Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. In Situ Observations
2.2. Satellite and Meteorological Input to ET Algorithms
3. Methods
3.1. Process-Based ET Algorithm
3.1.1. Modified Satellite-Based Priestley–Taylor ET Algorithm (MS-PT)
3.1.2. MODIS ET Product Algorithm (MOD16)
3.1.3. Shuttleworth–Wallace Dual Source ET Algorithm (SW)
3.1.4. Priestley–Taylor-Based ET Algorithm (PT-JPL)
3.1.5. Simple Hybrid ET Algorithm (SIM)
3.2. Machine Learning-Based ET Merging Algorithm
- (1)
- Deep neural networks (DNN)
- (2)
- Random forest (RF)
- (3)
- Gradient boosting regression tree (GBRT)
- (4)
- Bayesian model averaging (BMA)
3.3. Evaluation Metrics
4. Results
4.1. Evaluation of Five Processed-Based ET Algorithms and DNN
4.1.1. Validation of Five Processed-Based ET Algorithms
4.1.2. Validation of the DNN
4.2. Map** of DNN over the Globe
5. Discussion
5.1. The Performance of DNN
5.1.1. The Ability of DNN to Estimate ET
5.1.2. The Uncertainties of DNN Estimate
5.2. Advantages and Limitations of DNN
6. Conclusions
- (1)
- There is no single VIIRS-derived ET algorithms that can yield the best ET estimations for all land cover types.
- (2)
- DNN has spatial consistency and relatively lower uncertainty with the highest R2 (0.71), RMSE (21.9 W/m2) and KGE (0.83) outperformed other merging methods (RF, GBRT and BMA) and five individual process-based ET algorithms (SIM, MS-PT, PT-JPL, MOD16 and SW).
Author Contributions
ET Algorithm | Forcing Input of the ET Algorithm | |||
---|---|---|---|---|
MERRA2 | VIIRS | GLASS | MODIS | |
Modified satellite-based Priestley–Taylor ET algorithm (MS-PT) | Ta, DT | NDVI | Rn | |
MODIS ET product algorithm (MOD16) | Ta, Tmin, e, RH | FPAR, LAI, albedo | Rn | Land cover |
Shuttleworth–Wallace dual-source ET algorithm (SW) | RH, Ta, e, SM, WS | FPAR, LAI, NDVI | Rn | |
Priestley–Taylor LE algorithm of Jet Propulsion Laboratory, Caltech (PT-JPL) | Ta, Tmax, e, RH | FPAR, LAI, NDVI | Rn | |
Simple hybrid ET algorithm (SIM) | Ta, Tmax, Tmin | NDVI | Rn |
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Share and Cite
**e, Z.; Yao, Y.; Tang, Q.; Zhang, X.; Zhang, X.; Jiang, B.; Xu, J.; Yu, R.; Liu, L.; Ning, J.; et al. Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data. Remote Sens. 2024, 16, 44. https://doi.org/10.3390/rs16010044
**e Z, Yao Y, Tang Q, Zhang X, Zhang X, Jiang B, Xu J, Yu R, Liu L, Ning J, et al. Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data. Remote Sensing. 2024; 16(1):44. https://doi.org/10.3390/rs16010044
Chicago/Turabian Style**e, Zi**g, Yunjun Yao, Qingxin Tang, Xueyi Zhang, **aotong Zhang, Bo Jiang, Jia Xu, Ruiyang Yu, Lu Liu, **g Ning, and et al. 2024. "Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data" Remote Sensing 16, no. 1: 44. https://doi.org/10.3390/rs16010044