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Special Issue Editors

Dr. Aris Kyparissis

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Guest Editor

Department of Agricultural Crop Production and Rural Environment, University of Thessaly, Fytokou Str., 384 46 Volos, Greece
Interests: plant ecophysiology; ecosystem dynamics; remote sensing; global climate change

Dr. Chris Cavalaris

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Guest Editor

Laboratory of Farm Mechanization, University of Thessaly, Fytokou Str., 384 46 Volos, Greece
Interests: soil tillage, biomass; biofuels; energy crops; drone
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Primary ecosystem productivity is of major importance, comprising all processes affecting the function of photosynthetic organisms and their relations to biotic and abiotic environmental factors. Productivity monitoring has been enhanced during recent decades by the advances of remote sensing through satellite and—more recently—drone data. Several products with different spatial, temporal, and spectral resolutions are now available and may be used for productivity assessment through simple or complicated modelling approaches, including machine learning, artificial intelligence, and neural networks. This Special Issue welcomes contributions focusing on current and future perspectives in ecosystem productivity monitoring with satellite and drone data. The dynamics of agricultural and forest ecosystems in relation to climate change and human interventions are of major interest. Revealing their functional responses may enhance our understanding from a biological perspective, help identify potential threats in the food and natural materials chains, propose viable solutions for ecosystem sustainability, and further clarify their role as important components of the climate system on local and global scales.

Dr. Aris Kyparissis
Dr. Chris Cavalaris
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

ecosystem primary productivity
yield
satellite
drone
UAV
modelling
agricultural
forest
climate change

Published Papers (2 papers)

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Research

21 pages, 6962 KiB

Open AccessArticle

Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches

by Cesar I. Alvarez-Mendoza, Diego Guzman, Jorge Casas, Mike Bastidas, Jan Polanco, Milton Valencia-Ortiz, Frank Montenegro, Jacobo Arango, Manabu Ishitani and Michael Gomez Selvaraj

Remote Sens. 2022, 14(22), 5870; https://doi.org/10.3390/rs14225870 - 19 Nov 2022

Cited by 9 | Viewed by 3135

Abstract

Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R² = 0.60, Linear with R² = 0.54, and Extra Trees with R² = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R² of 0.76, Extra Trees with an R² of 0.75, and Bayesian Ridge with an R² of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia. Full article

(This article belongs to the Special Issue Assessing Primary Ecosystem Productivity Using Satellite and Drone Data)

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Graphical abstract

12 pages, 1302 KiB

Open AccessArticle

Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach

by Maria Bebie, Chris Cavalaris and Aris Kyparissis

Remote Sens. 2022, 14(16), 3880; https://doi.org/10.3390/rs14163880 - 10 Aug 2022

Cited by 5 | Viewed by 1488

Abstract

Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression model (VI-MLR) based on vegetation indices (EVI, NMDI) was used. In the second approach, the reflectance data of all Sentinel-2 bands for several dates during the growth periods were used as input parameters in three machine learning model algorithms, i.e., random forest (RF), k-nearest neighbors (KNN), and boosting regressions (BR). Modeling results were examined against yield data collected by a combine harvester equipped with a yield mapping system. VI-MLR showed a moderate performance with R² = 0.532 and RMSE = 847 kg ha⁻¹. All machine learning approaches enhanced model accuracy when all images during the growing periods were used, especially RF and KNN (R² > 0.91, RMSE < 360 kg ha⁻¹). Additionally, RF and KNN accuracy remained high (R² > 0.87, RMSE < 455 kg ha⁻¹) when images from the start of the growing period until March, i.e., three months before harvest, were used, indicating the high suitability of machine learning on Sentinel-2 data for early yield prediction of durum wheat, information considered essential for precision agriculture applications. Full article

(This article belongs to the Special Issue Assessing Primary Ecosystem Productivity Using Satellite and Drone Data)

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