Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-6041-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6041-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2021
Research article |
| 25 Nov 2021
| 25 Nov 2021
A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration
Jiancong Chen
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of
California, Berkeley, CA, USA,
Baptiste Dafflon
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Anh Phuong Tran
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of
Water Resources Engineering and Technology, Water Resources Institute, 8,
Phao Dai Lang, Dong Da, Hanoi, Vietnam
Nicola Falco
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Susan S. Hubbard
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Cited
14 citations as recorded by crossref.
- Deep learning and satellite remote sensing for biodiversity monitoring and conservation N. Pettorelli et al. 10.1002/rse2.415
- A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin X. Jiang et al. 10.3390/rs15092234
- A physics-constrained deep learning framework for actual evapotranspiration estimation using ground station observations and remote sensing data L. Wang et al. 10.1016/j.envsoft.2025.106585
- Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: Constraining an optimal stomatal model within a two-source energy balance model J. Bu et al. 10.1016/j.rse.2024.113999
- Advanced monitoring of soil-vegetation co-dynamics reveals the successive controls of snowmelt on soil moisture and on plant seasonal dynamics in a mountainous watershed B. Dafflon et al. 10.3389/feart.2023.976227
- Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
- Application and Uncertainty Analysis of Data-Driven and Process-Based Evapotranspiration Models Across Various Ecosystems Q. Wang et al. 10.1007/s11269-024-03772-5
- Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands W. Liu et al. 10.3390/rs14153563
- An Outlook for Deep Learning in Ecosystem Science G. Perry et al. 10.1007/s10021-022-00789-y
- Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces J. Feng et al. 10.1016/j.agwat.2023.108627
- Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques X. Jiang et al. 10.1016/j.agwat.2024.108755
- An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence M. Taheri et al. 10.3390/w17091384
- The Colorado East River Community Observatory Data Collection Z. Kakalia et al. 10.1002/hyp.14243
- Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review M. Kamarudin et al. 10.3390/app11041403
12 citations as recorded by crossref.
- Deep learning and satellite remote sensing for biodiversity monitoring and conservation N. Pettorelli et al. 10.1002/rse2.415
- A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin X. Jiang et al. 10.3390/rs15092234
- A physics-constrained deep learning framework for actual evapotranspiration estimation using ground station observations and remote sensing data L. Wang et al. 10.1016/j.envsoft.2025.106585
- Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: Constraining an optimal stomatal model within a two-source energy balance model J. Bu et al. 10.1016/j.rse.2024.113999
- Advanced monitoring of soil-vegetation co-dynamics reveals the successive controls of snowmelt on soil moisture and on plant seasonal dynamics in a mountainous watershed B. Dafflon et al. 10.3389/feart.2023.976227
- Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
- Application and Uncertainty Analysis of Data-Driven and Process-Based Evapotranspiration Models Across Various Ecosystems Q. Wang et al. 10.1007/s11269-024-03772-5
- Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands W. Liu et al. 10.3390/rs14153563
- An Outlook for Deep Learning in Ecosystem Science G. Perry et al. 10.1007/s10021-022-00789-y
- Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces J. Feng et al. 10.1016/j.agwat.2023.108627
- Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques X. Jiang et al. 10.1016/j.agwat.2024.108755
- An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence M. Taheri et al. 10.3390/w17091384
Latest update: 16 Jul 2025
Short summary
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent neural network to estimate evapotranspiration (ET) and ecosystem respiration (Reco) with only meteorological and remote-sensing inputs. We developed four use cases to demonstrate the applicability of HPM. The results indicate HPM is capable of providing ET and Reco estimations in challenging mountainous systems and enhances our understanding of watershed dynamics at sparsely monitored watersheds.
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent...
