Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods
Abstract
:1. Introduction
2. Data and Preprocessing
2.1. The BELMANIP Network
2.2. The VIIRS Surface Reflectance Data and Reconstruction
2.3. The GLASS FVC Data
2.4. An Independent Validation Case in Heihe Region
3. Methods
3.1. Training Samples Selection
3.2. BPNNs
3.3. GRNNs
3.4. MARS
3.5. GPR
4. Results
4.1. The VIIRS Reflectance Data Reconstruction
4.2. Training Samples
4.3. Theoretical Performances of Machine Learning Methods
4.4. Temporal–Spatial Comparisons
4.5. Direct Accuracy Validation in Heihe Region
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgement
Conflicts of Interest
References
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Data Type | Spatial Resolution | Acquisition Dates |
---|---|---|
ASTER | 15 m | 05/30, 06/15, 06/24, 07/10, 08/02, 08/11, 08/18, 08/27, 09/03 |
CASI | 1 m | 06/29, 07/07 |
Parameter | maxFuncs | C | Threshold |
---|---|---|---|
Value | 201 | 3 | 1.0 × 10−4 |
Models | RMSE | R2 | Computational Time (s) | Statistical Formula |
---|---|---|---|---|
BPNNs | 0.0909 | 0.8973 | 0.0172 | y = 0.8853x + 0.0603 |
GRNNs | 0.0893 | 0.9006 | 19.4393 | y = 0.9013x + 0.0503 |
MARS | 0.0891 | 0.9011 | 0.0089 | y = 0.9972x − 0.0004 |
GPR | 0.0887 | 0.9019 | 9.4425 | y = 0.9041x + 0.0489 |
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Liu, D.; Yang, L.; Jia, K.; Liang, S.; Xiao, Z.; Wei, X.; Yao, Y.; Xia, M.; Li, Y. Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods. Remote Sens. 2018, 10, 1648. https://doi.org/10.3390/rs10101648
Liu D, Yang L, Jia K, Liang S, Xiao Z, Wei X, Yao Y, Xia M, Li Y. Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods. Remote Sensing. 2018; 10(10):1648. https://doi.org/10.3390/rs10101648
Chicago/Turabian StyleLiu, Duanyang, Linqing Yang, Kun Jia, Shunlin Liang, Zhiqiang Xiao, Xiangqin Wei, Yunjun Yao, Mu Xia, and Yuwei Li. 2018. "Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods" Remote Sensing 10, no. 10: 1648. https://doi.org/10.3390/rs10101648