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Dear Colleagues,

Remote sensing involves the collection of data about the Earth’s surface from a distance using sensors mounted on satellites, aircraft, or drones. These sensors capture a wide range of information, including imagery, spectral data, and geospatial measurements. Remote sensing enables us to gather data on land cover, vegetation health, atmospheric conditions, and more. When machine learning is applied to remote sensing data, it opens up new possibilities for extracting valuable insights from the vast amount of Earth observation data. Therefore, machine learning and remote sensing are two powerful fields that have significantly contributed to advancements in various scientific fields, such as Earth observation and environmental analysis.

Thus, this Special Issue provides a platform for researchers to present their recent research progress regarding the applications of machine learning and remote sensing. We welcome various manuscript types, e.g., articles, letters, reviews, and technical reports.

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

Object detection and classification;
Data analytics in the remote sensing community;
Image intelligent processing;
Airborne and satellite systems;
Deep learning;
Signal and image processing;
Regression analysis for remote sensing data;
Feature selection, optimization, and dimensionality reduction for remote sensing data.

Dr. Mohammed Dabboor
Dr. Karantzalos Konstantinos
Prof. Ghada Atteia
Prof. Dr. Wided Lejouad Chaari
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. 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
machine learning
deep learning
image analysis
data classification
regression analysis
feature selection
artificial intelligence

Published Papers (2 papers)

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Research

40 pages, 4474 KiB

Open AccessArticle

Corn Grain Yield Prediction Using UAV-Based High Spatiotemporal Resolution Imagery, Machine Learning, and Spatial Cross-Validation

by Patrick Killeen, Iluju Kiringa, Tet Yeap and Paula Branco

Remote Sens. 2024, 16(4), 683; https://doi.org/10.3390/rs16040683 - 14 Feb 2024

Cited by 1 | Viewed by 897

Abstract

Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map’s accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields). In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery-based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models. When using high spatial resolution MS imagery, RF and LR obtained 0.81 and 0.56 correlation coefficient (CC), respectively, when using 10-fold CV, and obtained 0.39 and 0.41, respectively, when using a k-means-based spatial CV approach. Furthermore, when using only location features, RF and LR obtained an average CC of 1.00 and 0.49, respectively. This suggested that LR had better spatial generalizability than RF, and that RF was likely being over-optimistic and was overfitting to the spatial structure of the data. Full article

(This article belongs to the Special Issue Advances in the Applications of Machine Learning and Remote Sensing)

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19 pages, 3429 KiB

Open AccessArticle

High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images

by Brandon Victor, Aiden Nibali, Saul Justin Newman, Tristan Coram, Francisco Pinto, Matthew Reynolds, Robert T. Furbank and Zhen He

Remote Sens. 2024, 16(2), 282; https://doi.org/10.3390/rs16020282 - 10 Jan 2024

Viewed by 1493

Abstract

To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning. Full article

(This article belongs to the Special Issue Advances in the Applications of Machine Learning and Remote Sensing)

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