1. Introduction
The leaf area index (LAI), defined as the total one-sided leaf area per unit of the surface area of vegetation [
1], is a key parameter for the crop management and is closely related to photosynthesis, respiration, and transpiration of crop canopies [
2,
3]. The accurate and timely monitoring of the LAI is of great importance for efficient agricultural management [
4]. Traditional methods for the monitoring of the LAI are expensive, laborious, and difficult to apply to the monitoring practice over large areas [
5]. However, advances in remote sensing technologies may provide alternative methods for the monitoring of crop phenotypes in a cost-effective and timely manner at regional scales [
6,
7].
In previous studies, ground-based, airborne, and spaceborne platforms have been commonly used to obtain remote sensing data. Although data collected from ground-based sensors have high temporal and spatial resolutions, its disadvantages are high labor costs and limited coverage ranges [
8]. In contrast, data from spaceborne platforms can cover wide spatial ranges and be obtained from multiple sources [
9]. However, it is difficult for spaceborne platforms to acquire multi-temporal data of crops in a timely manner, due to the limitations of the fixed revisit cycle of the spaceborne platforms and weather influence [
10]. The airborne platforms have gradually been employed in crop phenotypes monitoring for their high temporal resolution and flexible time selection of free flights [
11].
As one of the most commonly used airborne remote sensing platforms, an unmanned aerial vehicle (UAV) has recently been popularly used to collect images for crop growth monitoring [
12,
13]. Previous studies have shown that the spectral data obtained from RGB, multispectral, or hyperspectral sensors onboard UAVs can be used for the monitoring of crop phenotypes [
14]. For example, color indices (CIs) derived from RGB imagery have been used for above-ground biomass estimation of maize [
15]. Vegetation indices (VIs) extracted from the multispectral or hyperspectral sensors commonly contain the range of wavelengths that are strongly associated with crop growth (i.e., red edge (RE), near-infrared (NIR)) which have been widely applied in the monitoring of crop yields, LAI, ground biomass, and chlorophyll [
16,
17,
18]. However, CIs and VIs appear to be saturated in the case of a high proportion of soil background or excessive canopy biomass [
19].
According to [
20], for improving the accuracy of crop phenotypic monitoring, it is feasible to use the data from multiple sources and types, e.g., spectral, texture, and structure of canopies. Both canopy texture and spectral data generated from UAV-based RGB and multispectral imagery were used for the prediction of the LAI, above-ground biomass, and grain yields in [
19,
20]. The canopy structure data, e.g., a crop height model (CHM) collected from UAV-based point clouds, have been also applied in crop phenotypic monitoring. The CHM data were used, in addition to spectral data, in the monitoring of above-ground biomass and yield for a variety of crops [
21,
22,
23]. However, only a few studies examined the application of CHM data for the LAI monitoring [
24,
25], particularly using CHM data in the LAI modelling for the prediction of wheat LAI.
To fuse remote sensing data from various sensors for LAI fitting models, three machine learning algorithms—the partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR)—have been widely employed [
26]. Since the PLSR method can deal with the multicollinearity among the model’s predictors, it was applied to the prediction for the LAI by Liu et al. [
10] and the results had good accuracy. Li et al. [
27] proved the effectiveness of using the RFR method to predict the LAI of crops, since this method has the advantage of expressing the nonlinear mappings between the feature of the remote sensing data and the phenotypes of the crop, and it is also not much affected by the noise and the number of the input variables [
28]. SVR is the implementation of support vector machine for regression approximation, and has the ability to handle the data of high dimensionality and those less affected by sample size [
29]. Therefore, both the PLSR, RFR, and SVR may be used to build LAI prediction models based on not only spectral data but also CHM data.
The main objectives of this study include: (1) to determine the quantitative relationship between the CHM data, CIs, VIs, and LAI of winter wheat during each stage of growth; (2) to investigate the potential of using the PLSR, RFR, and SVR methods and the sample data of both the CHM data and spectral data (i.e., the CIs and VIs) from UAV imagery to develop LAI fitting models for each stage of the growth; (3) to evaluate the effect of the spatial resolution of UAV imagery on the relationship between the LAI and the UAV-based remote sensing data (such as the CHM, CIs, and VIs) as well as on the LAI prediction.
3. Results
3.1. Data Range of Wheat LAI
Table 4 shows the fundamental statistics of the LAI values from the training and testing datasets during each growth stage. For the training dataset, the data ranges of the LAI values during the heading, flowering, filling, maturation and entire growth period were 2.22−7.37 (CV = 24.91%), 1.84−5.45 (CV = 24.26%), 1.43−6.20 (CV = 33.05%), 1.71−4.97 (CV = 24.67%) and 1.43−7.37 (CV = 29.38%), respectively. The analysis of the data from the training dataset of different growth stages showed that the variation in the LAI values was larger in the filling and entire growth period than the other growth stages. The testing datasets in different stages have the similar features or distributions. Hence, the selection of the training and testing datasets for the LAI modelling is reasonable.
3.2. Data Size Per Hectare
According to [
50], the digitization footprint contributions ascribable to UAV imagery and the CHM data were shown in
Table 5. In this study, the required storage disc size per hectare of three types of data increased with the increased in spatial resolution. Additionally, at the same spatial resolution, the required data storage disc size for multispectral UAV imagery was significantly higher than that of the CHM data and RGB imagery.
3.3. Correlation between LAI and CHM Data, CIs and VIs
Figure 2 shows the correlation coefficient between the wheat LAI and the UAV-based CHM data, CIs, and VIs at each of the flight altitudes in each of the growth stages (heading, flowering, filling, maturation), respectively, and also the entire growth period. From the most left column, which is for the CHM results, comparing the three values in the same growth stage, we can find that the value at the 50 m altitude was the largest, followed by the 75 m altitude, and the weakest correlation was found at the 100 m altitude. Moreover, if the CHM results from the five different stages were compared, the largest correlation was in the heading stage (top), with the values of 0.80, 0.75, and 0.75 at the 50 m, 75 m, and 100 m altitudes, respectively. These results reveal the potential of using the CHM data in the modelling of the wheat LAI, which is for the prediction of the wheat LAI.
From the second (EXG) to the eighth columns (VEG) in
Figure 2 are the correlation results of the seven CIs. We can find that the correlation values of all these CIs in the heading stage from the same height were smaller than that of CHM, and it is also the same at the 50 m height in the flowering stage. However, in the remaining results, most of the correlation values of the CIs were larger than that of CHM. In addition, from the ninth to the last columns are the results of the nine VIs. The majority of their values were larger than that of the CIs, and they were less affected by flight altitudes. The DVI (the third last column) and RVI (the last column) presented the strongest correlations, compared to the other VIs in all five stages.
According to the above results, the CHM data, CIs, and VIs extracted from the UAV imagery at 50 m flight altitude were selected in
Section 3.3 and
Section 3.4 to analyze the effect of using the CHM data and spectral data for the wheat LAI models developed using the PLSR, RFR, and SVR methods.
3.4. LAI Prediction Accuracy from PLSR, RFR, and SVR over the Entire Growth Period
In this section, the following five combinations of data were used as the input data of the LAI fitting models developed using the PLSR, RFR, and SVR methods over the entire growth period: (1) CHM, (2) CIs, (3) CIs + CHM, (4) VIs, and (5) VIs + CHM. The PLSR, RFR, and SVR fitting models were tested using an out-of-sample dataset (i.e., the test data were not used in the modelling) and the results are shown in
Table 6.
When using the CHM data alone, SVR achieved the best LAI prediction performance (R2 = 0.147, RMSE = 0.926) among the three regression methods. When using the CIs or VIs as the model input set, the best performance was also found for the SVR method. Moreover, the accuracy of VIs results was obviously better than that of CHM and CIs cases.
The combination of CHM and CIs led to obvious improvements for all regression methods than the CIs-only cases, with the increment of 0.158, 0.268 and 0.201 in R2 for PLSR, RFR, SVR, respectively. In addition, SVR achieved the highest accuracy (R2 = 0.652, RMSE = 0.591) in the LAI prediction. For VIs, the additional CHM data also enhanced the accuracy of the LAI models, with the increment of 0.046, 0.073 and 0.020 in R2 for PLSR, RFR, SVR, respectively.
Figure 3u–y shows the scatter plots for measured versus predicted LAI from optimal LAI fitting models in the entire growth period. It could be found that the combination of the CHM and spectral data led to the data points closer to the 1:1 line than the cases of using the CHM or spectral data only.
3.5. LAI Prediction Accuracy for Individual Growth Stages
As shown in
Figure 4, the performance of LAI fitting models was inconsistent across four individual growth stages for five types of input sets with three regression methods. In the cases of using the CHM data, CIs and the combination of the CHM data and CIs, the LAI prediction accuracy showed a trend of first decreasing and then increasing from the heading stage to the maturation stage. The increase in LAI prediction accuracy from the flowering to the filling stage was the most significant change in all adjacent stages. In addition, all three regression methods had the stable performance in the filling and the maturation stage. In contrary to the LAI prediction results of the entire growth period, using the CHM data as the model input set had the better performance in LAI prediction than that of the CIs in the heading and the flowering stages for SVR an PLSR methods. However, the combination of CHM and CIs led to a significant improvement in its resultant LAI, compared with the case of using the CHM data or CIs only in four individual growth stages.
When using VIs and VIs + CHM as the model input sets, the accuracy of the LAI fitting models showed an upward trend from the heading to the filling stage and decreased slightly in the maturation stage. All three regression methods had the stable performance in LAI prediction in four individual stages.
3.6. Effect of Spatial Resolution on LAI Prediction
In this study, the CIs and VIs extracted from UAV imagery at different altitudes in the heading, flowering, filling, and maturation stages had the pixel resolutions of 2.7 cm (50 m), 4.0 cm (75 m), and 5.4 cm (100 m), respectively; the pixel resolutions of the CHM data extracted were 5.4 cm (50 m), 8.0 cm (75 m), and 10.8 cm (100 m). The LAI fitting models based on the PLSR, RFR, and SVR methods, and the sample data of the CHM data, CIs and VIs extracted at the same flight altitudes were constructed, and their test results, i.e., the R
2 and RMSE of the LAI values predicted by the models are shown in
Figure 5.
For using the CHM data only, the accuracy of the LAI prediction increased with the increased in spatial resolution in each individual growth stage. However, as shown in
Figure 5e,j, the increase in spatial resolution of the CHM data had no significant impact on LAI prediction in the entire growth period.
From the R2 and RMSE results of the CIs-only case (green in each pane) in the heading, flowering and entire growth period, one can see that the highest accuracy of the LAI prediction was at the 75 m altitude, followed by 50 m and 100 m; while in the other two stages, the accuracy of the LAI prediction decreased with the decrease in spatial resolution. Moreover, the accuracy of the CIs + CHM results in each growth stage was obviously improved for all flight altitudes compared with the results of the CIs-only case, meaning the contribution made by the use of the CHM data in the LAI modeling based on RGB imagery.
For the VIs-only case, the accuracy of the LAI prediction at the 100 m altitude was significantly better than the 50 m and 75 m altitudes; and only in the flowering stage the highest accuracy of the LAI prediction was at the 75 m altitude, followed by 50 m and 100 m. Moreover, the CHM + VIs results significantly improved only in the heading stage and the entire growth period. However, in the rest three stages the introduction of the CHM data did not improve the accuracy.
5. Conclusions
This study investigated the potential of combining the CHM and spectral data (CIs, VIs) obtained from a low-altitude UAV in wheat LAI modeling based on PLSR, RFR, and SVR methods. The results demonstrated that the accuracy of the CIs-based LAI prediction models was obviously improved by the use of the additional CHM data. For the VIs case, the improvement of the CHM data for a better LAI prediction was mainly reflected in the heading and entire growth period but was not significant in the other three stages. Moreover, the performance of the CHM data, CIs, and VIs under different image resolutions was analyzed, and the results showed the additional CHM data at the resolution of 5.4 cm had the best performance in improving the LAI prediction accuracy.
The results from this study suggest the effectiveness the combination of the CHM data and CIs in the wheat LAI modelling in and after the heading stage for the wheat LAI monitoring. However, for the LAI monitoring based on multispectral UAV imagery, only the CHM data with high spatial resolution may improve the LAI prediction accuracy more obviously in heading and entire growth period. Our future work will be focused on more investigations on the influence of using the CHM data on the wheat LAI model results before the heading stage, and on the contribution of using the CHM data from different types of crops in each growth stage.