Remote Sensing of Evapotranspiration Using SEBAL and Metric Energy Balance Models for Enhanced Precision Agriculture Cotton Irrigation Scheduling

2020 ◽  
Author(s):  
Francesco Morari ◽  
Ahmed Harb Rabia ◽  
Stefano Lo Presti ◽  
Stefano Gobbo ◽  
George Vellidis
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Precision Agriculture ◽  
Irrigation Schedule ◽  
Plant Cover ◽  
Crop Coefficient ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Balance Model ◽  
Scheduling Irrigation

<p>Irrigation scheduling is one of the main factors that affect the crop ability to resist stress symptoms in addition to affecting directly the final yield. In the last decade, many remote sensing methods have been developed to help in scheduling irrigation with higher precision. Some of these methods estimate irrigation needs indirectly such as those using normalized difference vegetation index (NDVI) or crop coefficient curve, and other methods that directly calculate Evapotranspiration (ET) through satellite images. Cotton SmartIrrigation App (Cotton App) is one of the recent applications that have been developed to help farmers in scheduling irrigation during the growing season. The App is based on an interactive ET-based soil water balance model. In this study, remote sensing of Evapotranspiration has been used to detect and map crop water requirements in order to enhance the Cotton App predictions for irrigation schedule during the growing season. Two remote sensing ET models based on thermal infrared (TIR), The surface energy balance algorithm for land (SEBAL) and Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), were used to derive ET over cotton. Results showed higher values of actual Evapotranspiration calculated by both SEBAL and METRIC models during the first 45 days of the growing season compared to the calculated values of ETa from crop coefficient. This is expected to be due to the higher evaporation fraction from bare soil since the plant cover is still very low and accordingly the plant transpiration too. However, later in the second growing stage, the models showed that the crop coefficient calculated ETa (ETa- Calculated) has overestimated the plant Evapotranspiration giving higher values compared to the values from the models. These results indicate that, the use of remote sensing techniques along with the ET-models will increase the app efficiency in giving more precise irrigation scheduling.</p>

scholarly journals Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management

2018 ◽  
Vol 61 (2) ◽  
pp. 533-548 ◽  
Author(s):  
J. Burdette Barker ◽  
Christopher M. U. Neale ◽  
Derek M. Heeren ◽  
Andrew E. Suyker
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Water Balance ◽  
Real Time ◽  
Eddy Covariance ◽  
Irrigation Management ◽  
Crop Coefficient ◽  
Irrigation Scheduling ◽  
Balance Model ◽  
Crop Coefficients

Abstract. Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrf alone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management. Keywords: Center-pivot irrigation, ET estimation methods, Evapotranspiration, Irrigation scheduling, Irrigation water balance, Model validation, Variable-rate irrigation.

scholarly journals Evaluation of Evapotranspiration from Eddy Covariance Using Large Weighing Lysimeters

Agronomy ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 99 ◽  
Author(s):  
Jerry Moorhead ◽  
Gary Marek ◽  
Prasanna Gowda ◽  
Xiaomao Lin ◽  
Paul Colaizzi ◽  
...  
Keyword(s):  
Energy Balance ◽  
Eddy Covariance ◽  
Water Use ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Error Rates ◽  
Energy Balance Closure ◽  
Crop Water ◽  
Crop Water Use ◽  
Hourly Data

Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. Although lysimetery is considered the most accurate method for crop water use measurements, high-precision weighing lysimeters are expensive to build and operate. Alternatively, other measurement systems such as eddy covariance (EC) are being used to estimate crop water use. However, due to numerous explicit and implicit assumptions in the EC method, an energy balance closure problem is widely acknowledged. In this study, three EC systems were installed in a field containing a large weighing lysimeter at heights of 2.5, 4.5, and 8.5 m. Sensible heat flux (H) and ET from each EC system were evaluated against the lysimeter. Energy balance closure ranged from 64% to 67% for the three sensor heights. Results showed that all three EC systems underestimated H and consequently overestimated ET; however, the underestimation of H was greater in magnitude than the overestimation of ET. Analysis showed accuracy of ET was greater than energy balance closure with error rates of 20%–30% for half-hourly values. Further analysis of error rates throughout the growing season showed that energy balance closure and ET accuracy were greatest early in the season and larger error was found after plants reached their maximum height. Therefore, large errors associated with increased biomass may indicate unaccounted-for energy stored in the plant canopy as one source of error. Summing the half-hourly data to a daily time-step drastically reduced error in ET to 10%–15%, indicating that EC has potential for use in agricultural water management.

scholarly journals Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model

2017 ◽  
Vol 21 (3) ◽  
pp. 1339-1358 ◽  
Author(s):  
Jordi Cristóbal ◽  
Anupma Prakash ◽  
Martha C. Anderson ◽  
William P. Kustas ◽  
Eugénie S. Euskirchen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Arctic Tundra ◽  
Soil Heat Flux ◽  
Energy Balance Model ◽  
The Arctic ◽  
Balance Model ◽  
Climate Conditions

Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is key to forecasting ecological responses to changing climate conditions in the Arctic. An extensive local evaluation of the Two-Source Energy Balance model (TSEB) – a remote-sensing-based model using thermal infrared retrievals of land surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for Arctic tundra. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean turbulent flux errors generally less than 50 W m−2 at half-hourly time steps, similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. The MODIS leaf area index (LAI) remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data on the local biomass amount. Model refinements found in this work at the local scale build toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

Monitoring evapotranspiration and water stress of Mediterranean oak savannas using optical and thermal remote sensing-based approaches

2020 ◽  
Author(s):  
Elisabet Carpintero ◽  
Ana Andreu ◽  
Pedro J. Gómez-Giráldez ◽  
María P. González-Dugo
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Energy Balance ◽  
Water Balance ◽  
Soil Water ◽  
Continuous Monitoring ◽  
Spectral Response ◽  
Balance Model ◽  
Landsat 8 ◽  
Management Actions

<p>In water-controlled systems, the evapotranspiration (ET) is a key indicator of the ecosystem health and the water status of the vegetation. Continuous monitoring of this variable over Mediterranean savannas (landscape consisting of widely-spaced oak trees combined with pasture, crops and shrubs) provides the baseline required to evaluate actual threats (e.g. vulnerable areas, land-use changes, invasive species, over-grazing, bush encroachment, etc.) and design management actions leading to reduce the economic and environmental vulnerability. However, the patched nature of these agropastoral ecosystems, with different uses (agricultural, farming, hunting), and their complex canopy structure, with various layers of vegetation and bare soil, pose additional difficulties. The combination of satellite mission with high/medium spatial/temporal resolutions provides appropriate information to characterize the variability of the Mediterranean savanna, assessing resource availability at local scales.</p><p>The aim of this work is to quantify ET and water stress at field-scale over a dehesa ecosystem located in Southern Spain, coupling remote sensing-based water and energy balance models. A soil water balance has been applied for five consecutive hydrological years (between 2012 and 2017) using the vegetation index (VI) based approach (VI-ETo model), on a daily scale and 30 m of spatial resolution. It combines FAO56 guidelines with the spectral response in the visible and near-infrared regions to compute more accurately the canopy transpiration. Landsat-8 and Sentinel-2 images, meteorological, and soil data have been used. This approach has been adapted to dehesa ecosystem, taking into account the double strata of annual grasses and tree canopies. However, the lack of available information about the spatial distribution of soil properties and the presence of multiple vegetation layers with very different root depths increase the uncertainty of water balance calculations. The combination with energy balance-based models may overcome these issues. In this case, the two-source energy balance model (TSEB) has been applied to explore the possibilities of integrating both approaches.  ET was estimated using TSEB in the days with available thermal data, more accurately assessing the reduction on ET due to soil water deficit, and allowing the adjustment of water stress coefficient in the VI-ETo model.</p><p>The modeled ET results have been validated with field observations (Santa Clotilde; 38º12’N, 4º17’ W; 736 m a.s.l.), measuring the energy balance components with an eddy covariance system and complementary instruments. The VI-ETo model has proven to be robust to monitor the vegetation water use of this complex ecosystem. However, the integration of the energy balance modelling has improved the estimations during the dry periods, with highly stressed vegetation, enabling a continuous monitoring of ET and water stress over this landscape.</p>

scholarly journals Satellite-based evapotranspiration and crop coefficient for irrigated sorghum in the Gezira scheme, Sudan

2006 ◽  
Vol 3 (3) ◽  
pp. 793-817
Author(s):  
M. A. Bashir ◽  
T. Hata ◽  
A. W. Abdelhadi ◽  
H. Tanakamaru ◽  
A. Tada
Keyword(s):  
Energy Balance ◽  
Irrigation Water ◽  
Crop Coefficient ◽  
Remotely Sensed ◽  
Data Availability ◽  
Balance Model ◽  
Time Data ◽  
Remotely Sensed Data ◽  
Time Step ◽  
Key Factor

Abstract. The availability of the actual water use from agricultural crops is considered as the key factor for irrigation water management, water resources planning, and water allocation. Traditionally, evapotranspiration (ET) has been estimated in the Gezira scheme by multiplying the reference evapotranspiration (ETo) by crop coefficient (kc) which is derived from the phenomenological crop stages. Recently, advanced developed energy balance models assist to estimate ET through remotely sensed data. In this study Enhanced Thematic Mapper Plus (ETM+) images were used to estimate spatial distribution of daily, monthly and seasonal ET for irrigated sorghum in the Gezira scheme, Sudan. The daily ET maps were also used to estimate kc over time and space. Results of remotely sensed based energy balance were compared with actual measurements conducted during 2004/05 season. The daily actual ET values estimated using the energy balance model during the satellite acquisition dates (28 July, 29 August, 16 October and 17 November) were 4.7, 5.5, 7.1 and 2.7 mm/day, while the average seasonal evapotranspiration for irrigated sorghum estimated to be around 596 mm. The remotely estimated kc values in the initial, crop development, mid-season and late-season stages were 0.62, 0.85, 1.15, and 0.48 respectively. On the other hand the widely used tradition kc values during the pervious mention stages are 0.55, 0.94, 1.21 and 0.65, respectively. This research shows that remotely sensed measurements can help objectively analyzed the irrigation water requirement for different field crops on daily and seasonal time step. Moreover, the remotely sensed real-time data availability provides the system managers with information that not previously available.

scholarly journals REMOTE SENSING OF GLOBAL MONTHLY EVAPOTRANSPIRATION WITH AN ENERGY BALANCE (EB) MODEL

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1729-1733
Author(s):  
X. Chen ◽  
Z. Su ◽  
Y. Ma
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Land Surface ◽  
Roughness Length ◽  
Meteorological Data ◽  
Global Scale ◽  
Balance Model ◽  
Time Step ◽  
Available Energy ◽  
Hydrological Cycles

<p><strong>Abstract.</strong> A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000&amp;ndash;2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3&amp;thinsp;mm/month, RMSE value of 36.9&amp;thinsp;mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.</p>

scholarly journals Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model

2016 ◽  
Author(s):  
Jordi Cristóbal ◽  
Anupma Prakash ◽  
Martha C. Anderson ◽  
William P. Kustas ◽  
Eugénie S. Euskirchen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Arctic Tundra ◽  
Soil Heat Flux ◽  
Energy Balance Model ◽  
The Arctic ◽  
Balance Model ◽  
Climate Conditions

Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as one of the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is a key to forecasting ecological responses to changing climate conditions in the Arctic regions. An extensive evaluation of the two-source energy balance model (TSEB) – a remote sensing-based model using thermal infrared retrievals of land–surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for the unique Arctic tundra conditions. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean flux errors around 50 W m−2 at half-hourly timesteps similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. MODIS LAI remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data. This work builds toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

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