scholarly journals Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images

2017 ◽  
Vol 21 (12) ◽  
pp. 6135-6151 ◽  
Author(s):  
Rangaswamy Madugundu ◽  
Khalid A. Al-Gaadi ◽  
ElKamil Tola ◽  
Abdalhaleem A. Hassaballa ◽  
Virupakshagouda C. Patil

Abstract. Accurate estimation of evapotranspiration (ET) is essential for hydrological modeling and efficient crop water management in hyper-arid climates. In this study, we applied the METRIC algorithm on Landsat-8 images, acquired from June to October 2013, for the mapping of ET of a 50 ha center-pivot irrigated alfalfa field in the eastern region of Saudi Arabia. The METRIC-estimated energy balance components and ET were evaluated against the data provided by an eddy covariance (EC) flux tower installed in the field. Results indicated that the METRIC algorithm provided accurate ET estimates over the study area, with RMSE values of 0.13 and 4.15 mm d−1. The METRIC algorithm was observed to perform better in full canopy conditions compared to partial canopy conditions. On average, the METRIC algorithm overestimated the hourly ET by 6.6 % in comparison to the EC measurements; however, the daily ET was underestimated by 4.2 %.

2017 ◽  
Author(s):  
Rangaswamy Madugundu ◽  
Khalid A. Al-Gaadi ◽  
ElKamil Tola ◽  
Abdalhaleem A. Hassaballa ◽  
Virupakshagouda C. Patil

Abstract. Accurate estimation of evapotranspiration (ET) is essential for hydrological modelling and efficient crop water management in hyper-arid climates, like the one in the Eastern Region of Saudi Arabia. Therefore, a study was designed to apply the METRIC algorithm on Landsat-8 images, acquired from June to October 2013, for the development of ET maps for a 50-ha center pivot irrigated alfalfa field. The METRIC estimated energy balance components and ET were evaluated against the data provided by an Eddy Covariance (EC) flux tower installed in the field. Results indicated that the METRIC algorithm provided accurate ET estimates over the study area, with RMSE values of 0.09 mm h−1 and 0.38 mm d−1. The METRIC algorithm was observed to perform a relatively better in full canopy conditions compared to that in partial canopy conditions. On the average, the METRIC algorithm overestimated the hourly ET by 6.6 % in comparison to the EC measurements; however, the daily ET was underestimated by 4.2 %.


2020 ◽  
Vol 24 (11) ◽  
pp. 5251-5277 ◽  
Author(s):  
Oliver Miguel López Valencia ◽  
Kasper Johansen ◽  
Bruno José Luis Aragón Solorio ◽  
Ting Li ◽  
Rasmus Houborg ◽  
...  

Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 individual fields across an area exceeding 2500 km2. Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data–model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.


2020 ◽  
Author(s):  
Oliver Lopez ◽  
Kasper Johansen ◽  
Bruno Aragon ◽  
Ting Li ◽  
Rasmus Houborg ◽  
...  

Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields, and their varying levels of water exploitation through time, than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection campaigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model, was developed to provide information on both irrigated crop-water use and groundwater abstraction rates. Results from the local-scale, comprising several thousand individual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo inputs, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables, and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each individual center-pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center-pivots in Al Kharj that was used for an initial evaluation, and a much larger agricultural region in Al Jawf province comprising more than 5,000 individual fields across an area exceeding 2,500 km2. Total groundwater abstraction for the year 2015 in Al Jawf were estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data-model fusion approach facilitates the compilation of national-scale groundwater abstractions, while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.


Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 188 ◽  
Author(s):  
Huaiwei Sun ◽  
Yong Yang ◽  
Ruiying Wu ◽  
Dongwei Gui ◽  
Jie Xue ◽  
...  

Evapotranspiration (ET) is one of the key components of the global hydrological cycle. Many models have been established to obtain an accurate estimation of ET, but the uncertainty of each model has not been satisfactorily addressed, and the weight determination in multi-model simulation methods remains unclear. In this study, the Bayesian model averaging (BMA) method was adopted to tackle this issue. We explored the combination of four surface energy balance (SEB) models (SEBAL, SSEB, S-SEBI and SEBS) with the BMA method by using Landsat 8 images over two study areas in China, the Huailai flux station (semiarid region) and the Sidaoqiao flux station (arid/semiarid region), and the data from two stations were used as validation for this method. The performances of SEB models and different BMA methods is revealed by three statistical parameters (i.e., the coefficient of determination (R2), root mean squared error (RMSE), and the Nash-Sutcliffe efficiency coefficient (NSE)). We found the best performing SEB model was SEBAL, with an R2 of 0.609 (0.672), RMSE of 1.345 (0.876) mm/day, and NSE of 0.407 (0.563) at Huailai (Sidaoqiao) station. Compared with the four individual SEB models, each of the BMA methods (fixed, posterior inclusion probability, or random) can provide a more accurate and reliable simulation result. Similarly, in Huailai (Sidaoqiao) station, the best performing BMA random model provided an R2 of 0.750 (0.796), RMSE of 0.902 (0.602) mm/day, and NSE of 0.746 (0.793). We conclude that the BMA method outperformed the four SEB models alone and obtained a more accurate prediction of ET in two cropland areas, which provides important guidance for water resource allocation and management in arid and semiarid regions.


2021 ◽  
Author(s):  
Ramesh Dhungel ◽  
Rob Aiken ◽  
Xiaomao Lin ◽  
Paul D. Colaizzi ◽  
R. Louis Baumhardt ◽  
...  

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Arturo Reyes-González ◽  
Jeppe Kjaersgaard ◽  
Todd Trooien ◽  
Christopher Hay ◽  
Laurent Ahiablame

Accurate estimation of crop evapotranspiration (ET) is a key factor in agricultural water management including irrigated agriculture. The objective of this study was to compare ET estimated from the satellite-based remote sensing METRIC model to in situ atmometer readings. Atmometer readings were recorded from three sites in eastern South Dakota every morning between 8:15 and 8:30 AM for the duration of the 2016 growing season. Seven corresponding clear sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for comparison. Three corn fields in three sites were used to compare actual evapotranspiration (ETa). The results showed a good relationship between ETa estimated by the METRIC model (ETa-METRIC) and ETa estimated with atmometer (ETa-atm) (r2 = 0.87, index of agreement of 0.84, and RMSE = 0.65 mm day−1). However, ETa-atm values were consistently lower than ETa-METRIC values. The differences in daily ETa between the two methods increase with high wind speed values (>4 m s−1). Results from this study are useful for improving irrigation water management at local and field scales.


PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0192830 ◽  
Author(s):  
Rangaswamy Madugundu ◽  
Khalid A. Al-Gaadi ◽  
ElKamil Tola ◽  
Abdalhaleem A. Hassaballa ◽  
Ahmed G. Kayad

Author(s):  
A. Basit ◽  
R. Z. Khalil ◽  
S. Haque

<p><strong>Abstract.</strong> Assessment and monitoring of crop water requirement (CWR) or crop evapotranspiration (ETc) over a large spatial scale is the critical component for irrigation and drought management. Due to growing competition and increasing shortage of water, careful utilization of water in irrigation is essential. The usage of water for irrigation/agriculture is a top priority for countries like Pakistan, where the GDP mostly based on agriculture, and its scarcity may affect the crop production. Remote sensing techniques can be used to estimate crop water requirement or crop evapotranspiration which can help in efficient irrigation. Simplified-surface energy balance index (SSEBI) model is used to estimate evapotranspiration (ET) of wheat during 2015&amp;ndash;16 growing period in Tando Adam, Sindh. Landsat-8 satellite data for the corresponding years were used. With the help of National Agromet Centre report chart of Crop coefficient (Kc) the CWR, ETc of all phonological stages were estimated. Results indicated that maximum ET and maximum CWR were found in the third leaf to tillering stage with a value of 0.75 and 0.89 respectively. This study will help in managing and monitoring of ET spatial distribution over irrigated crops which results in better irrigation scheduling and water consumption.</p>


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