Abstract
Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but its potential in growth parameters inversion has not been fully explored. In this paper, a time series of images of CP SAR was simulated from five FP SAR data gathered during the entire growth season of rape. CP response of 27 parameters, relying on Stokes parameters and their child parameters, decomposition parameters and backscattering coefficients, were extracted and investigated as a function of days after sowing (DAS) during the whole rape growth cycle to interpret their sensitivity to each growth parameter. Then, random forest (RF) was chosen as an automatic approach for the growth parameters inversion method, and its results were compared with traditional single-parameter regression models. Most of the CP parameters showed high sensitivity with growth parameters and great potential for growth parameters inversion. Among all of the regression models, the quadratic regression model showed the best performance for all of the growth parameters inversion, the best result for biomass inversion was the third component of the Stokes parameters (g3) with R2 of 0.765 and RMSE of 73.20 g/m2. For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (Uc), with R2 of 0.857 and 0.923 and RMSE of 0.66 and 18.71 cm, respectively. RF showed the highest accuracy and smallest RMSE for all of three growth parameters inversion; R2 for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m2, 0.25 and 13.5 cm, respectively. However, there are also some CP parameters, which showed low sensitivity to growth parameters, that had high importance for RF inversion. The results confirmed the potential of CP data and the RF method in growth parameters inversion, but they also confirmed that it was difficult to give a physical interpretation for the RF inversion model.
Highlights
Rape (Brassica napus L.) is surely one of the important oil plants in the world because it accounts for the main part of annual edible oil, and constitutes the most promising cleanRemote Sens. 2017, 9, 591; doi:10.3390/rs9060591 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 591 fuels through biodiesel made from its seeds [1]
Analysis of Compact polarimetric (CP) Observables’ Sensitivity to Rape Growth Parameters. Both the temporal evolution of CP response and rape growth parameters during the whole growth cycle were analyzed as a function of the number of day after sowing (DAS)
DAS of rape fields were calculated with the methods proposed in [27]; CP observables were averaged from rape fields with the same DAS
Summary
Rape (Brassica napus L.) is surely one of the important oil plants in the world because it accounts for the main part of annual edible oil, and constitutes the most promising cleanRemote Sens. 2017, 9, 591; doi:10.3390/rs9060591 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 591 fuels through biodiesel made from its seeds [1]. Stem height is a useful indicator of crops biomass; the biomass of crop vegetation plays an important role in plant monitoring and yield prediction. The leaf area index (LAI) is an important cultivation physiology parameter for the measurement of the reasonableness of the crop community. These growth parameters reflect the photosynthetic potential of crops at a certain period, which directly affect the biological and economic yield [2]. For the importance of these growth parameters for yield estimation, the sensitivity of climate change and crop monitoring, as well as the destructive and expensive procedure for directly measuring, there is a significant interest in this information collected by remote sensing techniques [3]. Various studies have been carried out using remote sensing to estimate biomass, LAI for various crops, based on vegetation indices from optical sensors with regression models or the vegetation radiation transfer function [4,5]
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