Remote Sensing Approach for Estimating Evapotranspiration Using Satellite-Based Energy Balance Models in Al Hamra, Oman

2021 ◽  
pp. 73-79
Author(s):  
Ahsan Ali ◽  
Yaseen Al-Mulla ◽  
Yassin Charabi ◽  
Ghazi Al-Rawas ◽  
Malik Al-Wardy
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Energy Balance Models

scholarly journals Comparison of the Remote Sensing Based-Energy Balance Models for Estimating Evaporation from Salin Lakes

2019 ◽  
Vol 7 (2) ◽  
pp. 155-175
Author(s):  
Mostafa Khorsand Movaghar ◽  
Somayeh Sima ◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Energy Balance Models

Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models

2015 ◽  
Vol 158 ◽  
pp. 281-294 ◽  
Author(s):  
Andrew N. French ◽  
Douglas J. Hunsaker ◽  
Kelly R. Thorp
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Energy Balance Models

scholarly journals Evapotranspiration and evaporation/transpiration partitioning with dual source energy balance models in agricultural lands

2018 ◽  
Vol 380 ◽  
pp. 17-22 ◽  
Author(s):  
Gilles Boulet ◽  
Emilie Delogu ◽  
Sameh Saadi ◽  
Wafa Chebbi ◽  
Albert Olioso ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Stress Levels ◽  
Dual Source ◽  
Energy Balance Models

Abstract. EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.

scholarly journals A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution

2014 ◽  
Vol XL-8 ◽  
pp. 1397-1401
Author(s):  
V. M. Bindhu ◽  
B. Narasimhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Energy Balance ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fine Spatial Resolution ◽  
Temporal Disaggregation ◽  
Energy Balance Models ◽  
Spatio Temporal ◽  
Fine Resolution

Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.

Evaluating the effect of different management practices on vineyard evapotranspiration by using remote sensing-based energy balance models

2021 ◽  
pp. 53-60
Author(s):  
J.M. Ramírez-Cuesta ◽  
I. Buesa ◽  
M.A. Moreno ◽  
R. Ballesteros ◽  
D. Hernández-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Management Practices ◽  
Energy Balance Models

scholarly journals EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 9-12
Author(s):  
G. Boulet ◽  
E. Delogu ◽  
W. Chebbi ◽  
Z. Rafi ◽  
V. Le Dantec ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Surface Energy Balance ◽  
Source Surface ◽  
Dual Source ◽  
Energy Balance Models

<p><strong>Abstract.</strong> Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.</p>

Collection Of Ground Data For Pixel Scale Parameterization Of Energy Balance Models For Remote Sensing Purposes

2005 ◽  
Author(s):  
A.A. Van De Griend ◽  
M. Owe ◽  
H.F. Vugts ◽  
S.D. Prince ◽  
F.W. Tesselaar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Energy Balance Models ◽  
Pixel Scale
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