China
Interests: agro-geoinformatics; agricultural disasters; geospatial interoperability and standards; EO systems
Special Issues, Collections and Topics in MDPI journals
Dr. Li Lin

E-Mail Website
Guest Editor
College of Humanities and Social Sciences, United Arab Emirates University, Sheik Khalifa Bin Zayed Street, Al Ain P.O. Box 15551, AZ, United Arab Emirates
Interests: agro-geoinformatics; land use land cover change; geospatial information interoperability
Special Issues, Collections and Topics in MDPI journals
Dr. Chen Zhang

E-Mail Website
Guest Editor
Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA
Interests: geographic information science (GIS); remote sensing; agro-geoinformatics; crop mapping; geospatial cyberinfrastructure; digital twin; land use and land cover change; machine learning; image processing; geospatial data interoperability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In total, 2.6 billion people make their livelihoods mostly from agriculture. In developing countries, agriculture accounts for 29% of GDP and 65% of jobs, making the sustained productivity of agricultural systems critical for ensuring world food security and economic sustainability. In recent decades, remote sensing and GIS technologies have emerged as valuable tools for agricultural monitoring and management. Moreover, advancements in information technologies, such as artificial intelligence (AI), Internet of Things (IoT), cloud computing, cyberinfrastructure, and digital twin (DT), have provided promising strategies to enhance crop management decisions by adopting a science-based, data-driven approach.

With this background in mind, we invite submissions for this Special Issue that focus on innovative approaches and applications related to Earth system digital twins for smart agriculture. Original research and review articles will be considered that address various aspects of continuous Earth observation platforms, FAIR agricultural data services, agriculture information models, and geospatial intelligence tools.

Potential topics include, but are not limited to the following:

  • Remote sensing-based crop monitoring and mapping;
  • Remote sensing-based agricultural disaster risk identification, emergency response, and impact assessment;
  • Agricultural monitoring and decision support systems;
  • Agricultural environments and public health;
  • Conceptual frameworks for smart agriculture;
  • Interoperable agriculture information models;
  • FAIR data services in agricultural applications;
  • GeoDataCube for agricultural data management;
  • Analysis-ready data (ARD) products for agricultural applications;
  • Decision-ready information (DRI) for crop management;
  • Machine learning, deep learning, and AI-based methods in smart agriculture;
  • Geospatial infrastructures, platforms, and systems to support agricultural sustainability;
  • IoT-based smart farming;
  • EO data-driven approaches in agricultural applications.

Dr. Lei Hu
Dr. Li Lin
Dr. Chen Zhang
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-anonymized 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

  • remote sensing
  • GIS
  • agro-geoinformatics
  • smart agriculture
  • digital twin
  • disaster risk reduction
  • reproducible EO science
  • FAIR

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

28 pages, 80675 KB  
Article
Multi-Platform LiDAR Comparative Assessment for Aboveground Biomass and Carbon Estimation in Mediterranean Woody Crops
by Mateo Pastrana, Cristina Velilla, Nelson Mattié, Alfonso Gómez and Sergio Molina
Remote Sens. 2026, 18(16), 2802; https://doi.org/10.3390/rs18162802 - 19 Aug 2026
Viewed by 656
Abstract
Reliable aboveground biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In [...] Read more.
Reliable aboveground biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy-cover proxies) were extracted from normalized point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). TreeQSM closely matched the field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Smart Agriculture and Digital Twins)
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25 pages, 4670 KB  
Article
An Efficient Remote Sensing Index for Soybean Identification: Enhanced Chlorophyll Index (NRLI)
by Dongmei Lyu, Chenlan Lai, Bingxue Zhu, Zhijun Zhen and Kaishan Song
Remote Sens. 2026, 18(2), 278; https://doi.org/10.3390/rs18020278 - 14 Jan 2026
Cited by 2 | Viewed by 1283
Abstract
Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we [...] Read more.
Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Smart Agriculture and Digital Twins)
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