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

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

doi:10.3390/rs14030614

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.

Highlights

Introduction iationsAssessing and quantifying the biophysical variables of vegetation cover such as biomass, leaf area index (LAI), height, and the fraction of vegetation cover (FVC), among others, have been identified as a paramount challenge in agriculture due to their role in modelling the growth and development of plants [1], improving prediction of crop yields [2,3,4], and in hydrological processes or droughts monitoring [5,6,7,8,9]
The results showed that the first four factors preserve 98% of the information in terms of cumulative variance, while the first two factors explain about 76% of the variability
The method is well-known in the statistical community, it was never applied to temporal series retrieved from remote sensing observations

Summary

Introduction

Assessing and quantifying the biophysical variables of vegetation cover such as biomass, leaf area index (LAI), height, and the fraction of vegetation cover (FVC), among others, have been identified as a paramount challenge in agriculture due to their role in modelling the growth and development of plants [1], improving prediction of crop yields [2,3,4], and in hydrological processes or droughts monitoring [5,6,7,8,9] Monitoring such parameters through ground sensors or field measurements is expensive and highly time-consuming; remote sensing represents an appealing alternative. Optical remote sensing has been successfully used [10,11,12,13,14], acquiring data using these types of sensors is limited to nearly cloud-free

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