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Advances in Remote Sensing for Smart Agriculture and Digital Twins

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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

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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. 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.

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25 pages, 4670 KB

Open AccessArticle

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 1 | Viewed by 565

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 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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