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Correction published on 28 October 2013, see Remote Sens. 2013, 5(11), 5572-5573.
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Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Institute for Environment and Sustainability, Joint Research Centre (JRC), European Commission, Via Fermi 2749, I-21027 Ispra (VA), Italy
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
Dokuchaev Soil Science Institute, Pyzhevsky per. 7, Moscow 117019, Russia
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(4), 1704-1733;
Received: 8 February 2013 / Revised: 28 March 2013 / Accepted: 2 April 2013 / Published: 8 April 2013
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today. View Full-Text
Keywords: yield forecasts; remote sensing; agriculture; low resolution yield forecasts; remote sensing; agriculture; low resolution
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MDPI and ACS Style

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.

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