Abstract
Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops.
Highlights
Temperate climatic conditions with easy access to water sources provide optimum conditions for rice cultivation
Phenological data with a priori growth phase information are used for determining the growth boundaries and trends and training a first Polynomial Chaos Expansion (PCE) metamodel that predicts the BBCH scale based on the available morphological parameter (PCEBBCH )
A stack of HH/VV dual-polarization descending TSX images over rice fields located in Spain and Turkey was employed to check the effectiveness of the proposed methodology
Summary
Temperate climatic conditions with easy access to water sources provide optimum conditions for rice cultivation. After the assessment of the importance of model parameters, it is possible to develop an inversion scheme for the crop morphology from polarimetric observations Despite their higher complexity (mathematical and computational), inverse methods have the potential to provide a much deeper insight into the actual growth stage of a crop field, as they take into account the quantitative. Apart from the previously-mentioned growth phase determination methods for the rice fields, the proposed method focuses on the effect of the changes in crop morphology on the polarimetric backscattering intensities during the phenological cycle. It extends the a priori growth phase information with an EM backscattering model in a computationally-efficient framework.
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