Remote Sens. 2013, 5(4), 1704-1733; doi:10.3390/rs5041704
Review

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

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.
Received: 8 February 2013; in revised form: 28 March 2013 / Accepted: 2 April 2013 / Published: 8 April 2013
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
PDF Full-text Download PDF Full-Text [998 KB, Updated Version, uploaded 9 April 2013 16:36 CEST]
The original version is still available [969 KB, uploaded 8 April 2013 13:52 CEST]
Abstract: 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.
Keywords: yield forecasts; remote sensing; agriculture; low resolution

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

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.

AMA Style

Rembold F, Atzberger C, Savin I, Rojas O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing. 2013; 5(4):1704-1733.

Chicago/Turabian Style

Rembold, Felix; Atzberger, Clement; Savin, Igor; Rojas, Oscar. 2013. "Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection." Remote Sens. 5, no. 4: 1704-1733.

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert