Journal Browser

Journal Browser

Special Issue "Smart Agriculture Based on Remote Sensing and Artificial Intelligence"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 15 December 2023 | Viewed by 1097

Special Issue Editors

Dr. Chongya Jiang
E-Mail Website
Guest Editor
College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: crop growth monitoring
Dr. Zhan Li
E-Mail Website
Guest Editor
BASF Digital Farming GmbH, Im Zollhafen 24, 50678 Köln, Germany
Interests: crop mapping
Dr. Wang Zhou
E-Mail Website
Guest Editor
College of Agriculture, Sun Yat-sen University, Guangzhou 510275, China
Interests: crop modeling

Special Issue Information

Dear Colleagues,

Smart agriculture leverages remote sensing to generate high-quality, timely, and actionable insights into farmlands, leading to improved crop yield, efficient use of resources, and sustainable practices. Remote sensing data for smart agriculture have the characteristics of multiple varieties, large volumes, and diverse application requirements. It is therefore necessary to adopt innovative technologies to process and manage remote sensing data and to extract and understand agricultural information in order to provide better basis for smart agriculture decision making. The use of artificial intelligence methods, in particular, deep learning methods, has become one of the most powerful tools in recent years. The unified, reliable, and easy implementation characteristics of artificial intelligence provide a new approach to exploitation and utilization of remote sensing data for smarter agriculture.

In this context, this Special Issue aims to explore the recent advances in remote sensing technologies and applications and artificial intelligence in the agriculture domain, with a focus on crops. Papers of a theoretical, technical, and applicative nature are welcome. Data sources could be from remote sensors on various platforms including ground-based, proximal, drone, aircraft, and satellites. Topics of interest include but are not limited to:

  • AI for crop type and soil mapping;
  • AI for crop growth monitoring;
  • AI for crop growth modeling;
  • AI for crop yield prediction;
  • AI for stress/weed/disease/insect detection;
  • AI for decision making of management practices;
  • AI for tractor/robot navigation/operations;
  • AI for phenotyping.

We look forward to seeing your contributions to this Special Issue.

Dr. Chongya Jiang
Dr. Zhan Li
Dr. Zhou Zhang
Dr. Wang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • smart agriculture
  • remote sensing
  • artificial intelligence

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


22 pages, 7024 KiB  
Contrastive-Learning-Based Time-Series Feature Representation for Parcel-Based Crop Mapping Using Incomplete Sentinel-2 Image Sequences
Remote Sens. 2023, 15(20), 5009; - 18 Oct 2023
Viewed by 634
Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-series features to identify crop types from incomplete optical image [...] Read more.
Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-series features to identify crop types from incomplete optical image sequences is a significant challenge. This study developed a contrastive-learning-based framework for time-series feature representation to improve crop classification using incomplete Sentinel-2 image sequences. Central to this method was the combined use of inherent time-series feature representation and machine-learning-based classifications. First, preprocessed multi-temporal Sentinel-2 satellite images were overlaid onto precise farmland parcel maps to generate raw time-series spectral features (with missing values) for each parcel. Second, an enhanced contrastive learning model was established to map the raw time-series spectral features to their inherent feature representation (without missing values). Thirdly, eXtreme Gradient-Boosting-based and Long Short-Term Memory-based classifiers were applied to feature representation to produce crop classification maps. The proposed method is further discussed and validated through parcel-based time-series crop classifications in two study areas (one in Dijon of France and the other in Zhaosu of China) with multi-temporal Sentinel-2 images in comparison to the existing methods. The classification results, demonstrating significant improvements greater than 3% in overall accuracy and 0.04 in F1 scores over comparison methods, indicate the effectiveness of the proposed contrastive-learning-based time-series feature representation for parcel-based crop classification utilizing incomplete Sentinel-2 image sequences. Full article
(This article belongs to the Special Issue Smart Agriculture Based on Remote Sensing and Artificial Intelligence)
Show Figures

Figure 1

Back to TopTop