Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland

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: closed (30 June 2021) | Viewed by 16121

Special Issue Editors

Dr. Manuel Ángel Aguilar

E-Mail Website
Guest Editor
Engineering Department, University of Almería, Carretera de Sacramento s/n. La Cañada de San Urbano, 04120 Almería, Spain
Interests: remote sensing; optical satellite imagery; greenhouse mapping; DSM extraction from satellite imagery; greenhouse crops monitoring; OBIA
Prof. Dr. Fernando José Aguilar

E-Mail Website1 Website2
Guest Editor
Engineering Department, University of Almería, Carretera de Sacramento s/n. La Cañada de San Urbano, 04120 Almería, Spain
Interests: remote sensing; satellite imagery; OBIA; DEM quality; greenhouse mapping; greenhouse crop monitoring; forests
Special Issues, Collections and Topics in MDPI journals
Dr. Dilek Koc-San

E-Mail Website
Guest Editor
Department of Urban and Regional Planning, Faculty of Architecture, Akdeniz University, 07058 Antalya, Turkey
Interests: remote sensing; GIS; object extraction; digital image processing; urban planning
Dr. Mimoun Chourak

E-Mail Website
Guest Editor
Mechanics and Applied Mathematics Department, Industrial and Seismic Engineering Research Team, National School of Applied Sciences of Oujda, Mohammed First University, Oujda, Morocco
Interests: modeling of earth structures by wide-angle seismic profiling and surface wave dispersion; forward and inverse modeling; elastic, anelastic, and anisotropic media; near-surface seismic velocity modelling; seismic tomography; risk seismic; tsunami; seismic hazard; seismic vulnerability
Prof. Dr. Eufemia Tarantino

E-Mail Website1 Website2
Guest Editor
Department of Civil, Environmental, Land, Construction and Chemistry (DICATECh), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Interests: geomatics; optical remote sensing; pixel-based and geographic object-based image analysis (GEOBIA); UAV applications; digital photogrammetry and spatial analysis; methodologies for multi-temporal analysis (change detection) and classification of optical satellite sensor data aimed at environmental, agricultural and cultural heritage monitoring and documentation
Special Issues, Collections and Topics in MDPI journals
Dr. Abderrahim Nemmaoui

E-Mail Website
Guest Editor
Department of Engineering, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
Interests: remote sensing; satellite imagery; DEM; geomatics; greenhouse mapping; crop detection; forests

Special Issue Information

Dear colleagues,

The use of plastic materials in agriculture during the past 70 years as a tool to move up the first harvest and increase crops’ yield, both those of fruits and vegetables, has been steadily increasing throughout the world. According to reports, the total area of agriculture plastic film has been expanded at an average rate of 20% per year globally over the last decade, being widely used for covering greenhouses, medium or low tunnels, and for mulching. In this way, plastic covered greenhouses have reached a total coverage of 3019 x 103 hectares over the world, mainly localized in China (91.4%), Korea (1.9%), Spain (1.7%), Japan (1.6%), Turkey (1.1%), and Italy (0.9%). In the same way, China also has the largest area of plastic-mulched farmland in the world, and that area has been growing more and more rapidly.

This expansive use of plastic film in agriculture is provoking important environmental and management problems. In this sense, accurate spatiotemporal mapping of plastic film (greenhouses and plastic-mulched farmland LULC) and monitoring of greenhouse crops (crop identification, phenological status, water needs, etc.) would be helpful for both farmers and decision and policy makers. Thus, an increasing amount of scientific literature has been published during the last decade focused on agricultural plastic covered areas using remote sensing.

This Special Issue will report the latest advances and trends in the field of remote sensing for mapping agricultural greenhouses and plastic-mulched farmland, addressing both original developments, new applications, and practical solutions to open questions. Topics for this Special Issue include but are not limited to the following:

  • Application of radar and/or optical satellite sensors, from very high to medium spatial resolution, for mapping greenhouses and plastic-mulched farmland;
  • Identification of crops that are growing under plastic coverings;
  • Integration of multi-temporal multisensor satellite imagery;
  • Spectral metrics specially developed for the detection of plastic covers;
  • Object-based image analysis (OBIA) applied to mapping greenhouses and plastic-mulched farmland: Segmentation and classification;
  • Generation and use of digital surface models for improving greenhouse 2D and 3D mapping;
  • Fusion and integration of data and information from multiple sensors (spectral and hyperspectral data, LiDAR, SAR, inSAR) and platforms (UAV, satellite, aircraft, ground vehicles) for mapping and monitoring greenhouse crops;
  • Machine learning and deep learning approaches for greenhouse mapping and greenhouse crops monitoring from remote-sensed data.

Papers must be original contributions, not previously published or submitted to other journals. Submissions based on previous published or submitted conference papers may be considered provided they are considerably improved and extended.

Dr. Manuel Ángel Aguilar
Dr. Fernando José Aguilar
Dr. Dilek Koc-San
Dr. Mimoun Chourak
Dr. Eufemia Tarantino
Dr. Abderrahim Nemmaoui
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
  • optical satellite imagery
  • SAR satellite imagery
  • greenhouse mapping
  • greenhouse crops monitoring
  • plastic mulch crops
  • deep learning
  • machine learning
  • OBIA
  • evapotranspiration of greenhouse crops

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 14587 KiB  
Article
Mapping Plastic Greenhouses with Two-Temporal Sentinel-2 Images and 1D-CNN Deep Learning
by Haoran Sun, Lei Wang, Rencai Lin, Zhen Zhang and Baozhong Zhang
Remote Sens. 2021, 13(14), 2820; https://doi.org/10.3390/rs13142820 - 18 Jul 2021
Cited by 23 | Viewed by 3535
Abstract
Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of [...] Read more.
Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland)
►▼ Show Figures

Figure 1

16 pages, 10952 KiB  
Article
Evaluation of Object-Based Greenhouse Mapping Using WorldView-3 VNIR and SWIR Data: A Case Study from Almería (Spain)
by Manuel A. Aguilar, Rafael Jiménez-Lao and Fernando J. Aguilar
Remote Sens. 2021, 13(11), 2133; https://doi.org/10.3390/rs13112133 - 28 May 2021
Cited by 15 | Viewed by 2725
Abstract
Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and [...] Read more.
Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR); (ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland)
►▼ Show Figures

Figure 1

20 pages, 2303 KiB  
Article
Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland: An Analysis of Worldwide Research
by Rafael Jiménez-Lao, Fernando J. Aguilar, Abderrahim Nemmaoui and Manuel A. Aguilar
Remote Sens. 2020, 12(16), 2649; https://doi.org/10.3390/rs12162649 - 17 Aug 2020
Cited by 38 | Viewed by 5028
Abstract
The total area of plastic-covered crops of 3019 million hectares has been increasing steadily around the world, particularly in the form of crops maintained under plastic-covered greenhouses to control their environmental conditions and their growth, thereby increasing production. This work analyzes the worldwide [...] Read more.
The total area of plastic-covered crops of 3019 million hectares has been increasing steadily around the world, particularly in the form of crops maintained under plastic-covered greenhouses to control their environmental conditions and their growth, thereby increasing production. This work analyzes the worldwide research dynamics on remote sensing-based mapping of agricultural greenhouses and plastic-mulched crops throughout the 21st century. In this way, a bibliometric analysis was carried out on a total of 107 publications based on the Scopus database. Different aspects of these publications were studied, such as type of publication, characteristics, categories and journal/conference name, countries, authors, and keywords. The results showed that “articles” were the type of document mostly found, while the number of published documents has exponentially increased over the last four years, growing from only one document published in 2001 to 22 in 2019. The main Scopus categories relating to the topic analyzed were Earth and Planetary Sciences (53%), Computer Science (30%), and Agricultural and Biological Sciences (28%). The most productive journal in this field was “Remote Sensing”, with 22 documents published, while China, Italy, Spain, USA, and Turkey were the five countries with the most publications. Among the main research institutions belonging to these five most productive countries, there were eight institutions from China, four from Italy, one from Spain, two from Turkey, and one from the USA. In conclusion, the evolution of the number of publications on Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland found throughout the period 2000–2019 allows us to classify the subject studied as an emerging research topic that is attracting an increasing level of interest worldwide, although its relative significance is still very limited within the remote sensing discipline. However, the growing demand for information on the arrangement and spatio-temporal dynamics of this increasingly important model of intensive agriculture is likely to drive this line of research in the coming years. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland)
►▼ Show Figures

Graphical abstract

22 pages, 5245 KiB  
Article
Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses
by Manuel Ángel Aguilar, Rafael Jiménez-Lao, Abderrahim Nemmaoui, Fernando José Aguilar, Dilek Koc-San, Eufemia Tarantino and Mimoun Chourak
Remote Sens. 2020, 12(12), 2015; https://doi.org/10.3390/rs12122015 - 23 Jun 2020
Cited by 23 | Viewed by 3460
Abstract
Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and [...] Read more.
Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop