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Remote Sensing 2013, 5(11), 5572-5573; doi:10.3390/rs5115572

Correction
Correction: Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704–1733
Felix Rembold 1,*, Clement Atzberger 2, Igor Savin 3 and Oscar Rojas 4
1
Institute for Environment and Sustainability, Joint Research Centre (JRC), European Commission, Via Fermi 2749, I-21027 Ispra (VA), Italy
2
Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU), Vienna, Peter Jordan Strasse 82, A-1190 Vienna, Austria; E-Mail: clement.atzberger@boku.ac.at
3
V.V. Dokuchaev Soil Science Institute, Pyzhevsky per. 7, Moscow 117019, Russia; E-Mail: savigory@gmail.com
4
Food and Agriculture Organization of the United Nations (FAO), Natural Resources Management and Environment Department, Via Terme di Caracalla 1, I-00600 Rome, Italy; E-Mail: Oscar.rojas@fao.org
*
Author to whom correspondence should be addressed; E-Mail: felix.rembold@jrc.ec.europa.eu; Tel.: +39-332-786-337; Fax: +39-332-789-029.
Received: 25 October 2013 / Accepted: 25 October 2013 / Published: 28 October 2013

Due to an oversight by the authors, in the upper graph in Figure 4 [1] only the determination coefficients for Morocco are correct. Those for the other three countries are wrong. The authors wish to correct this mistake (see the following Figure 4).

Remotesensing 05 05572f4 1024
Figure 4. NDVI/yield linear regressions for cereals in North Africa (from Maselli and Rembold [46]; modified). (Top) Evolution of the coefficient of determination (R2) between radiometric variable and yield over time. (Bottom) Scatter plots between NDVI and cereal yield. Each dot corresponds to the annual yield for agricultural areas at national level and to the monthly NDVI best correlated to yield.

Click here to enlarge figure

Figure 4. NDVI/yield linear regressions for cereals in North Africa (from Maselli and Rembold [46]; modified). (Top) Evolution of the coefficient of determination (R2) between radiometric variable and yield over time. (Bottom) Scatter plots between NDVI and cereal yield. Each dot corresponds to the annual yield for agricultural areas at national level and to the monthly NDVI best correlated to yield.
Remotesensing 05 05572f4 1024

The authors apologize for the inconvenience.

Reference

  1. Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens 2013, 5, 1704–1733. [Google Scholar]
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