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Agronomy
Special Issues
Use of Satellite Imagery in Agriculture—Volume II
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Use of Satellite Imagery in Agriculture—Volume II

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 1580

Special Issue Editors

Dr. Riccardo Dainelli

E-Mail Website1 Website2
Guest Editor
Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: precision agriculture; satellite; unmanned aerial vehicles; image processing; cereals; vegetable crops; field variability; life cycle assessment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria-Paz Diago

E-Mail Website1 Website2
Guest Editor
1. Department of Agriculture and Food Science, University of La Rioja, 26007 Logroño, La Rioja, Spain
2. Instituto de Ciencias de la Vid y del Vino, Finca La Grajera, Ctra. Burgos Km 6, 26007 Logroño, La Rioja, Spain
Interests: precision viticulture; non-invasive sensors; vineyard monitoring; grapevine water status; grapevine composition; vineyard spatial variability; spectroscopy; thermography; vineyard robotics; machine vision; yield estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Facing climate change and an increasing population, the reduced supply of agronomic inputs and the rational exploitation of natural resources should be reconciled with high production standards and farmer income. In this context, the use of remote-sensing tools (platform and sensors) such as satellites plays a fundamental role.

With the aim of introducing cutting-edge satellite applications to the scientific community, I am pleased to invite you to submit your work to this Special Issue, “Use of Satellite Imagery in Agriculture—Volume II”. Papers may be original research articles and reviews regarding the use of optical and radar satellite images for precision agriculture applications and the sustainable development of agronomy. In particular, research areas may include (but are not limited to) the following: spatio-temporal variability in cropping systems, retrieval of soil properties, crop monitoring (e.g., phenology and stress), yield prediction, and crop recognition using artificial intelligence. Papers addressing satellite time series, data fusion, or collecting in situ data are also welcome, as well as those using a combination of remote-sensing tools where satellites represent the leading platform.

I look forward to receiving your contributions.

Dr. Riccardo Dainelli
Prof. Dr. Maria-Paz Diago
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 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. Agronomy 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 2600 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

  • precision agriculture
  • remote sensing
  • active and passive sensors
  • soil fertility and characteristics
  • crop recognition, status, and yield
  • artificial intelligence
  • management zones
  • in situ data
  • time series
  • data fusion

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Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 4865 KiB  
Article
Combining Gaussian Process Regression with Poisson Blending for Seamless Cloud Removal from Optical Remote Sensing Imagery for Cropland Monitoring
by Soyeon Park and No-Wook Park
Agronomy 2023, 13(11), 2789; https://doi.org/10.3390/agronomy13112789 - 10 Nov 2023
Cited by 2 | Viewed by 1146
Abstract
Constructing optical image time series for cropland monitoring requires a cloud removal method that accurately restores cloud regions and eliminates discontinuity around cloud boundaries. This paper describes a two-stage hybrid machine learning-based cloud removal method that combines Gaussian process regression (GPR)-based predictions with [...] Read more.
Constructing optical image time series for cropland monitoring requires a cloud removal method that accurately restores cloud regions and eliminates discontinuity around cloud boundaries. This paper describes a two-stage hybrid machine learning-based cloud removal method that combines Gaussian process regression (GPR)-based predictions with image blending for seamless optical image reconstruction. GPR is employed in the first stage to generate initial prediction results by quantifying temporal relationships between multi-temporal images. GPR predictive uncertainty is particularly combined with prediction values to utilize uncertainty-weighted predictions as the input for the next stage. In the second stage, Poisson blending is applied to eliminate discontinuity in GPR-based predictions. The benefits of this method are illustrated through cloud removal experiments using Sentinel-2 images with synthetic cloud masks over two cropland sites. The proposed method was able to maintain the structural features and quality of the underlying reflectance in cloud regions and outperformed two existing hybrid cloud removal methods for all spectral bands. Furthermore, it demonstrated the best performance in predicting several vegetation indices in cloud regions. These experimental results indicate the benefits of the proposed cloud removal method for reconstructing cloud-contaminated optical imagery. Full article
(This article belongs to the Special Issue Use of Satellite Imagery in Agriculture—Volume II)
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