DOI: 10.5194/hess-21-197-2017
Scopus记录号: 2-s2.0-85009260526
论文题名: Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach
作者: Wandera L ; , Mallick K ; , Kiely G ; , Roupsard O ; , Peichl M ; , Magliulo V
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 1 起始页码: 197
结束页码: 215
语种: 英语
Scopus关键词: Climate models
; Ecology
; Ecosystems
; Evapotranspiration
; Learning algorithms
; Learning systems
; Mapping
; Neural networks
; Rain
; Sensitivity analysis
; Silicon
; Soil moisture
; Artificial neural network approach
; Daily evapotranspirations
; Geographic location
; Polar-orbiting satellites
; Remote sensing applications
; Short-wave radiation
; Temporal resolution
; Terrestrial ecosystems
; Remote sensing
; algorithm
; artificial neural network
; evapotranspiration
; forest ecosystem
; machine learning
; remote sensing
; satellite altimetry
; sensitivity analysis
; sensor
; shortwave radiation
; soil moisture
; upscaling
英文摘要: 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-0.56MJm-2d-1 approx. 4%), and good agreement (RMSE 1.55 to 1.86 MJm-2d-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. © 2017 The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79300
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Remote Sensing and Ecohydrological Modeling, Dept. ERIN, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg; Water Resources, Dept. ITC, University of Twente, Enschede, Netherlands; Civil and Environmental Engineering Dept., Environmental Research Institute, University College Cork, Cork, Ireland; CIRAD, UMR EcoandSols, 2 Place Viala, Montpellier, France; CATIE, Centro Agronómico Tropical de Investigación y Ensenañza, Turrialba, Cartago, Costa Rica; Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeä, Sweden; Consiglio Nazionale Delle Ricerche, ISAFOM, Ercolano, Naples, Italy
Recommended Citation:
Wandera L,, Mallick K,, Kiely G,et al. Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach[J]. Hydrology and Earth System Sciences,2017-01-01,21(1)