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Application of Satellite and UAV Data in Precision Agriculture

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: 26 May 2024 | Viewed by 1404

Special Issue Editor


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Guest Editor
School of Environmental Sciences, Charles Sturt University, Albury, NSW, Australia
Interests: remote sensing; geospatial data; cloud computing; ICT in agriculture

Special Issue Information

Dear Colleagues,

With the increase in the world's population and the reduction in land resources, it is imperative to find a way to improve the efficiency of agricultural production and make it develop sustainably. Precision agriculture is a management strategy that supports management decisions through the collection and analysis of temporal, spatial, and ancillary data. It revolutionizes agriculture by improving productivity and reducing environmental impacts.

The goal of precision agriculture is to increase crop yields while minimizing inputs such as water, fertilizer, and pesticides. As a space–air–ground integrated information collection technology, remote sensing has the potential to provide people with detailed and accurate data, enabling precise planting and intelligent management. Satellite and UAV data are widely used in crop monitoring, providing up-to-date information on moisture stress, nutrient levels, and disease. It can provide farmers with guidance to optimize crop inputs, such as water, fertilizer, or chemicals. As technology continues to evolve, precision agriculture becomes more sophisticated, enabling farmers to achieve even greater levels of productivity and sustainability.

The Special Issue invites contributions using satellite and UAV data in precision agriculture. Topics of interest for this Special Issue include, but are not limited to:

  • Decision support systems for agricultural monitoring;
  • Water resource management;
  • IoT in agriculture;
  • Soil fertility and plant nutrition;
  • Soil moisture and plant water content;
  • Yield monitoring and mapping;
  • Insect pest monitoring and management;
  • Variable rate applications;
  • Stakeholder perception on the adoption of digital technologies in agriculture.

Dr. Mobushir Riaz Khan
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. 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

  • precision agriculture
  • decision support systems in agriculture
  • crop growth modeling
  • crop yield estimation
  • crop water stress detection
  • soil properties mapping
  • satellite and UAV data

Published Papers (1 paper)

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Research

19 pages, 11920 KiB  
Article
Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing
by Mathyam Prabhakar, Kodigal A. Gopinath, Nakka Ravi Kumar, Merugu Thirupathi, Uppu Sai Sravan, Golla Srasvan Kumar, Gutti Samba Siva, Pebbeti Chandana and Vinod Kumar Singh
Remote Sens. 2024, 16(6), 954; https://doi.org/10.3390/rs16060954 - 08 Mar 2024
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Abstract
Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands [...] Read more.
Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage. Full article
(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)
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