Special Issue "Remote Sensing Applications in Agricultural Ecosystems"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Jia Yang
E-Mail Website
Guest Editor
Department of Forestry, Mississippi State University, Starkville, MS 39762, USA
Interests: remote sensing and GIS applications in land ecosystems; land cover and land use change; terrestrial ecosystem modeling; fire ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Tao
E-Mail Website
Guest Editor
Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY 40506, USA
Interests: land use/cover change; climate Change; process-based ecosystem modeling; remote sensing application
Dr. Padmanava Dash
E-Mail Website
Guest Editor
Department of Geosciences, Mississippi State University, Starkville, MS 39762, USA
Interests: remote sensing; water biogeochemistry

Special Issue Information

Dear Colleagues,

The world’s population is projected to increase continuously throughout the 21st century. To mitigate the global food security problem, it is of utmost importance to improve crop health and enhance grain yield through better agricultural management, crop cultivars, etc. Remote sensing has many advantages in monitoring crop growth at the regional and global scales and detecting crop responses to various stresses (such as droughts, pests, and limited nutrient availability) that are invisible to humans. This Special Issue aims to present a collection of papers on topics regarding remote sensing applications in agricultural ecosystems from local to regional and global scales. Acceptable topics include, but are not restricted to, crop yield prediction, nutrient limitation, cropland area change, crop phenology, agricultural drought and water stress, the carbon balance in and greenhouse gas emissions from agricultural lands, crop health assessment, agricultural fires, and crop type classification. Papers are required to include a novelty, such as a new satellite sensor or data archive, a new approach to analysis, or a novel application to improve crop monitoring and evaluation.

Dr. Jia Yang
Dr. Bo Tao
Dr. Padmanava Dash
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 papers will be 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 2400 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

  • crop growth and health condition
  • crop yield
  • crop phenology
  • drought stress and irrigation
  • agricultural management
  • crop type classification
  • carbon dynamics
  • hyperspectral remote sensing

Published Papers (3 papers)

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Research

Article
Spatiotemporal Changes and Driving Factors of Cultivated Soil Organic Carbon in Northern China’s Typical Agro-Pastoral Ecotone in the Last 30 Years
Remote Sens. 2021, 13(18), 3607; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183607 - 10 Sep 2021
Viewed by 373
Abstract
In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to [...] Read more.
In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to predict the SOC from 1989 to 2018, and the geographic detector method (GDM) was applied to analyze quantitatively the natural and anthropogenic factors that are affecting Aohan banner. The results indicated that: (1) After adding the terrain factors, the R2 and residual predictive deviation (RPD) of the RF model increased by 1.178 and 0.39%, with root mean square errors (RMSEs) of 1.42 g/kg and 1.05 g/kg, respectively; (2) The spatial distribution of SOC was higher in the south and lower in the north; the negative growth of SOC accounted for 55.923% of the total area, showing a trend of degradation; (3) Precipitation was the main driving factor of SOC spatial variation in the typical agro-pastoral ecotone of northern China, which was also affected by temperature, elevation, soil type and soil texture (p < 0.01). (4). Anthropogenic factors (carbon input and gross domestic product (GDP)) had a greater impact on SOC than did climate factors (temperature and precipitation), making anthropogenic factors the dominant factors affecting SOC temporal variation (p < 0.01). The results of this work constitute a basis for a regional assessment of the temporal evolution of organic carbon in the soil surface, which is a key tool for monitoring the sustainable development of agropastoral ecotones. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)
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Article
Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing
Remote Sens. 2021, 13(16), 3263; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163263 - 18 Aug 2021
Viewed by 435
Abstract
The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been [...] Read more.
The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)
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Article
Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images
Remote Sens. 2021, 13(15), 2992; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152992 - 29 Jul 2021
Viewed by 471
Abstract
This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order [...] Read more.
This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)
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