Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Remotely Sensed Data
2.2.2. Calibration and Validation Data
2.3. Crop and Green-Vegetation Masking
2.4. Machine Learning Regression Algorithms
2.4.1. Sparse Partial Least Squares
2.4.2. Random Forest
2.4.3. Gradient Boosting Machines
2.5. Prediction Accuracy Assessment
3. Results
3.1. Optimal Tuning Parameters
3.2. Model Performance
3.3. Biophysical Parameter Mapping
3.4. Variable Importance
4. Discussion
4.1. Predictive Performance of Machine Learning Regression Algorithms
4.2. Influential Variables for Biophysical Parameter Estimation
4.3. Limitations of the Study
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Datasets | n | Min | Mean | Max | SD | |
---|---|---|---|---|---|---|
LAI | Calibration | 113 | 1.78 | 3.35 | 5.57 | 0.86 |
Validation | 48 | 2.02 | 3.60 | 5.75 | 1 | |
LCab | Calibration | 113 | 4.06 | 33.87 | 66.18 | 14.93 |
Validation | 48 | 3.69 | 32.62 | 70.69 | 19.20 | |
CCC | Calibration | 113 | 10.30 | 105.69 | 288.22 | 61.60 |
Validation | 48 | 7.87 | 116.42 | 339.10 | 90.39 |
Parameters | Description |
---|---|
Number of trees (T) | This is the total number of trees to fit or iterations. |
Tree depth (K) | The depth of a tree determines the number of splits in each tree to control the complexity of the boosted ensemble. |
Learning rate (⋋) | The learning rate controls the speed of the algorithm down the gradient descent. The smaller values improve the performance and reduce the chance of overfitting. |
Subsample (p) | The subsample ratio of the training instance controls the randomly collected data instance to grow trees. For example, a value of 0.5 causes GBM to randomly collect half of the data instances and prevent overfitting through implementing stochastic gradient descent. The values for this parameter should be between 0 and 1. |
sPLS | RF | GBM | |
---|---|---|---|
LAI | eta = 0.7; K = 5; p = 10; RMSECV = 0.8 | mtry = 5; OOB error = 0.34 | ntrees = 146; interaction depth = 5; shrinkage = 0.1; n.minobsinnode = 15; RMSECV =0.65 |
LCab | eta = 0.9; K = 5; p = 7 *; RMSECV = 7.58 | mtry = 3; OOB error = 7.21 | ntrees = 28; interaction depth = 3; shrinkage = 0.1; n.minobsinnode = 15; RMSECV = 7.56 |
CCC | eta = 0.8; K = 5; p = 10; RMSECV = 41.22 | mtry = 2; OOB error = 34.56 | ntrees = 94; interaction depth = 5; shrinkage = 0.1; n.minobsinnode = 15; RMSECV = 37.21 |
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Kganyago, M.; Mhangara, P.; Adjorlolo, C. Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery. Remote Sens. 2021, 13, 4314. https://doi.org/10.3390/rs13214314
Kganyago M, Mhangara P, Adjorlolo C. Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery. Remote Sensing. 2021; 13(21):4314. https://doi.org/10.3390/rs13214314
Chicago/Turabian StyleKganyago, Mahlatse, Paidamwoyo Mhangara, and Clement Adjorlolo. 2021. "Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery" Remote Sensing 13, no. 21: 4314. https://doi.org/10.3390/rs13214314