Next Article in Journal
A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data
Next Article in Special Issue
Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China
Previous Article in Journal
Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest
Previous Article in Special Issue
Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(8), 9886-9903; doi:10.3390/rs70809886

Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards

1
KU Leuven, Department of Biosystems, Division of Crop Biotechnics, Willem de Croylaan 34, BE-3001 Leuven, Belgium
2
KU Leuven, Department of Earth and Environmental Sciences, Division of Forest, Nature and Landscape Research, Celestijnenlaan 200E, BE-3001 Leuven, Belgium
3
Pcfruit research station, Fruittuinweg 1, BE-3800 Sint-Truiden, Belgium
4
Soil Service of Belgium, Willem de Croylaan 48, BE-3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Clement Atzberger and Prasad S. Thenkabail
Received: 23 March 2015 / Revised: 24 July 2015 / Accepted: 28 July 2015 / Published: 4 August 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
View Full-Text   |   Download PDF [753 KB, uploaded 4 August 2015]   |  

Abstract

Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process. View Full-Text
Keywords: temporal dependence; fruit yield and quality estimation; pyrus communis “conference”; hyperspectral remote sensing temporal dependence; fruit yield and quality estimation; pyrus communis “conference”; hyperspectral remote sensing
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Van Beek, J.; Tits, L.; Somers, B.; Deckers, T.; Verjans, W.; Bylemans, D.; Janssens, P.; Coppin, P. Temporal Dependency of Yield and Quality Estimation through Spectral Vegetation Indices in Pear Orchards. Remote Sens. 2015, 7, 9886-9903.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top