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Special Issue "Remote Sensing for Precision Nitrogen Management"

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 2019

Special Issue Editors

Guest Editor
Dr. Yuxin Miao

Precision Agriculture Center, University of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108, USA
Website | E-Mail
Interests: precision agriculture; remote sensing-based precision nitrogen management; combining crop growth modeling and remote sensing for precision crop management; food security and sustainable development
Guest Editor
Dr. Raj Khosla

Department of Soil and Crop Sciences, Colorado State University, 307 University Ave., Fort Collins, CO 80523, USA
Website | E-Mail
Interests: precision agriculture; management zone; precision nitrogen management
Guest Editor
Dr. David J. Mulla

Precision Agriculture Center, University of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108, USA
Website | E-Mail
Interests: precision agriculture; geospatial modeling and analysis; remote sensing; precision conservation

Special Issue Information

Dear Colleagues,

Nitrogen is the most widely used macro nutrient in the world. Agriculture is a major source of N2O emissions in the biosphere. Precision nitrogen management is an important area of advanced nutrient management as well as precision agriculture for solving problems in food and environmental security for sustainable agricultural and social development. Precision nitrogen management aims to match nitrogen supply with crop N demand in both space and time to ensure high crop yield while increasing N use efficiency and protecting the environment. It is a research area that involves management zone delineation, proximal and remote sensing, crop growth modeling, spatial statistics, variable rate technology, agronomy, soil science, meteorology, plant nutrition and greenhouse gas emission mitigation, etc.

Remote sensing is one of the key supporting technologies for precision agriculture, and advances of proximal and remote sensing technologies have greatly contributed to the development of precision nitrogen management. To help readers keep up with the progresses on the applications of proximal canopy sensors, UAV-based remote sensing, aerial remote sensing and satellite remote sensing in precision nitrogen management of cereal crops, vegetables and fruit trees, etc., we would like to invite you to submit research and review papers on the following topics:

  • Proximal and remote sensing-based non-destructive diagnosis of crop nitrogen status
  • Proximal and remote sensing-based in-season variable rate nitrogen recommendation algorithms and precision management strategies
  • Remote sensing-based site-specific management zone delineation and evaluation for precision nitrogen management
  • Simultaneous diagnosis of crop nitrogen stress and other stress factors (other nutrients, water, disease, insect damage, etc.)
  • Combining remote sensing and crop growth modeling for precision nitrogen management
  • Data fusion of sensing and other related data for precision nitrogen management
  • Integration of sensing technology-based precision nitrogen management with other high yield crop management technologies
  • Evaluation of sensing technology-based precision nitrogen and crop management strategies using field experiments and on-farm trials
  • New sensing technologies for precision nitrogen management

Dr. Yuxin Miao
Dr. Raj Khosla
Dr. David J. Mulla
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 monthly 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 1800 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

  • Precision nitrogen management
  • Active canopy sensing
  • UAV remote sensing
  • Aerial and satellite remote sensing
  • In-season nitrogen status diagnosis
  • In-season site-specific nitrogen management
  • Nitrogen use efficiency
  • Crop growth modeling
  • Food security
  • Sustainable development
  • Precision agriculture
  • Management zone

Published Papers (1 paper)

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Research

Open AccessArticle Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice
Remote Sens. 2018, 10(6), 824; https://doi.org/10.3390/rs10060824 (registering DOI)
Received: 3 April 2018 / Revised: 19 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
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Abstract
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three
[...] Read more.
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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