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Applied Sciences
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Selected Papers from The 4th China International Agricultural Remote...
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Selected Papers from The 4th China International Agricultural Remote Sensing Application and Technology Summit (ARS 2018)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (31 August 2018) | Viewed by 8011

Special Issue Editors

Dr. Xingfa Gu

E-Mail Website
Guest Editor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: quantitative remote sensing; optical satellite sensor calibration; aerosol remote sensing; earth observation system demonstration
Prof. Dr. Jean Sequeira

E-Mail Website
Guest Editor
Computer Science Department, Aix-Marseille University, 860 Allée du Garlaban, 13360 Roquevaire, France
Interests: image analysis; pattern recognition; geometrical modeling; visualization
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Yu

E-Mail Website
Guest Editor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: quantitative remote sensing; Earth observation system demonstration

Special Issue Information

Dear Colleagues,

The 4th China International Agricultural Remote Sensing Application and Technology Summit (ARS 2018) will provide an advanced forum on all aspects of agricultural remote sensing. Our aim is to create a stage for exchanging the latest research results and sharing advanced research methods on the theory, science, applications, and technology of agricultural remote sensing studies, in as much detail as possible, and to promote the development of modern agriculture and the efficient utilization of agricultural and soil and water resources.

This Special Issue, “Selected Papers from the Fourth China International Agricultural Remote Sensing Application and Technology Summit (ARS 2018)”, is expected to select excellent papers presented at ARS 2018. We invite investigators interested in agricultural remote sensing to contribute their original research articles to this Special Issue. Potential topics include, but are not limited to:

  • Agricultural remote sensing technology and regional application
  • Agricultural remote sensing industry development
  • Intelligent agriculture
  • The Belt and Road and International agricultural remote sensing
Dr. Xingfa Gu
Prof. Dr. Jean Sequeira
Dr. Tao Yu
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. Applied Sciences 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

  • Agricultural remote sensing technology in the crop area and growth monitoring
  • Agricultural remote sensing technology in the crop yield estimation
  • Agricultural remote sensing technology in the agricultural pests and diseases monitoring
  • Intelligent agriculture

Published Papers (2 papers)

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Research

12 pages, 1957 KiB  
Article
Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index
by Chunrong Qiu, Guiping Liao, Hongyuan Tang, Fan Liu, Xiaoyi Liao, Rui Zhang and Zanzhong Zhao
Appl. Sci. 2018, 8(8), 1300; https://0-doi-org.brum.beds.ac.uk/10.3390/app8081300 - 04 Aug 2018
Cited by 11 | Viewed by 4600
Abstract
AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. [...] Read more.
AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors. Full article
(This article belongs to the Special Issue Selected Papers from The 4th China International Agricultural Remote Sensing Application and Technology Summit (ARS 2018))
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20 pages, 6378 KiB  
Article
Effect of the Temporal Gradient of Vegetation Indices on Early-Season Wheat Classification Using the Random Forest Classifier
by Mousa Saei Jamal Abad, Ali A. Abkar and Barat Mojaradi
Appl. Sci. 2018, 8(8), 1216; https://0-doi-org.brum.beds.ac.uk/10.3390/app8081216 - 24 Jul 2018
Cited by 11 | Viewed by 3070
Abstract
Early-season area estimation of the winter wheat crop as a strategic product is important for decision-makers. Multi-temporal images are the best tool to measure early-season winter wheat crops, but there are issues with classification. Classification of multi-temporal images is affected by factors such [...] Read more.
Early-season area estimation of the winter wheat crop as a strategic product is important for decision-makers. Multi-temporal images are the best tool to measure early-season winter wheat crops, but there are issues with classification. Classification of multi-temporal images is affected by factors such as training sample size, temporal resolution, vegetation index (VI) type, temporal gradient of spectral bands and VIs, classifiers, and values missed under cloudy conditions. This study addresses the effect of the temporal resolution and VIs, along with the spectral and VIs gradient on the random forest (RF) classifier when missing data occurs in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, a study area is selected on an overlapping area between two Landsat Data Continuity Mission (LDCM) paths. In the proposed method, the missing data from cloudy pixels are retrieved using the average of the k-nearest cloudless pixels in the feature space. Next, multi-temporal image analysis is performed by considering different scenarios provided by decision-makers for the desired crop types, which should be extracted early in the season in the study areas. The classification results obtained by RF improved by 2.2% when the temporally-missing data were retrieved using the proposed method. Moreover, the experimental results demonstrated that when the temporal resolution of Landsat-8 is increased to one week, the classification task can be conducted earlier with slightly better overall accuracy (OA) and kappa values. The effect of incorporating VIs along with the temporal gradients of spectral bands and VIs into the RF classifier improved the OA by 3.1% and the kappa value by 6.6%, on average. The results show that if only three optimum images from seasonal changes in crops are available, the temporal gradient of the VIs and spectral bands becomes the primary tool available for discriminating wheat from barley. The results also showed that if wheat and barley are considered as single class versus other classes, with the use of images associated with 162 and 163 paths, both crops can be classified in March (at the beginning of the growth stage) with an overall accuracy of 97.1% and kappa coefficient of 93.5%. Full article
(This article belongs to the Special Issue Selected Papers from The 4th China International Agricultural Remote Sensing Application and Technology Summit (ARS 2018))
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