A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem
Table 5
Calibration and validation accuracies for approach B—partial least squares regression. The models further to be applied for a large-scale prediction of soil constituents based on HyMap imagery are highlighted.
Spectral dataset
Pretreatment
No. of factors
Calibration (941/1232
samples)
Validation (311/402
samples)
RMSCal
RPDCal
RMSVal
RPDVal
ASD spectral resolution
(1) In situ field
1st derivative
6
0.79
0.45
2.16
0.53
0.52
1.30
(2) Bare soil field
MSC + 1st derivative
6
0.82
0.41
2.38
0.65
0.42
1.61
(3) Laboratory
Mean center + 1st derivative
6
0.82
0.40
2.36
0.69
0.45
1.53
(1) In situ field
Mean center
7
0.66
0.76
1.74
0.39
0.84
1.14
Iron oxides
(2) Bare soil field
Mean center
8
0.69
0.73
1.80
0.43
0.80
1.20
(3) Laboratory
Log(1/R) + mean center
9
0.82
0.64
2.39
0.43
0.98
1.14
(1) In situ field
Mean center
7
0.23
4.34
1.15
0.02
4.63
0.97
Clay
(2) Bare soil field
Log(1/R)
5
0.11
4.67
1.06
0.06
4.42
1.02
(3) Laboratory
Mean center
8
0.33
4.31
1.22
0.08
4.43
1.02
HyMap spectral resolution
(1) In situ field
Log(1/R) + mean center
5
0.77
0.46
2.11
0.59
0.47
1.43
(2) Bare soil field
Log(1/R) + 1st derivative
5
0.82
0.41
2.38
0.62
0.45
1.51
(3) Laboratory
Log(1/R)
9
0.79
0.43
2.20
0.79
0.32
2.11
(1) In situ field
Mean center
7
0.67
0.75
1.75
0.43
0.76
1.26
Iron oxides
(2) Bare soil field
Mean center
7
0.66
0.76
1.73
0.45
0.77
1.25
(3) Laboratory
Log(1/R) + mean center
9
0.81
0.67
2.28
0.49
0.88
1.27
(1) In situ field
Mean center
4
0.14
4.58
1.09
0.01
5.06
0.89
Clay
(2) Bare soil field
Mean center
5
0.15
4.56
1.09
0.03
4.47
1.01
(3) Laboratory
1st derivative
8
0.37
4.16
1.27
0.10
4.43
1.02
1Number of samples in training and test sets for field datasets. 2Number of samples in training and test sets for laboratory datasets.
