A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem
Table 3
Calibration and validation accuracies for approach A—Multiple linear regression of spectral parameters. The models further to be applied for a large-scale prediction of soil constituents based on HyMap imagery are highlighted.
Spectral dataset
No. of spectral variables
Calibration (941/1232 samples)
Validation (311/402 samples)
RMSCal
RPDCal
RMSVal
RPDVal
ASD spectral resolution
(1) In situ field
12 of 16
0.77
0.47
2.09
0.49
0.54
1.26
(2) Bare soil field
14 of 16
0.79
0.44
2.22
0.56
0.48
1.42
(3) Laboratory
13 of 16
0.75
0.47
2.01
0.74
0.36
1.93
(1) In situ field
19 of 21
0.63
0.80
1.64
0.24
0.90
1.07
Iron oxides
(2) Bare soil field
15 of 21
0.64
0.78
1.68
0.21
0.93
1.04
(3) Laboratory
19 of 21
0.75
0.76
2.01
0.23
1.63
0.69
(1) In situ field
14 of 16
0.31
4.12
1.20
0.01
5.13
0.88
Clay
(2) Bare soil field
11 of 16
0.21
4.39
1.13
0.03
4.80
0.94
(3) Laboratory
16 of 16
0.23
4.60
1.14
0.05
4.52
1.00
HyMap spectral resolution
(1) In situ field
13 of 16
0.79
0.44
2.19
0.51
0.54
1.27
(2) Bare soil field
12 of 16
0.81
0.43
2.28
0.62
0.43
1.57
(3) Laboratory
15 of 16
0.77
0.46
2.08
0.77
0.35
2.00
(1) In situ field
18 of 21
0.62
0.80
1.64
0.17
1.04
0.93
Iron oxides
(2) Bare soil field
19 of 21
0.66
0.76
1.73
0.26
0.93
1.03
(3) Laboratory
20 of 21
0.73
0.79
1.94
0.25
1.58
0.71
(1) In situ field
14 of 16
0.28
4.20
1.18
0.00
5.05
0.89
Clay
(2) Bare soil field
14 of 16
0.17
4.49
1.11
0.01
4.91
0.92
(3) Laboratory
12 of 16
0.25
4.53
1.16
0.05
4.62
0.98
1Number of samples in training and test set for field datasets. 2Number of samples in training and test set for laboratory datasets.
