Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques

Sipols, Ana E.; Valcarce-Diñeiro, Rubén; Santos-Martín, Maria Teresa; Sánchez, Nilda; de Blas, Clara Simón

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.

Citation

Sipols, A. E., Valcarce-Diñeiro, R., Santos-Martín, M. T., Sánchez, N., & de Blas, C. S. (2022). Time Series of Quad-Pol C-Band Synthetic Aperture Radar for the Forecasting of Crop Biophysical Variables of Barley Fields Using Statistical Techniques. Remote Sensing, 14(3), Article 614. https://doi.org/10.3390/rs14030614

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2022
• Online Publication Date: Jan 27, 2022
• Publication Date: Feb 1, 2022
• Deposit Date: Nov 27, 2024
• Publicly Available Date: Nov 28, 2024
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 3
• Article Number: 614
• DOI: https://doi.org/10.3390/rs14030614
• Keywords: RADARSAT-2; Polarimetric SAR; Biophysical variables; Time series; Cointegration
• Public URL: https://hull-repository.worktribe.com/output/4716116