Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques
Abstract
This paper aims to both fit and predict crop biophysical variables with a SAR image series by performing a factorial experiment and estimating time series models using a combination of forecasts. Two plots of barley grown under rainfed conditions in Spain were monitored during the growing cycle of 2015 (February to June). The dataset included nine field estimations of agronomic parameters, 20 RADARSAT-2 images, and daily weather records. Ten polarimetric observables were retrieved and integrated to derive the six agronomic and monitoring variables, including the height, biomass, fraction of vegetation cover, leaf area index, water content, and soil moisture. The statistical methods applied, namely double smoothing, ARIMAX, and robust regression, allowed the adjustment and modelling of these field variables. The model equations showed a positive contribution of meteorological variables and a strong temporal component in the crop’s development, as occurs in natural conditions. After combining different models, the results showed the best efficiency in terms of forecasting and the influence of several weather variables. The existence of a cointegration relationship between the data series of the same crop in different fields allows for adjusting and predicting the results in other fields with similar crops without re-modelling.
Description
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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