Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Self Cite

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 (g 3 ) with R 2 of 0.765 and RMSE of 73.20 g/m 2 . For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (U c ), with R 2 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; R 2 for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m 2 , 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.

“…There are three CP decomposition algorithms named m − δ decomposition, m − α decomposition and m − χ decomposition [10]. However, α is a supplementary angle for χ.…”

Section: Decomposition Parametersmentioning

confidence: 99%

Section: Decomposition Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion

Zhang

Chen

et al. 2017

Self Cite

“…In this study, the relationship between field-observed biomass with each metric was first examined using both the linear and exponential regression analyses according to the previous studies [17,48]. These metrics included VIs derived from hyperspectral data and LiDAR-derived metrics.…”

Section: Regression Analysismentioning

confidence: 99%

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Abstract:The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H CV ) of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H CV combined with H mean performed better than one LiDAR-derived metric alone in biomass estimation (R 2 = 0.835, RMSE = 374.655 g/m 2 , RMSE CV = 393.573 g/m 2 ). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R 2 = 0.883, RMSE = 321.092 g/m 2 , RMSE CV = 337.653 g/m 2 ) compared with LiDAR or hyperspectral data alone.

“…Recently, with the development of remote sensing techniques, different methods have been developed to estimate biomass and yields based on satellite data. One approach develops statistical relationships between the satellite-derived vegetation indices (VIs) and field measured biomass or yields [5][6][7]. These regression models are widely used because they are simpler and more convenient than multivariate statistical methods; nevertheless, they are limited to local applications and are difficult to extend because they lack a theoretical basis.…”

Section: Introductionmentioning

confidence: 99%

Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

Zheng

Zhang

et al. 2016

Abstract:Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R 2 ≤ 0.94 for the red band, 0.50 ≤ R 2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R 2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%-20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R 2 and low root mean square errors (RMSE) (0.864 ≤ R 2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m 2 for biomass; and 0.631 ≤ R 2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m 2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.