Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-28T01:39:34.987Z Has data issue: false hasContentIssue false

Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study

Published online by Cambridge University Press:  01 June 2017

A. Escolà*
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
N. Badia
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Environmental and Soil Sciences, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
J. Arnó
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
J. A. Martínez-Casasnovas
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Environmental and Soil Sciences, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
*
Get access

Abstract

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models. Yield data were acquired with a yield monitor installed in a combine harvester. Green-based vegetation indices performed slightly better. However, the highest correlation coefficient was 0.48. Better results may be achieved with earlier imagery and other vegetation indices. Sentinel-2 is a promising tool for precision agriculture in large arable crop fields.

Type
Satellite Applications
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Gaadi, KA, Hassaballa, AA, Tola, E, Kayad, AG, Madugundu, R, Alblewi, B et al 2016. Prediction of potato crop yield using precision agriculture techniques. PLoS ONE 11 (9).CrossRefGoogle ScholarPubMed
Congedo, L 2016. Semi-Automatic Classifiation Plugin Documentation. https://fromgistors.blogspot.com/p/semi-automatic-classification-plugin.html (retrieved 5/12/2016).Google Scholar
Drusch, M, Del Bello, U, Carlier, S, Colin, O, Fernandez, V, Gascon, F et al 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment 120, 2536.CrossRefGoogle Scholar
ESA 2016. Copernicus. Observing the Earth. http://www.esa.int/Our_Activities//Observing_the_Earth/Copernicus/Overview4 (retrieved 5/12/2016).Google Scholar
Gitelson, AA 2004. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. Journal of Plant Physiology 161, 165173.Google Scholar
Gitelson, AA, Kaufman, J and Merzlyak, MN 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58 (3), 289298.Google Scholar
Kaneko, E, Aoki, H and Tsukada, M 2016. Image-based path radiance estimation guided by physical model. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 6942–6945.Google Scholar
Klug, P, Schlenz, F, Hank, T, Migdall, S, Weiß, I, Danner, M et al 2016. Implementation of Sentinel-2 data in the M4Land system for the generation of continuous information products in agriculture. In: Living Planet Symposium 2016 (Vol. SP-740). European Space Agency.Google Scholar
Lantzanakis, G, Mitraka, Z and Chrysoulakis, N 2016. Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. In: Themistocleous K, Hadjimitsis D G, Michaelides S and Papadavid G (Eds.), International Society for Optics and Photonics p. 96880A.CrossRefGoogle Scholar
Lilienthal, H, Gerighausen, H and Schnug, E 2016. First experiences with the European remote sensing satellites Sentinel-1A / -2A for agricultural research. In: 13th International conference on Precision Agriculture, ISPA, Monticello, IL, USA, pp. 1–11.Google Scholar
Minasny, B, McBratney, AB and Whelan, BM 2005. VESPER version 1.62. Australian Centre for Precision Agriculture, McMillan Building A05, The University of Sydney, NSW 2006 http://www.usyd.edu.au/su/agric/acpa.CrossRefGoogle Scholar
Nazeer, M, Nichol, JE and Yung, YK 2014. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International Journal of Remote Sensing 35 (16), 62716291.Google Scholar
Rouse, JW Jr, Haas, RH, Deering, DW, Schell, JA and Harlan, JC 1974. Monitoring the Vernal Advancement and Retrogradation (GreenWave Effect) of Natural Vegetation, NASA/GSFC Type III Final Report: Greenbelt, MD, USA, 371p.Google Scholar
Soil Survey Staff 2014. Keys to Soil Taxonomy, 12th ed. USDA-Natural Resources Conservation Service, Washington, DC, USA.Google Scholar
Sripada, RP, Heiniger, RW, White, JG and Meijer, AD 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal 98, 968977.Google Scholar
Taylor, JA, McBratney, AB and Whelan, BM 2007. Establishing management classes for broadacre grain production. Agronomy Journal 99, 13661376.Google Scholar