scholarly journals Accuracy of temporal upscaling instantaneous evapotranspiration insimulating daily values in remote sensing applications 

2021 ◽  
Author(s):  
Zhaofei Liu
Keyword(s):  
Function Method ◽  
Potential Evapotranspiration ◽  
Gaussian Function ◽  
Time Range ◽  
Sine Function ◽  
Simulation Accuracy ◽  
Sensing Applications ◽  
Instantaneous Values ◽  
Sky Conditions ◽  
Atmospheric Transmissivity

Abstract. This study evaluated the accuracy of seven upscaling methods in simulating daily latent heat flux (LE) from instantaneous values using observations from 148 global sites under all sky conditions, and at different times during the day. Daily atmospheric transmissivity (τ) was used to represent the sky conditions. The results showed that all seven methods could accurately simulate daily LE from instantaneous values. The mean and median of Nash–Sutcliffe efficiency were 0.80 and 0.85, respectively, and the corresponding determination coefficients were 0.87 and 0.90, respectively. The sine and Gaussian function methods simulated mean values with relatively higher accuracy, with relative errors generally within ±10 %. The evaporative fraction (EF) methods, which use potential evapotranspiration and incoming shortwave radiation, performed relatively better than the other methods in simulating daily series. Overall, the EF method using potential evapotranspiration had the highest accuracy. However, the sine function and the EF method using extraterrestrial solar irradiance are recommended in upscaling applications because of the relatively minimal data requirements of these methods and their comparable or relatively higher accuracy. The intra-day distribution of the LE showed greater consistency with the Gaussian function than the sine function. However, the accuracy of simulated daily LE series using the Gaussian function method did not improve significantly compared with the sine function method. The simulation accuracy showed minor difference when using the same type of methods, for example, the same type of mathematical function or EF method. In any upscaling scheme, the simulation accuracy from multi-time values was significantly higher than that from a single time value. Therefore, when multi-time data are available, multi-time values should be used in evapotranspiration upscaling. The upscaling methods show the ability to accurately simulate daily LE from instantaneous values from 9:00–15:00, particularly for instantaneous values between 11:00 and 14:00. However, outside of this time range the upscaling methods performed poorly. These methods can simulate daily LE series with high accuracy at τ > 0.6; when τ < 0.6, simulation accuracy is significantly affected by sky conditions, and is generally positively related to daily atmospheric transmissivity. Although every upscaling scheme can accurately simulate daily LE from instantaneous values at most sites, this ability is lost at tropical rainforest and tropical monsoon sites.

scholarly journals The accuracy of temporal upscaling of instantaneous evapotranspiration to daily values with seven upscaling methods

2021 ◽  
Vol 25 (8) ◽  
pp. 4417-4433
Author(s):  
Zhaofei Liu
Keyword(s):  
Function Method ◽  
Potential Evapotranspiration ◽  
Gaussian Function ◽  
Time Range ◽  
Mathematical Function ◽  
Sine Function ◽  
Simulation Accuracy ◽  
Instantaneous Values ◽  
Sky Conditions ◽  
Atmospheric Transmissivity

Abstract. This study evaluated the accuracy of seven upscaling methods in simulating daily latent heat flux (LE) from instantaneous values using observations from 148 global sites under all sky conditions and at different times during the day. Daily atmospheric transmissivity (τ) was used to represent the sky conditions. The results showed that all seven methods could accurately simulate daily LE from instantaneous values. The mean and median of Nash–Sutcliffe efficiency were 0.80 and 0.85, respectively, and the corresponding determination coefficients were 0.87 and 0.90, respectively. The sine and Gaussian function methods simulated mean values with relatively higher accuracy, with relative errors generally within ±10 %. The evaporative fraction (EF) methods, which use potential evapotranspiration and incoming shortwave radiation, performed relatively better than the other methods in simulating daily series. Overall, the EF method using potential evapotranspiration had the highest accuracy. However, the sine function and the EF method using extraterrestrial solar irradiance are recommended in upscaling applications because of the relatively minimal data requirements of these methods and their comparable or relatively higher accuracy. The intra-day distribution of the LE showed greater consistency with the Gaussian function than the sine function. However, the accuracy of simulated daily LE series using the Gaussian function method did not improve significantly compared with the sine function method. The simulation accuracy showed a minor difference when using the same type of method, for example, the same type of mathematical function or EF method. In any upscaling scheme, the simulation accuracy from multi-time values was significantly higher than that from a single-time value. Therefore, when multi-time data are available, multi-time values should be used in evapotranspiration upscaling. The upscaling methods show the ability to accurately simulate daily LE from instantaneous values from 09:00 to 15:00, particularly for instantaneous values between 11:00 and 14:00. However, outside of this time range the upscaling methods performed poorly. These methods can simulate daily LE series with high accuracy at τ > 0.6; when τ < 0.6, simulation accuracy is significantly affected by sky conditions and is generally positively related to daily atmospheric transmissivity. Although every upscaling scheme can accurately simulate daily LE from instantaneous values at most sites, this ability is lost at tropical rainforest and tropical monsoon sites.

scholarly journals Measurements of UV irradiance within the area of one satellite pixel

2008 ◽  
Vol 8 (18) ◽  
pp. 5615-5626 ◽  
Author(s):  
P. Weihs ◽  
M. Blumthaler ◽  
H. E. Rieder ◽  
A. Kreuter ◽  
S. Simic ◽  
...  
Keyword(s):  
Ozone Monitoring Instrument ◽  
Short Term ◽  
Uv Index ◽  
Clear Sky ◽  
Uv Irradiance ◽  
Instantaneous Values ◽  
The Mean ◽  
The Difference ◽  
Sky Conditions ◽  
Better Than

Abstract. A measurement campaign was performed in the region of Vienna and its surroundings from May to July 2007. Within the scope of this campaign erythemal UV was measured at six ground stations within a radius of 30 km. First, the homogeneity of the UV levels within the area of one satellite pixel was studied. Second, the ground UV was compared to ground UV retrieved by the ozone monitoring instrument (OMI) onboard the NASA EOS Aura Spacecraft. During clear-sky conditions the mean bias between erythemal UV measured by the different stations was within the measurement uncertainty of ±5%. Short term fluctuations of UV between the stations were below 3% within a radius of 20 km. For partly cloudy conditions and overcast conditions the discrepancy of instantaneous values between the stations is up to 200% or even higher. If averages of the UV index over longer time periods are compared the difference between the stations decreases strongly. The agreement is better than 20% within a distance of 10 km between the stations for 3 h averages. The comparison with OMI UV showed for clear-sky conditions higher satellite retrieved UV values by, on the average, approximately 15%. The ratio of OMI to ground measured UV lies between 0.9 and 1.5. and strongly depends on the aerosol optical depth. For partly cloudy and overcast conditions the OMI derived surface UV estimates show larger deviation from the ground-based reference data, and even bigger systematic positive bias. Here the ratio OMI to ground data lies between 0.5 and 4.5. The average difference between OMI and ground measurements is +24 to +37% for partly cloudy conditions and more than +50% for overcast conditions.

scholarly journals Based on the Gaussian Fitting Method to Derive Daily Evapotranspiration from Remotely Sensed Instantaneous Evapotranspiration

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jing Tian ◽  
Shaohui Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Function Method ◽  
Water Resource Management ◽  
Remotely Sensed ◽  
Estimation Accuracy ◽  
Average Error ◽  
Sine Function ◽  
Gaussian Fitting ◽  
Fitting Method

Evapotranspiration (ET) is a significant component in the water cycle, and the estimation of it is imperative in water resource management. Regional ET can be derived by using remote sensing technology which combines remote sensing inputs with ground-based measurements. However, instantaneous ET values estimated through remote sensing directly need to be converted into daily totals. In this study, we attempted to retrieve daily ET from remotely sensed instantaneous ET. The study found that the Gaussian fitting curve closely followed the ET measurements during the daytime and hence put forward the Gaussian fitting method to convert the remotely sensed instantaneous ET into daily ETs. The method was applied to the middle reaches of Heihe River in China. Daily ETs on four days were derived and evaluated with ET measurements from the eddy covariance (EC) system. The correlation between daily ET estimates and measurements showed high accuracy, with a coefficient of determination (R2) of 0.82, a mean average error (MAE) of 0.41 mm, and a root mean square error (RMSE) of 0.46 mm. To make more scientific assessments, percent errors were calculated on the estimation accuracy, which ranged from 0% to 18%, with more than 80% of locations having the percent errors within 10%. Analyses on the relationship between daily ET estimates and land use status were also made to assess the Gaussian fitting method, and the results showed that the spatial distribution of daily ET estimates well demonstrated ET differences caused by land use types and was intimately linked with the vegetation pattern. The comparison between the Gaussian fitting method and the sine function method and the ETrF method indicated that results derived through the Gaussian fitting method had higher precision than that obtained by the sine function method and the ETrF method.

scholarly journals Sine-Function Method In The Soliton Solution Of Nonlinear Partial Differential Equations

2013 ◽  
Vol 32 ◽  
pp. 55-60 ◽  
Author(s):  
M Abdur Rab ◽  
Jasmin Akhter
Keyword(s):  
Wave Equation ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Function Method ◽  
Nonlinear Partial Differential Equations ◽  
Traveling Wave Solution ◽  
Sine Function ◽  
Partial Differential ◽  
Regularized Long Wave Equation ◽  
Different Types

In this paper we establish a traveling wave solution for nonlinear partial differential equations using sine-function method. The method is used to obtain the exact solutions for three different types of nonlinear partial differential equations like general equal width wave equation (GEWE), general regularized long wave equation (GRLW), general Korteweg-de Vries equation(GKDV) which are the important soliton equations DOI: http://dx.doi.org/10.3329/ganit.v32i0.13647 GANIT J. Bangladesh Math. Soc. (ISSN 1606-3694) 32 (2012) 55-60

scholarly journals Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an Artificial Neural Network approach

2016 ◽  
Author(s):  
Loise Wandera ◽  
Kaniska Mallick ◽  
Gerard Kiely ◽  
Olivier Roupsard ◽  
Matthias Peichl ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regional Scale ◽  
Short Wave ◽  
Shortwave Radiation ◽  
Wave Radiation ◽  
Sensing Applications ◽  
Daily Evapotranspiration ◽  
Artificial Neural ◽  
Sky Conditions

Abstract. Upscaling instantaneous evapotranspiration retrieved at any specific time-of-daytime (ETi) to daily evapotranspiration (ETd) is a key challenge in regional scale vegetation water use mapping using polar orbiting sensors. Various studies have unanimously cited the short wave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi on the terrestrial surfaces. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming short wave radiation (RSd/RSi) as a factor for converting ETi to ETd. The approach relies on the availability of RSd measurements that in many cases is hindered if not by cost but due to the environmental conditions such as cloudiness. 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 ecosystem. Using RSi and RSd observations from multiple subnetworks of 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 (derived from simple mathematical computation), we developed 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 time-of-daytime, under varying sky conditions, and also at different geographic locations. Based on the measurements from 126 sites, we found RS-based upscaled ETd to produce a significant linear relation (R2 = 0.65 to 0.69), low bias (−0.31 to −0.56 MJ m−2 d−1) (appx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m−2 d−1) (appx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN driven RS method and was found to produce lowest RMSE under cloudy conditions. 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.

scholarly journals Using a Gaussian Function to Describe the Seasonal Courses of Monthly Precipitation and Potential Evapotranspiration across the Yellow River Basin, China

2019 ◽  
Vol 20 (11) ◽  
pp. 2185-2201 ◽  
Author(s):  
Yaqin Wang ◽  
Yi Luo ◽  
Muhammad Shafeeque
Keyword(s):  
Water Supply ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Seasonal Pattern ◽  
Gaussian Function ◽  
Yellow River Basin ◽  
Energy Availability ◽  
The Yellow River Basin ◽  
Regional Water ◽  
Insight Into

Abstract Seasonal variations in precipitation (P) and potential evapotranspiration (ET0) are critical for regional hydrometeorological studies and water resource management. The sinusoidal function is widely used to describe the seasonal pattern of P and ET0. However, high errors occur either in the arid places or in places with hyperseasonal precipitation. These limitations are intrinsic properties of the sinusoidal climate descriptor and remain a barrier to provide insight into regional water–energy partitions and hydrologic similarity and predictability. In this study, we used a Gaussian framework as an alternative to describe seasonal variations in P and ET0 regimes in the Yellow River basin (YRB). The results show that the Gaussian framework provides a good approximation to the seasonal pattern of P and has a strong regional applicability for reproducing the monthly P and ET0. This allows us to assess the climate seasonality characterizing the regional balance between water supply and energy availability using δP, δET0, and aridity index. The climate seasonality indicates that the balance between water supply and energy availability has a switch in about 32% of the grid cells during the seasonal cycle from 1982 to 2015. These grid cells are mostly located in regions with average annual precipitation above 550 mm. In the northwest region of the YRB, which has a dry climate, the amount of potential evapotranspiration always exceeds the precipitation. We argue that the Gaussian function provides a quantitative conceptual framework for accurate assessment of regional water supply and energy availability and offers a penetrating insight into hydrometeorology.

scholarly journals Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach

2017 ◽  
Vol 21 (1) ◽  
pp. 197-215 ◽  
Author(s):  
Loise Wandera ◽  
Kaniska Mallick ◽  
Gerard Kiely ◽  
Olivier Roupsard ◽  
Matthias Peichl ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regional Scale ◽  
Time Of Day ◽  
Day Length ◽  
Shortwave Radiation ◽  
Sensing Applications ◽  
Daily Evapotranspiration ◽  
Artificial Neural ◽  
Sky Conditions

Abstract. 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.

scholarly journals Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications

2013 ◽  
Vol 10 (6) ◽  
pp. 7325-7350 ◽  
Author(s):  
C. Cammalleri ◽  
M. C. Anderson ◽  
W. P. Kustas
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
Eddy Covariance ◽  
Time Of Day ◽  
Surface Solar Radiation ◽  
Evaluation Approach ◽  
Sensing Applications ◽  
Wide Range ◽  
Systematic Biases ◽  
Sky Conditions

Abstract. Four upscaling methods for estimating daytime evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods are based on the assumption of self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggests that at-surface solar radiation is the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux did not perform as well as the solar radiation method, but similarly had errors with little seasonal dependency. Finally, the commonly-used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied.

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