CHAPTER 8: LANDSCAPE SCALE MULTISPECTRAL REMOTE SENSING OF COFFEE FOLIAR
8.3 Results
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where for all cases n is the number of data points, yi is the measured foliar N content at that data point and ŷi is the model predicted coffee foliar N content at that data point (Moriasi et al., 2007; DeJonge et al., 2016).
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The results of predicting coffee foliar N with spectral bands only are shown in Table 8.6.
Optimization of variables slightly increased performance of modelling coffee foliar N with spectral bands (Figure 8.2). The performance of the RF model in predicting coffee foliar N with all spectral bands is shown in Figure 8.2a. The map showing the N levels predicted from using all spectral bands in the field is shown in Figure 8.3a. Compared to other variables, using all variables resulted in overestimation of coffee foliar N levels, which explains the low performance for this set of variables (R2=0.57 and RMSE=0.32).
Table 8.6: Accuracy of coffee foliar N prediction with all and optimized Sentinel-2 bands.
Accuracy metrics All bands Optimum bands
# of variables 9 4
MAE 0.27 0.28
RMSE 0.32 0.32
NRMSE % 67.3 66.7
r 0.75 0.76
R2 0.57 0.58
Using the RF oob error, only four spectral bands, which are RE2 (B6), RE3(B7), NIR(B8) and SWIR2(B12), were identified as significant in modelling coffee foliar N (Figure 8.4). It is interesting that the RF model identified two bands in the red-edge region as influencing N distribution and yet these were less correlated to N compared to the NIR band. Also, it was surprising that the RF results put the SWIR2 band in higher importance than the red, red-edge-1 and green bands in N estimation. Using the four optimized variables did not considerably improve the prediction accuracy of N using Sentinel-2 spectral bands (Table 8.6, Figure 8.2b).
The map of predicted N from using optimized spectral bands only is shown in Figure 8.3b). This map was similar to that obtained using all Sentinel-2 MSI spectral bands, in terms of areas with low and sufficient N levels.
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Figure 8.2: 1:1 plot showing the relationship between measured and predicted coffee foliar N using (a) all spectral bands and (b) optimized spectral bands.
Figure 8.3: Predicted distribution of foliar nitrogen levels in coffee leaves obtained from (a) modelling with all spectral bands and (b) modelling with optimized spectral bands.
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Figure 8.4: Variable optimization for prediction of coffee foliar N using (a) spectral bands and (b) vegetation indices.
8.3.3 Foliar N prediction with Sentinel-2 vegetation Indices
Predicting coffee foliar N with vegetation indices considerably improved the prediction accuracy compared to using Sentinel-2 MSI spectral bands only (Table 8.7, Figure 8.5). The RF oob error identified five vegetation indices as important in the modelling of coffee foliar N from Sentinel- 2 MSI data. All the optimized vegetation indices had the NIR band while three had spectral bands from the red-edge portion of the spectrum.
Table 8.7: Performance of predicting coffee foliar N with all and optimized Sentinel-2 vegetation indices.
Accuracy metrics All vegetation indices Optimum vegetation indices
# of variables 9 5
MAE 0.22 0.2
RMSE 0.27 0.25
NRMSE % 55.9 51.8
r 0.84 0.85
R2 0.71 0.73
As with spectral bands, optimisation of vegetation indices slightly improved the performance of modelling coffee foliar N. The N levels distribution map produced from using all nine vegetation indices is shown in Figure 8.6. It was interesting that using all vegetation indices had the highest
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positive bias (+2.9) indicating overestimation of foliar N. On the contrary, optimized vegetation indices had the least bias of all variables used (+0.4), indicating no systematic shifts in foliar N prediction (Figure 8.5). While the general areas with low N levels were similar to those obtained from using spectral bands, when optimized spectral bands were identified, the areas predicted as having high N content were more than those from other approaches (Figure 8.6). In addition, other areas in the middle block were only identified as having low foliar N by optimized vegetation indices alone.
Figure 8.5: 1:1 plot showing the relationship between the measured and the predicted foliar N levels with (a) all nine vegetation indices and (b) five optimized vegetation indices.
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Figure 8.6: Predicted distribution of foliar nitrogen levels in coffee leaves obtained from (a) modelling with all vegetation indices and (b) modelling with optimized vegetation indices.
8.3.4 Foliar N prediction with combination of bands and vegetation indices
The optimum spectral bands (N=4) and the optimum vegetation indices (N=5) were stacked together and used to predict foliar N distribution in coffee fields with the RF (N=9). A combination of optimal bands and vegetation indices performed the best as shown by the highest accuracy metrics and the least error metrics (Table 8.8). All N prediction models showed that they underestimated N when values were over 3.0%.
Table 8.8: Performance of predicting coffee foliar N with a combination of optimal bands and vegetation indices.
Accuracy metrics Optimum bands and vegetation indices
# of variables 9
MAE 0.19
RMSE 0.23
NRMSE % 48.5
r 0.89
R2 0.78
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Figure 8.7: 1:1 plot showing the relationship between the measured and the predicted foliar N levels using a combination of optimal bands and vegetation indices.
The distribution of predicted N levels from optimized bands and vegetation indices is shown in Figure 8a. Compared to other N modelling approaches, a larger area was predicted as having low N levels when a combination of optimal bands and vegetation indices were used (Figure 8.8b).
Figure 8.8: Predicted distribution of foliar nitrogen levels from combination of optimal bands and vegetation indices showing (a) variation in foliar N and (b) distribution of N levels.
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8.3.5 Mapping of low foliar nitrogen areas in coffee plantations
Areas with low, sufficient and high foliar N levels are shown in Table 8.8, with Figure 8.8 comparing positive (high foliar N) and negative (low N) anomalous areas. Using optimized bands had the highest area considered to have low N levels (11% of the study area) while concurrently having the least area with positive anomalies of high foliar N levels (0.14 ha). Contrary, using all spectral bands identified a greater part of the area as having sufficient foliar N levels (Table 8.8). 8.5 ha (2.6%) of coffee area was identified as having low N levels by all datasets. However, considering that vegetation indices and combination of optimized variables produced better accuracy, an intersection of these showed that 15.2 ha (4.7%) of the mature coffee area in the study area have low foliar N levels (<2.5%).
Figure 8.9: Comparison of coffee area with positive (high foliar N) and negative (low N) anomalous foliar N levels from different modelling approaches.
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Table 8.9: Sizes (in ha) and % of low, sufficient and high coffee foliar N levels.
Approach Level
Low nitrogen
Sufficient nitrogen
High
nitrogen Total
All bands Area(ha) 9.7 342.9 0.2 352.9
% 2.8 97.2 0.1 100.0
Optimized Bands Area(ha) 38.8 313.9 0.1 352.9
% 11.0 89.0 0.0 100.0
All VIs Area(ha) 25.3 322.6 4.9 352.9
% 7.2 91.4 1.4 100.0
Optimized VIS Area(ha) 20.2 331.3 1.3 352.9
% 5.7 93.9 0.4 100.0
Optimized Bands + Vis Area(ha) 17.0 330.0 5.9 352.8
% 4.8 93.5 1.7 100.0