Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Naeba Dataset
2.2. DNN Model Construction
2.3. Other Leaf Biophysical/Biochemical Traits as Additional Predictors
2.4. Performance Evaluation
3. Results
3.1. Performance of DNN Models for Predicting Vcmax and Jmax from Reflectance
3.2. Performance of DNN Models for Predicting Vcmax and Jmax from Reflectance in Different Leaf Types
3.3. Performance of DNN Models for Predicting Vcmax and Jmax from Reflectance during Different Growing Periods
3.4. Leaf Biophysical/Biochemical Traits as Additional Predictors of DNN Models
4. Discussion
4.1. Estimation of Leaf Photosynthetic Traits from Spectra Using the DNN Models
4.2. Including Other Leaf Biophysical/Biochemical Traits to Better Predict Photosynthetic Capacity from Reflectance Using DNN Models
4.3. Bootstrap as a Remedy for Setting Up Robust DNN Models from Limited Samples
4.4. Limits and Future Studies
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Traits | Period | R2 | RMSE | MAE | RPD |
---|---|---|---|---|---|
Vcmax | Flushing | 0.70 | 11.03 | 8.34 | 1.85 |
Maturity | 0.48 | 12.53 | 9.39 | 1.41 | |
Senescence | 0.52 | 8.05 | 6.33 | 1.46 | |
Jmax | Flushing | 0.78 | 16.60 | 12.06 | 2.15 |
Maturity | 0.50 | 19.68 | 15.18 | 1.43 | |
Senescence | 0.55 | 18.84 | 15.04 | 1.47 |
Leaf Type | DNN Model | Vcmax | Jmax | ||||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | RPD | R2 | RMSE | MAE | RPD | ||
All | ref, LT | 0.53 | 11.83 | 8.94 | 1.46 | 0.53 | 21.40 | 16.82 | 1.46 |
ref, LMA | 0.54 | 11.24 | 8.58 | 1.47 | 0.53 | 21.21 | 16.53 | 1.46 | |
ref, Chl | 0.54 | 11.73 | 8.90 | 1.47 | 0.55 | 20.92 | 16.27 | 1.49 | |
ref, LT, Chl | 0.49 | 11.90 | 9.20 | 1.41 | 0.51 | 20.94 | 16.74 | 1.42 | |
ref, LMA, Chl | 0.55 | 11.53 | 8.94 | 1.48 | 0.53 | 21.79 | 16.73 | 1.45 | |
ref, LT, LMA, Chl | 0.54 | 11.70 | 9.24 | 1.44 | 0.51 | 22.67 | 17.46 | 1.41 | |
Sunlit | ref, LT | 0.50 | 12.56 | 9.69 | 1.41 | 0.48 | 22.98 | 17.78 | 1.39 |
ref, LMA | 0.50 | 12.36 | 9.69 | 1.42 | 0.49 | 22.12 | 17.27 | 1.40 | |
ref, Chl | 0.54 | 11.55 | 9.28 | 1.49 | 0.52 | 19.96 | 16.87 | 1.45 | |
ref, LT, Chl | 0.50 | 12.71 | 9.91 | 1.42 | 0.49 | 21.05 | 16.22 | 1.41 | |
ref, LMA, Chl | 0.52 | 12.79 | 10.18 | 1.43 | 0.54 | 20.97 | 16.62 | 1.49 | |
ref, LT, LMA, Chl | 0.53 | 12.44 | 9.88 | 1.45 | 0.50 | 21.53 | 16.66 | 1.40 | |
Shaded | ref, LT | 0.42 | 7.07 | 5.37 | 1.29 | 0.36 | 14.38 | 11.66 | 1.20 |
ref, LMA | 0.49 | 6.17 | 5.05 | 1.41 | 0.45 | 11.48 | 9.14 | 1.39 | |
ref, Chl | 0.46 | 6.39 | 5.04 | 1.28 | 0.41 | 10.30 | 8.37 | 1.26 | |
ref, LT, Chl | 0.44 | 6.84 | 5.64 | 1.30 | 0.37 | 12.05 | 7.73 | 1.22 | |
ref, LMA, Chl | 0.47 | 6.49 | 5.50 | 1.39 | 0.43 | 9.34 | 7.99 | 1.38 | |
ref, LT, LMA, Chl | 0.45 | 6.82 | 5.01 | 1.33 | 0.37 | 11.57 | 9.67 | 1.23 |
Period | DNN Model | Vcmax | Jmax | ||||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | RPD | R2 | RMSE | MAE | RPD | ||
Flushing | ref, LT | 0.73 | 11.18 | 8.63 | 1.93 | 0.78 | 17.68 | 12.89 | 2.07 |
ref, LMA | 0.71 | 11.25 | 8.49 | 1.86 | 0.73 | 18.05 | 13.01 | 1.93 | |
ref, Chl | 0.77 | 11.49 | 9.37 | 2.04 | 0.75 | 18.83 | 15.15 | 2.00 | |
ref, LT, Chl | 0.64 | 12.19 | 9.48 | 1.86 | 0.75 | 17.71 | 12.50 | 1.92 | |
ref, LMA, Chl | 0.82 | 10.64 | 9.43 | 2.26 | 0.79 | 17.17 | 13.56 | 2.13 | |
ref, LT, LMA, Chl | 0.82 | 11.31 | 9.63 | 2.01 | 0.83 | 15.65 | 12.33 | 2.21 | |
Maturity | ref, LT | 0.43 | 12.51 | 9.50 | 1.36 | 0.48 | 21.84 | 16.92 | 1.41 |
ref, LMA | 0.49 | 11.89 | 9.24 | 1.45 | 0.49 | 21.79 | 16.70 | 1.40 | |
ref, Chl | 0.41 | 12.22 | 9.79 | 1.33 | 0.49 | 21.57 | 16.84 | 1.41 | |
ref, LT, Chl | 0.48 | 12.15 | 10.04 | 1.41 | 0.46 | 21.63 | 17.71 | 1.39 | |
ref, LMA, Chl | 0.46 | 11.90 | 10.68 | 1.42 | 0.49 | 20.14 | 16.23 | 1.43 | |
ref, LT, LMA, Chl | 0.44 | 12.43 | 9.98 | 1.39 | 0.52 | 20.09 | 16.36 | 1.45 | |
Senescence | ref, LT | 0.49 | 8.24 | 6.56 | 1.42 | 0.52 | 19.31 | 15.45 | 1.44 |
ref, LMA | 0.48 | 8.42 | 6.78 | 1.40 | 0.56 | 18.89 | 14.77 | 1.50 | |
ref, Chl | 0.56 | 7.84 | 6.03 | 1.52 | 0.54 | 19.16 | 15.07 | 1.47 | |
ref, LT, Chl | 0.53 | 8.18 | 6.23 | 1.49 | 0.59 | 18.56 | 14.59 | 1.53 | |
ref, LMA, Chl | 0.51 | 8.40 | 6.67 | 1.43 | 0.53 | 18.58 | 15.14 | 1.46 | |
ref, LT, LMA, Chl | 0.47 | 9.37 | 7.19 | 1.42 | 0.61 | 17.67 | 14.14 | 1.62 |
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Song, G.; Wang, Q. Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance. Remote Sens. 2021, 13, 4467. https://doi.org/10.3390/rs13214467
Song G, Wang Q. Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance. Remote Sensing. 2021; 13(21):4467. https://doi.org/10.3390/rs13214467
Chicago/Turabian StyleSong, Guangman, and Quan Wang. 2021. "Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance" Remote Sensing 13, no. 21: 4467. https://doi.org/10.3390/rs13214467