Articles | Volume 7, issue 2
https://doi.org/10.5194/gi-7-151-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-7-151-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2018
Research article |
| 07 Jun 2018
| 07 Jun 2018
Evaluating four gap-filling methods for eddy covariance measurements of evapotranspiration over hilly crop fields
Nissaf Boudhina
LISAH, IRD, INRA, Montpellier SupAgro, University of Montpellier, 34060 Montpellier, France
Institut National Agronomique de Tunisie (INAT)/Carthage University, Tunis, Tunisia
Institut National de Recherche en Génie Rural, Eaux et Forêts (INRGREF)/Carthage University, Ariana, Tunisia
Insaf Mekki
Institut National de Recherche en Génie Rural, Eaux et Forêts (INRGREF)/Carthage University, Ariana, Tunisia
Frédéric Jacob
LISAH, IRD, INRA, Montpellier SupAgro, University of Montpellier, 34060 Montpellier, France
Institut National de Recherche en Génie Rural, Eaux et Forêts (INRGREF)/Carthage University, Ariana, Tunisia
Nétij Ben Mechlia
Institut National Agronomique de Tunisie (INAT)/Carthage University, Tunis, Tunisia
Moncef Masmoudi
Institut National Agronomique de Tunisie (INAT)/Carthage University, Tunis, Tunisia
Laurent Prévot
Institut National de Recherche en Génie Rural, Eaux et Forêts (INRGREF)/Carthage University, Ariana, Tunisia
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Cited
15 citations as recorded by crossref.
- An analog period method for gap‐filling of latent heat flux measurements L. Hoeltgebaum et al. 10.1002/hyp.14105
- Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms X. Cui et al. 10.1016/j.scitotenv.2021.145130
- Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data L. Fine et al. 10.3390/w14050763
- Multicriteria evaluation of the AquaCrop crop model in a hilly rainfed Mediterranean agrosystem M. Dhouib et al. 10.1016/j.agwat.2022.107912
- Providing a comprehensive understanding of missing data imputation processes in evapotranspiration-related research: a systematic literature review E. Başakın et al. 10.1080/02626667.2023.2249456
- A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration Y. Jiang et al. 10.1016/j.agrformet.2022.109087
- Estimating soil available water capacity within a Mediterranean vineyard watershed using satellite imagery and crop model inversion M. Alkassem et al. 10.1016/j.geoderma.2022.116081
- Benefits of a robotic chamber system for determining evapotranspiration in an erosion-affected, heterogeneous cropland A. Dahlmann et al. 10.5194/hess-27-3851-2023
- Evaluation of the storage and evapotranspiration terms of the water budget for an agricultural watershed using local and remote-sensing measurements L. Hoeltgebaum & N. Dias 10.1016/j.agrformet.2023.109615
- A comparison of three models used to determine water fluxes over the Albany Thicket, Eastern Cape, South Africa A. Palmer et al. 10.1016/j.agrformet.2020.107984
- Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems D. Guo et al. 10.3390/rs14051286
- A gap filling method for daily evapotranspiration of global flux data sets based on deep learning L. Qian et al. 10.1016/j.jhydrol.2024.131787
- Documenting evapotranspiration and surface energy fluxes over rainfed annual crops within a Mediterranean hilly agrosystem R. Zitouna-Chebbi et al. 10.1016/j.agwat.2022.108117
- Eddy Covariance CO2 Flux Gap Filling for Long Data Gaps: A Novel Framework Based on Machine Learning and Time Series Decomposition D. Gao et al. 10.3390/rs15102695
- Surface eddy fluxes and friction velocity estimates taking measurements at the canopy top F. Castellví et al. 10.1016/j.agwat.2020.106358
15 citations as recorded by crossref.
- An analog period method for gap‐filling of latent heat flux measurements L. Hoeltgebaum et al. 10.1002/hyp.14105
- Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms X. Cui et al. 10.1016/j.scitotenv.2021.145130
- Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data L. Fine et al. 10.3390/w14050763
- Multicriteria evaluation of the AquaCrop crop model in a hilly rainfed Mediterranean agrosystem M. Dhouib et al. 10.1016/j.agwat.2022.107912
- Providing a comprehensive understanding of missing data imputation processes in evapotranspiration-related research: a systematic literature review E. Başakın et al. 10.1080/02626667.2023.2249456
- A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration Y. Jiang et al. 10.1016/j.agrformet.2022.109087
- Estimating soil available water capacity within a Mediterranean vineyard watershed using satellite imagery and crop model inversion M. Alkassem et al. 10.1016/j.geoderma.2022.116081
- Benefits of a robotic chamber system for determining evapotranspiration in an erosion-affected, heterogeneous cropland A. Dahlmann et al. 10.5194/hess-27-3851-2023
- Evaluation of the storage and evapotranspiration terms of the water budget for an agricultural watershed using local and remote-sensing measurements L. Hoeltgebaum & N. Dias 10.1016/j.agrformet.2023.109615
- A comparison of three models used to determine water fluxes over the Albany Thicket, Eastern Cape, South Africa A. Palmer et al. 10.1016/j.agrformet.2020.107984
- Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems D. Guo et al. 10.3390/rs14051286
- A gap filling method for daily evapotranspiration of global flux data sets based on deep learning L. Qian et al. 10.1016/j.jhydrol.2024.131787
- Documenting evapotranspiration and surface energy fluxes over rainfed annual crops within a Mediterranean hilly agrosystem R. Zitouna-Chebbi et al. 10.1016/j.agwat.2022.108117
- Eddy Covariance CO2 Flux Gap Filling for Long Data Gaps: A Novel Framework Based on Machine Learning and Time Series Decomposition D. Gao et al. 10.3390/rs15102695
- Surface eddy fluxes and friction velocity estimates taking measurements at the canopy top F. Castellví et al. 10.1016/j.agwat.2020.106358
Latest update: 20 Nov 2024
Short summary
To provide reliable time series of evapotranspiration, we evaluated the performances of four different gap-filling methods when tailored to conditions of hilly crop fields. The tailoring consisted of splitting the time series beforehand on the basis of upslope and downslope winds. The obtained accuracies on evapotranspiration after gap filling were comparable to those previously reported over flat and mountainous terrains, and they were better with the most widely used gap-filling method.
To provide reliable time series of evapotranspiration, we evaluated the performances of four...
