China - Jiangsu

Specific Project Objectives & Deliverables

Results

  1. An automated method for rice phenology monitoring
    An automated method of rice phenology retrieval was developed using a feature optimization strategy integrated support vector machine (SVM) and sequential forward selection (SFS). The flowchart of the method is displayed in Fig 3. The optimal polarimetric variables for each phenological stage were acquired, based on which eight rice phenological stages were retrieved automatically. The accuracies were higher than 90% except for the dough stage and the transition period between two stages.


    Figure 3. The flowchart of the rice phenology retrieal.
     
  2. Further validation of the method of rice parameter estimation proposed in 2015

    In 2015 a modified water cloud model (MWCM) for rice parameters estimation was proposed, in which the heterogeneity of the rice canopy were considered as well as the polarimetric information. We validate the method using R-Square and RMSE only. In 2016, we further validated the methods and compared the results with that of the tradional water cloud model (Fig 4).

    In the CASE I, the heterogeneity of rice canopy in the horizontal direction is considered without the polarimetric information. In the CASE II, the polarimetric variables from the improved polarimetric decomposition are used in the MWCM, while the heterogeneity of rice canopy in the horizontal direction is ignored. In the CASE III, both the heterogeneity of rice canopy and the polarimetric information are considered in the MWCM.

    During the whole rice growth cycle, r for the CASE III was largest, implying that the estimation accuracy is the best by considering both the heterogeneity of rice canopy and the decomposition components. From the seedling to the booting stage (from June 27 to Aug. 4), r for the CASE I (larger than 10%) is much larger than that for the CASE II (2-3%), implying that the estimation accuracy is improved much by considering the heterogeneity of rice canopy during the this period, whereas the estimation accuracy is just improved approximately 3% by applying the decomposition components. From the heading to the mature stage (from Aug. 28 to Oct. 15), r for the CASE II (approximately 5%) is larger than that for the CASE I (less than 2%), implying the application of decomposition components in the MWCM could improve the estimation accuracy more than the consideration of the rice canopy heterogeneity during the reproductive phase of rice growth cycle. In sum, the heterogeneity of rice canopy is very essential to consider in the MWCM, especially during the vegetative phase. With the rice canopy becomes dense and uniform, the heterogeneity of rice canopy decreases. The application of decomposition components in the MWCM could improve the estimation accuracy with 3-5% during the rice growth cycle.


    Figure 4. Comparison between the estimation accuracy in different cases. The estimation accuracies of (a) LAI, (b) h, and (c) m was shown as example

  3. The multi-sphere ear scattering model

    A novel rice ear scattering model, the multi-sphere ear scattering model, was developed by incorporating the micro-structure of rice ear panicles, including the ear grain parameters in particular. A virtual ear model (Fig 5) was used to simulate the ear morphology, and a multi-sphere scattering model was used to simulate the ear scattering. This novel multi-sphere ear scattering model provides a potential way of retrieving grain parameters from SAR imagery.


    Figure 5. Virtual Rice Ear with ϕ0 = 74.39◦ and ϕN = 50.70◦, ϕ0 and ϕN are the basal and distal elevation angle of the axis curve, obtained from ground measurements.



    Figure 6. The simulation results over 100 realizations.
     
©2015 Joint Experiment for Crop Assessment and Monitoring
© HER MAJESTY THE QUEEN IN RIGHT OF CANADA SA MAJESTE LA REINE DU CHEF DU CANADA (2015)