# A Multi-Year Study on Rice Morphological Parameter Estimation with X-Band Pulsar Data
## 5 Overview and Summary  
In this research, a stochastic inversion method has been presented to invert a multi-dimensional PCEEM metamodel, trained from an EM model [11]. Apart from the previous studies that focuses on the estimation of the stalk height, this study extends the estimations to the stalk height, leaf length and leaf width parameters of rice plants by considering the natural growth limitations. This study was conducted over three cultivation cycles for validation purposes.  
We have tested the proposed inversion algorithm on a three-year cultivation period of ground, and copolar high spatial resolution and high frequency SAR datasets. The data were acquired by TerraSAR-X over broadcast seeded and flooded rice fields. For method validation we trained the PCEEM metamodel by employing the EM model [11], ground measurements and field average SAR backscattering intensities.  
As a result, we obtained significant correlations between the estimated and measured values of stalk height, leaf length and leaf width parameters using the proposed PCEEM metamodel inversion scheme. From the analysis, we calculated RMSE values less than 13.5 cm for stalk height, 6.9 cm for leaf length and 34 mm for leaf width parameters. The results pointed out that the use of spaceborne X-band PolSAR data is powerful for the development of new agriculture monitoring practices. We should mention that the overall performance of the proposed approach mainly relies on the accuracy of the EM model and the PCEEM metamodel, which shows the importance of EM model selection.  
The presented algorithm has presented several advantages such as coupling the agronomical growth rules with the EM models for efficiency, inversion with stochastic optimization to handle environmental variability and most importantly being computationally efficient with the use of metamodels that are trained for once. In the presented study, the requirement of a prioriknowledge of the growth phase and plant morphology information can be seen as a disadvantage. However, considering the importance of rice as the main source or food and income for several highly populated countries, related information can be found in the crop databases. Besides, the literature currently covers several different methodologies to determine the growth phase of rice plants [3; 18; 19].  
In the future, our studies are going forward to improve the approach by substituting the _a priori_ growth phase information with canopy height estimations that can be directly calculated from Pol-InSAR data. In addition, it is planned to extend the applicability of the inversion procedure to monitor other major crops.  
This work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project 113Y446 and by the German Aerospace Center (DLR) under project XTILAND1476. Besides, the authors would like to thank for the support of Dr. [PERSON] and Prof. Dr. [PERSON] from the Chair of Risk, Safety & Uncertainty Quantification from ETH Zurich in setting up and providing help with the Polynomial Chaos Expansion metamodel and to the Directorate of Trakya Agricultural Research Institute with the ground campaigns.  
[PERSON] developed and implemented the proposed inversion methodology, interpreted the results, wrote and revised the paper; [PERSON] supervised the work, contributed to the underlying ideas as well as to the interpretation and discussion of the results and revised the paper; [PERSON] supervised the work, contributed to the underlying ideas and revised the paper.  
The authors declare no conflict of interest.