Next Article in Journal
Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation
Previous Article in Journal
Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks
Previous Article in Special Issue
Spatial and Temporal Pasture Biomass Estimation Integrating Electronic Plate Meter, Planet CubeSats and Sentinel-2 Satellite Data
Open AccessArticle

A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the ‘Cipolla Rossa di Tropea’ (Italy)

1
Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Località Feo di Vito, I-89122 Reggio Calabria, Italy
2
Plant Protection Department, Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), 28006 Madrid, Spain
3
Department of Agricultural, Food, and Environmental Sciences, University of Perugia, 06121 Perugia, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3424; https://doi.org/10.3390/rs12203424
Received: 21 September 2020 / Revised: 14 October 2020 / Accepted: 16 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Digital Agriculture)
Precision agriculture (PA) is a management strategy that analyzes the spatial and temporal variability of agricultural fields using information and communication technologies with the aim to optimize profitability, sustainability, and protection of agro-ecological services. In the context of PA, this research evaluated the reliability of multispectral (MS) imagery collected at different spatial resolutions by an unmanned aerial vehicle (UAV) and PlanetScope and Sentinel-2 satellite platforms in monitoring onion crops over three different dates. The soil adjusted vegetation index (SAVI) was used for monitoring the vigor of the study field. Next, the vigor maps from the two satellite platforms with those derived from UAV were compared by statistical analysis in order to evaluate the contribution made by each platform for monitoring onion crops. Besides, the two coverage’s classes of the field, bare soil and onions, were spatially identified using geographical object-based image classification (GEOBIA), and their spectral contribution was analyzed comparing the SAVI calculated considering only crop pixels (i.e., SAVI onions) and that calculated considering only bare soil pixels (i.e., SAVI soil) with the SAVI from the three platforms. The results showed that satellite imagery, coherent and correlated with UAV images, could be useful to assess the general conditions of the field while UAV permits to discriminate localized circumscribed areas that the lowest resolution of satellites missed, where there are conditions of inhomogeneity in the field, determined by abiotic or biotic stresses. View Full-Text
Keywords: spatial resolution; mixed pixels; vegetation indices (VIs); soil adjusted vegetation index (SAVI); geographical object-based image classification (GEOBIA); correlation analysis; onion crops; bare soil spatial resolution; mixed pixels; vegetation indices (VIs); soil adjusted vegetation index (SAVI); geographical object-based image classification (GEOBIA); correlation analysis; onion crops; bare soil
Show Figures

Graphical abstract

MDPI and ACS Style

Messina, G.; Peña, J.M.; Vizzari, M.; Modica, G. A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the ‘Cipolla Rossa di Tropea’ (Italy). Remote Sens. 2020, 12, 3424.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop