Abstract
In this monograph, the authors mainly investigated the small UAV applications for tree-level evapotranspiration estimation. Several UAVs and remote sensing payloads were introduced and discussed. Then, the authors proposed new methods for reliable tree-level ET estimation and water status inference with machine learning for an experimental pomegranate orchard at USDA.
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References
Fisher, J.B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R., McCabe, M.F., Hook, S., Baldocchi, D., Townsend, P.A., et al.: The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53(4), 2618–2626 (2017)
Niu, H., Wang, D., Chen, Y.: Estimating actual crop evapotranspiration using deep stochastic configuration networks model and UAV-based crop coefficients in a pomegranate orchard. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenoty** V. International Society for Optics and Photonics, Bellingham (2020)
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Niu, H., Chen, Y. (2022). Conclusion and Future Research. In: Towards Tree-level Evapotranspiration Estimation with Small UAVs in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-14937-5_7
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DOI: https://doi.org/10.1007/978-3-031-14937-5_7
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-031-14937-5
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