Special Issue "Spatial Analysis of Agricultural Data"

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

Deadline for manuscript submissions: 10 December 2021.

Special Issue Editor

Prof. Dr. Antonio López-Quílez
E-Mail Website
Guest Editor
Department of Statistics and Operational Research, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain
Interests: spatial statistics; bayesian statistics; environmental statistics; biostatistics; epidemiology
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Special Issue Information

Dear Colleagues,

In the 21st century, the global population is expected to grow to 10 billion. The question of how to increase agricultural production to achieve food security and feed a growing population is one of the greatest challenges facing humanity. This needs to be addressed while maintaining sustainable agricultural systems and simultaneously facing challenges related to climate, resources and weather events. Automation with new technologies, sensors, yield monitors, internet of things (IoT) and drones and robots, as well as the use of GIS methods, artificial intelligence (AI), highly structured mathematical models and Big Data statistical techniques, serves as the basis for a global “Digital Twin”. This conceptualization will contribute to the development of site-specific conservation and management practices that will increase the income and global sustainability of agricultural systems. The spatial analysis of agricultural data is a key element in this context.

Satellite and aerial images, sensors and yield monitors provide information about production variability at macro and micro scales, with a great amount of agricultural data to be processed, represented, modeled and understood. Spatiotemporal models seem to offer additional benefits beyond the classical, spatially explicit modeling. Hierarchical models can deal with complex interactions by specifying parameters that change on several levels via the introduction of random effects.

The spread of transboundary plant pests and diseases caused by fungi, bacteria or viruses has increased significantly in recent years. These threats are causing significant losses and impacting food security. In essence, they spread by human-migrated movement and are windborne or vector-borne. A wide range of environmental, climatic and socioeconomic factors underlie their spatial patterns. In addition, factors such as changes in climate, habits or land use intervene and complicate the understanding of these processes.

This Special Issue is intended for a wide and multidisciplinary audience and presents some of the most recent advances and novel approaches in the spatial analysis of agricultural data.

Prof. Dr. Antonio López-Quílez
Guest Editor

Manuscript Submission Information

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Keywords

  • precision agriculture
  • ICT applications
  • Internet of Things (IoT)
  • GIS applications
  • remote sensing
  • spatial statistics
  • geospatial artificial intelligence
  • clustering
  • spatial prediction

Published Papers (1 paper)

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Research

Article
Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions
Appl. Sci. 2021, 11(15), 6923; https://doi.org/10.3390/app11156923 - 28 Jul 2021
Viewed by 409
Abstract
The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, [...] Read more.
The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained. Full article
(This article belongs to the Special Issue Spatial Analysis of Agricultural Data)
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