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Volume 5
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Correction to Remote Sens. 2013, 5(4), 1704-1733.
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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

by
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
3
Dokuchaev Soil Science Institute, Pyzhevsky per. 7, Moscow 117019, Russia
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
*
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(11), 5572-5573; https://0-doi-org.brum.beds.ac.uk/10.3390/rs5115572
Submission received: 25 October 2013 / Accepted: 25 October 2013 / Published: 28 October 2013
Browse Figure
Versions Notes
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 05572 g004 550
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.
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 05572 g004
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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MDPI and ACS Style

Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. 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. Remote Sens. 2013, 5, 5572-5573. https://0-doi-org.brum.beds.ac.uk/10.3390/rs5115572

AMA Style

Rembold F, Atzberger C, Savin I, Rojas O. 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. Remote Sensing. 2013; 5(11):5572-5573. https://0-doi-org.brum.beds.ac.uk/10.3390/rs5115572

Chicago/Turabian Style

Rembold, Felix, Clement Atzberger, Igor Savin, and Oscar Rojas. 2013. "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" Remote Sensing 5, no. 11: 5572-5573. https://0-doi-org.brum.beds.ac.uk/10.3390/rs5115572

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