Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

Abstract

Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R 2 , SD R 2 , V-RMSE, and SD RMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

“…Fewer studies evaluating DT for AGB estimation are available, which may be because DT does not deliver high accuracy for AGB estimation by remote sensing. Yuan et al [81] indicated that the accuracy of the simple random sampling method is lower than stratified sampling, and our results are in agreement with that study; our results also indicate that all GPS models are more stable than GRS with 2/3 sampling (Table 6 GRS 2/3, and Table 7 GPS 2/3). This may be because the inappropriate sample selection method affects modeling and validation accuracy, which may indicate that GPS sampling is more suitable for these techniques.…”

“…Through thousands of rough modeling and verification processes (Note in Table 3), we obtained the best optimal model parameters under the optimal VIs input. Leave one sampling (LOS) was used to evaluate the performance of each technique, and global random sampling (GRS) and growth-period sampling (GPS) were used to evaluate the performance and stability of each technique with different sampling methods [81,82]. Global random sampling represents random samples from all samples, with a total of three samplings taken and denoted GRS1/3 (64 samples for modeling, the remaining 128 samples for validation), GRS1/2 (96 samples for modeling, the remaining 96 samples for validation), and GRS2/3 (128 samples for modeling, the remaining 64 samples for validation).…”

“…Previous studies have shown that machine learning techniques (ANN [81], BBRT [24] and RF [21,85]) can make full use of the narrow hyperspectral bands (strongly collinear data) and VIs. In the current study, ANN, BBRT and RF are almost unaffected by using multi-collinearity data (Figure 6a,b,g), which may indicate that these techniques are robust against noise, which may relate to the principles of these techniques.…”

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

“…Many previous research works suggested that making full use of hyperspectral information may provide a huge improvement greater than only two-band spectral vegetation indices, such as PLSR [25,43,44,64,72,73], ANN [45,46], SVM [47], RF [48,49], PCR [73] and SMLR [72]. In Tables 5 and 6, the best two-band spectral vegetation indices (HR680) have been greatly improved with crop height.…”

“…Han et al [48] suggested that RF is more efficient than SVM in apple tree canopy LAI estimation. The results of Yuan et al [49] indicated that RF can be used to monitor the soybean leaf area index over the whole growth period.…”