Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques
This study aims to fit and predict crop biophysical variables using a series of SAR images by conducting a factorial experiment and estimating time series models through a combination of forecasts. The research focused on two barley plots grown under rainfed conditions in Spain during the 2015 growi...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/41230 |
| Acceso en línea: | https://hdl.handle.net/10115/41230 |
| Access Level: | acceso abierto |
| Palabra clave: | RADARSAT-2 polarimetric SAR biophysical variables time series cointegration |
| Sumario: | This study aims to fit and predict crop biophysical variables using a series of SAR images by conducting a factorial experiment and estimating time series models through a combination of forecasts. The research focused on two barley plots grown under rainfed conditions in Spain during the 2015 growing season (February to June). The dataset included nine field measurements of agronomic parameters, 20 RADARSAT-2 images, and daily weather records. Ten polarimetric observables were extracted and integrated to derive six key agronomic and monitoring variables, including height, biomass, vegetation cover fraction, leaf area index, water content, and soil moisture. Statistical methods, such as double smoothing, ARIMAX, and robust regression, were employed to model these field variables. The model equations indicated a positive influence of meteorological factors and a significant temporal component in crop development, reflecting natural growth patterns. After combining various models, the results showed improved forecasting efficiency and highlighted the impact of several weather variables. The presence of a cointegration relationship between data series from the same crop across different fields enables the prediction of crop outcomes in similar fields without the need for re-modelling. |
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