Table 2.
Characteristics of the calibration models for the three sensors. Models were parameterised for all data points or by separating the dicot (grapevine and kiwi) and monocot (wheat and maize) species (P < 0.0001 for all models). They were linear (a + bx) for Dualex, homographic [(ax)/ (b − x)] for SPAD and exponential (a + becx) for CCM. The 95% confidence intervals for the fit coefficients are indicated in brackets (non-significant in italic). Residual sum of squares (RSS), root mean square error (RMSE), bias (BIAS), standard error of prediction corrected for bias (SEPC), relative error (%) = SEPC/mean are given
| Sensor Species | Model parameters | Model statistics | Mean | Min | Max | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| a | b | c | R2 | RSS | RMSE | BIAS | SEPC | (µg cm−2) | N | Error (%) | |||
| Dualex Monocots | −7.46 (2.0) | 1.04 (0.046) | − | 0.963 | 631 | 6.36 | −5.67 | 2.89 | 39.9 | 12.4 | 61.6 | 79 | 7 |
| Dicots | −4.82 (1.4) | 1.24 (0.047) | − | 0.960 | 960 | 4.24 | 1.57 | 3.94 | 26.7 | 5.18 | 49.5 | 117 | 15 |
| Global | −1.12 (1.8) | 0.993 (0.051) | − | 0.883 | 4952 | 5.20 | −1.34 | 5.03 | 32.0 | 5.18 | 61.6 | 196 | 16 |
| SPAD Monocots | 82.2 (10) | 135 (11) | − | 0.941 | 1000 | 3.56 | −0.06 | 3.56 | 38.2 | 9.40 | 57.8 | 79 | 9 |
| Dicots | 59.0 (6.1) | 95.0 (5.8) | − | 0.915 | 2026 | 4.16 | −0.12 | 4.16 | 29.1 | 1.30 | 47.6 | 117 | 14 |
| Global | 138 (47) | 185 (48) | − | 0.876 | 5269 | 5.18 | 0 | 5.18 | 32.7 | 1.30 | 57.8 | 196 | 16 |
| CCM Monocots | 72.4 (6.8) | −68.8 (5.8) | −0.0242 (0.0045) | 0.913 | 1484 | 4.33 | 0 | 4.33 | 27.5 | 2.90 | 63.1 | 79 | 16 |
| Dicots | 86.1 (14) | −84.9 (13) | −0.0267 (0.0070) | 0.897 | 2440 | 4.57 | 0 | 4.57 | 15.2 | 1.20 | 38.4 | 117 | 30 |
| Global | 61.1 (5.7) | −60.2 (4.6) | −0.0407 (0.0089) | 0.863 | 5804 | 5.44 | 0 | 5.44 | 20.1 | 1.20 | 63.1 | 196 | 27 |