Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach
| Autor: | Kaniska Mallick, Vincenzo Magliulo, Olivier Roupsard, Matthias Peichl, Loise Wandera, Gerard Kiely |
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| Přispěvatelé: | UT-I-ITC-WCC, Faculty of Geo-Information Science and Earth Observation, Department of Water Resources |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Cloud cover Image spot 0208 environmental biotechnology Solar zenith angle 02 engineering and technology 01 natural sciences lcsh:Technology FluxNet Evapotranspiration Shortwave radiation lcsh:Environmental technology. Sanitary engineering Hydrologie lcsh:Environmental sciences lcsh:GE1-350 lcsh:Geography. Anthropology. Recreation Overcast Ressource en eau Écosystème F40 - Écologie végétale Meteorology P40 - Météorologie et climatologie Télédétection lcsh:TD1-1066 Conservation de l'eau Mesure P10 - Ressources en eau et leur gestion 0105 earth and related environmental sciences Remote sensing Changement climatique Cartographie Méthode statistique evapotranspiration remote sensing neural networks lcsh:T 15. Life on land Évapotranspiration 020801 environmental engineering lcsh:G 13. Climate action Temporal resolution ITC-ISI-JOURNAL-ARTICLE Environmental science U30 - Méthodes de recherche ITC-GOLD Shortwave |
| Zdroj: | Hydrology and earth system sciences, 21(1), 197-215. Copernicus Hydrology and earth system sciences (2016). doi:10.5194/hess-2016-344 info:cnr-pdr/source/autori:L. Wandera, K. Mallick, G. Kiely, O. Roupsard, M. Peichl, and V. Magliulo/titolo:Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an Artificial Neural Network approach/doi:10.5194%2Fhess-2016-344/rivista:Hydrology and earth system sciences/anno:2016/pagina_da:/pagina_a:/intervallo_pagine:/volume Hydrology and Earth System Sciences, Vol 21, Iss 1, Pp 197-215 (2017) Hydrology and earth system sciences 21 (2017): 197–215. doi:10.5194/hess-21-197-2017 info:cnr-pdr/source/autori:L. Wandera, K. Mallick, G. Kiely, O. Roupsard, M. Peichl, and V. Magliulo/titolo:Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an Artificial Neural Network approach/doi:10.5194%2Fhess-21-197-2017/rivista:Hydrology and earth system sciences/anno:2017/pagina_da:197/pagina_a:215/intervallo_pagine:197–215/volume:21 Hydrology and Earth System Sciences |
| ISSN: | 1607-7938 1027-5606 |
| DOI: | 10.5194/hess-2016-344 |
| Popis: | Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd ∕ RSi) as a factor for converting ETi to ETd.This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd ∕ RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R2 = 0.65 to 0.69), low bias (−0.31 to −0.56 MJ m−2 d−1; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m−2 d−1; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven RS-based ETi upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ETd prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. The overall methodology appears to be promising and has substantial potential for upscaling ETi to ETd for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites. |
| Databáze: | OpenAIRE |
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