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Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection (2013)
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@MISC{Rembold13usinglow,
author = {Felix Rembold and Clement Atzberger and Igor Savin and Oscar Rojas},
title = {Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection},
year = {2013}
}
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Abstract
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 theRemote Sens. 2013, 5 1705
Keyphrases
low resolution satellite imagery yield anomaly detection yield prediction regional scale detailed scale drought monitoring system low resolution image quantitative approach high temporal frequency different role fundamental component complex crop growth model mixed nature low cost early warning final crop yield crop monitoring new sensor low resolution pixel main predictor area unit theremote sen important role extended geographical coverage operational system vegetation performance anomaly detection yield forecasting convenient choice remote sensing-derived indicator
