Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detectionстатья
Статья опубликована в высокорейтинговом журнале
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 26 декабря 2015 г.
Аннотация: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.