Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Data Acquisition
2.2.1. Net Radiation and Micrometeorological Data
2.2.2. Crop Physiology and Solar Radiation Interception Data
2.2.3. UAV Field Campaign
2.3. Remote Sensing Data Processing and ET Modelling
2.3.1. Aerial Imagery Processing
2.3.2. Leaf Area Index (LAI) Estimation
2.3.3. Tree Segmentation
2.3.4. RSEB Algorithm in ET Modeling
2.3.5. Site-Specific Crop ET
3. Results and Discussions
3.1. Canopy Temperatures and NDVI
3.2. Tree-By-Tree Segmentation
3.3. Energy Balance Components
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Park, S.; Ryu, D.; Fuentes, S.; Chung, H.; O’Connell, M.; Kim, J. Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery. ISPRS Int. J. Geo-Inf. 2021, 10, 211. https://doi.org/10.3390/ijgi10040211
Park S, Ryu D, Fuentes S, Chung H, O’Connell M, Kim J. Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery. ISPRS International Journal of Geo-Information. 2021; 10(4):211. https://doi.org/10.3390/ijgi10040211
Chicago/Turabian StylePark, Suyoung, Dongryeol Ryu, Sigfredo Fuentes, Hoam Chung, Mark O’Connell, and Junchul Kim. 2021. "Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery" ISPRS International Journal of Geo-Information 10, no. 4: 211. https://doi.org/10.3390/ijgi10040211