Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

Abstract

Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape during growth using high spatial resolution optical satellite data covering a test site located in southeast China. Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values. The optimal VIs were selected using curve fitting methods and the random forest algorithm. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, k-nearest neighbor (kNN), and random forest regression (RFR). Finally, the mapping and estimation of winter oilseed rape LAI using reflectance obtained from Pleiades-1A, WorldView-3, SPOT-6, and WorldView-2 were implemented using the inversion method and the LAI estimation accuracy was validated using ground-measured datasets acquired during the 2014-2015 growing season. Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R 2 > 0.954 and RMSE <0.218. Using the optimal VIs, the remote sensing-based mapping of winter oilseed rape LAI yielded an accuracy of R 2 = 0.520 and RMSE = 0.923 (RRMSE = 93.7%). These results have demonstrated the potential operational applicability of the hybrid method proposed in this study for the mapping and retrieval of winter oilseed rape LAI values at field scales using multi-source and high spatial resolution optical remote sensing datasets. Details provided by this high resolution mapping cannot be easily discerned at coarser mapping scales and over larger spatial extents that usually employ lower resolution satellite images. Our study therefore has significant implications for field crop monitoring at local scales, providing relevant data for agronomic practices and precision agriculture.

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

“…Compared to SR, slight improvements were obtained by ANN (R 2 = 0.79 and RMSE = 1.80 t ha −1 ) ( Figure 7e) and RF (R 2 = 0.73 and RMSE = 2.03 t ha −1 ) (Figure 7h). Moreover, the LME model achieved moderate improvements with R 2 and RMSE values of 0.86 and 1.49 t ha −1 , respectively (Figure 7k). For the post-heading stages, SR had the highest estimation accuracy using DATT but remained low (R 2 = 0.22 and RMSE = 3.49 t ha −1 ) (Figure 7c).…”

“…In contrast, the LME model outperformed the other models (R 2 = 0.81 and RMSE = 1.04) (Figure 8k). For post-heading stages, SR achieved the highest estimation accuracy using CIRE, with respective R 2 and RMSE values of 0.53 and 1.59 (Figure 8c). ANN and RF slightly improved the estimation accuracy, with respective R 2 values of 0.58 and 0.56 and RMSE values of 1.49 and 1.53 (Figure 8f,i).…”

“…Monitoring crop growth status efficiently and in a non-destructive way is necessary for precision crop management [1]. Aboveground biomass (AGB) and leaf area index (LAI) are important growth parameters with which to evaluate crop growth status [2,3]. Both have been applied to describe the canopy structure of plants and to predict grain yield [4].…”

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

“…The KNN finds a group of k samples (e.g., using the distance functions) which are closest to unknown samples in the calibration dataset, constituting the basic theory of KNN. The KNN also determines the label (class) of unknown samples among the k samples through the calculation of the average of the response variables [61,62]. Consequently, k plays a significant role in the performance of the KNN [63].…”

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

“…where D(x,y) represents the distance between the two selected input vectors, and x i and y i represent the data points. In the classifier, k is a tuning parameter that plays an important role in the performance of kNN [32].…”