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Applied Sciences
Special Issues
Frontier Trends of Remote Sensing in Agriculture
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Frontier Trends of Remote Sensing in Agriculture

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 2706

Special Issue Editor

Dr. Piero Toscano

E-Mail Website
Guest Editor
Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: remote sensing; precision agriculture; crop modeling; climate services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has demonstrated great potential in providing useful information for agricultural applications at various spatial and temporal scales. At the same time, the advances in digital technologies and the application of artificial intelligence algorithms, coupled with the technological advancements in remote sensing, are creating a unique opportunity to implement new solutions that specifically address key aspects of agricultural systems. This Special Issue aims to contribute to the dissemination of pioneering research findings in the study and management of agricultural crops, with particular emphasis on the development of new and appropriate technical solutions in data acquisition and processing.

Dr. Piero Toscano
Guest Editor

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

  • remote sensing (satellite, UAV, aerial platform)
  • crop status
  • yield and quality
  • artificial intelligence
  • time series analysis
  • NRT data processing

Published Papers (1 paper)

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Research

21 pages, 5841 KiB  
Article
Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain
by Xueting Wang, Sha Zhang, Lili Feng, Jiahua Zhang and Fan Deng
Appl. Sci. 2020, 10(8), 2667; https://doi.org/10.3390/app10082667 - 13 Apr 2020
Cited by 14 | Viewed by 2225
Abstract
Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation [...] Read more.
Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops. Full article
(This article belongs to the Special Issue Frontier Trends of Remote Sensing in Agriculture)
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