Simulating Maize Production, Water and Surface Energy Balance, Canopy Temperature, and Water Stress under Full and Deficit Irrigation

2016 ◽  
Vol 59 (2) ◽  
pp. 623-633 ◽  
Keyword(s):  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Deficit Irrigation ◽  
Surface Energy Balance ◽  
Canopy Temperature ◽  
Maize Production ◽  
Production Water

scholarly journals Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate

2014 ◽  
Vol 18 (3) ◽  
pp. 1165-1188 ◽  
Author(s):  
J. Chirouze ◽  
G. Boulet ◽  
L. Jarlan ◽  
R. Fieuzal ◽  
J. C. Rodriguez ◽  
...  
Keyword(s):  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
In Situ Data ◽  
Stress Levels ◽  
Single Pixel ◽  
Semi Arid ◽  
Svat Model

Abstract. Instantaneous evapotranspiration rates and surface water stress levels can be deduced from remotely sensed surface temperature data through the surface energy budget. Two families of methods can be defined: the contextual methods, where stress levels are scaled on a given image between hot/dry and cool/wet pixels for a particular vegetation cover, and single-pixel methods, which evaluate latent heat as the residual of the surface energy balance for one pixel independently from the others. Four models, two contextual (S-SEBI and a modified triangle method, named VIT) and two single-pixel (TSEB, SEBS) are applied over one growing season (December–May) for a 4 km × 4 km irrigated agricultural area in the semi-arid northern Mexico. Their performance, both at local and spatial standpoints, are compared relatively to energy balance data acquired at seven locations within the area, as well as an uncalibrated soil–vegetation–atmosphere transfer (SVAT) model forced with local in situ data including observed irrigation and rainfall amounts. Stress levels are not always well retrieved by most models, but S-SEBI as well as TSEB, although slightly biased, show good performance. The drop in model performance is observed for all models when vegetation is senescent, mostly due to a poor partitioning both between turbulent fluxes and between the soil/plant components of the latent heat flux and the available energy. As expected, contextual methods perform well when contrasted soil moisture and vegetation conditions are encountered in the same image (therefore, especially in spring and early summer) while they tend to exaggerate the spread in water status in more homogeneous conditions (especially in winter). Surface energy balance models run with available remotely sensed products prove to be nearly as accurate as the uncalibrated SVAT model forced with in situ data.

scholarly journals Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates

2018 ◽  
Vol 10 (11) ◽  
pp. 1806 ◽  
Author(s):  
Emilie Delogu ◽  
Gilles Boulet ◽  
Albert Olioso ◽  
Sébastien Garrigues ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Heat Flux ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Surface Energy Balance ◽  
Source Model ◽  
Vegetation Stress ◽  
Stress Levels ◽  
Dual Source

Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based on the Two Sources Energy Balance (TSEB) model rationale which solves the surface energy balance equations for the soil and the canopy. SPARSE can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. The main innovative feature of SPARSE is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. The main objective of the paper is to assess the SPARSE model predictions of water stress and evapotranspiration components for its two proposed versions (the “patch” and “layer” resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. Over a large range of leaf area index values and for contrasting vegetation stress levels, SPARSE showed good retrieval performances of evapotranspiration and sensible heat fluxes. For cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. The bounded layer version of SPARSE provided the best estimates of latent heat flux over different sites and climates. Broad tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. The synchronous dynamics of observed and retrieved estimates underlined that the SPARSE retrieved water stress estimates from Thermal Infra-Red data were relevant tools for stress detection.

scholarly journals Mass and Surface Energy Balance Approaches for Monitoring Water Stress in Vineyards

2013 ◽  
Vol 19 ◽  
pp. 231-238 ◽  
Author(s):  
M. Palladino ◽  
A. Staiano ◽  
G. D’Urso ◽  
M. Minacapilli ◽  
G. Rallo
Keyword(s):  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance

scholarly journals Long-term water stress and drought assessment of Mediterranean oak savanna vegetation using thermal remote sensing

2021 ◽  
Vol 25 (2) ◽  
pp. 755-768
Author(s):  
María P. González-Dugo ◽  
Xuelong Chen ◽  
Ana Andreu ◽  
Elisabet Carpintero ◽  
Pedro J. Gómez-Giraldez ◽  
...  
Keyword(s):  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Agricultural Drought ◽  
Vegetation Coverage ◽  
Balance Model ◽  
Separate Analysis ◽  
Energy Fluxes ◽  
Remotely Sensed Data

Abstract. Drought is a devastating natural hazard that is difficult to define, detect and quantify. The increased availability of both meteorological and remotely sensed data provides an opportunity to develop new methods to identify drought conditions and characterize how drought changes over space and time. In this paper, we applied the surface energy balance model, SEBS (Surface Energy Balance System), for the period 2001–2018, to estimate evapotranspiration and other energy fluxes over the dehesa area of the Iberian Peninsula, with a monthly temporal resolution and 0.05∘ pixel size. A satisfactory agreement was found between the fluxes modeled and the measurements obtained for 3 years by two flux towers located over representative sites (RMSD = 21 W m−2 and R2=0.76, on average, for all energy fluxes and both sites). The estimations of the convective fluxes (LE and H) showed higher deviations, with RMSD = 26 W m−2 on average, than Rn and G, with RMSD = 15 W m−2. At both sites, annual evapotranspiration (ET) was very close to total precipitation, with the exception of a few wet years in which intense precipitation events that produced high runoff were observed. The analysis of the anomalies of the ratio of ET to reference ET (ETo) was used as an indicator of agricultural drought on monthly and annual scales. The hydrological years 2004/2005 and 2011/2012 stood out for their negative values. The first one was the most severe of the series, with the highest impact observed on vegetation coverage and grain production. On a monthly scale, this event was also the longest and most intense, with peak negative values in January–February and April–May 2005, explaining its great impact on cereal production (up to 45 % reduction). During the drier events, the changes in the grasslands' and oak trees' ground cover allowed for a separate analysis of the strategies adopted by the two strata to cope with water stress. These results indicate that the drought events characterized for the period did not cause any permanent damage to the vegetation of dehesa systems. The approach tested has proven useful for providing insight into the characteristics of drought events over this ecosystem and will be helpful to identify areas of interest for future studies at finer resolutions.

scholarly journals Inter-comparison of four remote sensing based surface energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate

2013 ◽  
Vol 10 (1) ◽  
pp. 895-963 ◽  
Author(s):  
J. Chirouze ◽  
G. Boulet ◽  
L. Jarlan ◽  
R. Fieuzal ◽  
J. C. Rodriguez ◽  
...  
Keyword(s):  
Heat Flux ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Latent Heat ◽  
Surface Energy Balance ◽  
Stress Levels ◽  
Single Pixel ◽  
Semi Arid

Abstract. Remotely sensed surface temperature can provide a good proxy for water stress level and is therefore particularly useful to estimate spatially distributed evapotranspiration. Instantaneous stress levels or instantaneous latent heat flux are deduced from the surface energy balance equation constrained by this equilibrium temperature. Pixel average surface temperature depends on two main factors: stress and vegetation fraction cover. Methods estimating stress vary according to the way they treat each factor. Two families of methods can be defined: the contextual methods, where stress levels are scaled on a given image between hot/dry and cool/wet pixels for a particular vegetation cover, and single-pixel methods which evaluate latent heat as the residual of the surface energy balance for one pixel independently from the others. Four models, two contextual (S-SEBI and a triangle method, inspired by Moran et al., 1994) and two single-pixel (TSEB, SEBS) are applied at seasonal scale over a four by four km irrigated agricultural area in semi-arid northern Mexico. Their performances, both at local and spatial standpoints, are compared relatively to energy balance data acquired at seven locations within the area, as well as a more complex soil-vegetation-atmosphere transfer model forced with true irrigation and rainfall data. Stress levels are not always well retrieved by most models, but S-SEBI as well as TSEB, although slightly biased, show good performances. Drop in model performances is observed when vegetation is senescent, mostly due to a poor partitioning both between turbulent fluxes and between the soil/plant components of the latent heat flux and the available energy. As expected, contextual methods perform well when extreme hydric and vegetation conditions are encountered in the same image (therefore, esp. in spring and early summer) while they tend to exaggerate the spread in water status in more homogeneous conditions (esp. in winter).

scholarly journals Long-term water stress and drought monitoring of Mediterranean oak savanna vegetation using thermal remote sensing

2020 ◽  
Author(s):  
María P. González-Dugo ◽  
Xuelong Chen ◽  
Ana Andreu ◽  
Elisabet Carpintero ◽  
Pedro J. Gómez-Giraldez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Agricultural Drought ◽  
Vegetation Coverage ◽  
Separate Analysis ◽  
Energy Fluxes ◽  
The Impact

Abstract. Drought is a devastating natural hazard, difficult to define, detect and quantify. Global meteorological data and remote sensing products present new opportunities to characterize drought in an objective way, and to extend this analysis in space and time. In this paper, we applied the surface energy balance model SEBS (Surface Energy Balance System) for the period 2001–2018, to estimate evapotranspiration and other energy fluxes over the dehesa area of the Iberian Peninsula, with a monthly temporal resolution and 0.05° pixel size. A satisfactory agreement was found between the fluxes modelled and the measurements obtained for three years by two flux towers located over representative sites (RMSD = 21 W m−2 and R2 of 0.76, for all energy fluxes and both sites). The estimations of the convective fluxes (LE and H) showed higher deviations, with RMSD = 26 W m−2 on average, than Rn and G, with RMSD = 15 W m−2. At both sites, annual ET was very close to total precipitation with the exception of a few wet years in which intense precipitation events, producing high run-off, were observed. The analysis of the anomalies of the ratio of evapotranspiration (ET) to reference ET (ETo) was used as an indicator of agricultural drought on monthly and annual scales. Hydrological years 2004/2005 and 2011/2012 stood out for their negative values, with the first one being the severest of the series, the impact observed on vegetation coverage and grain production. On a monthly scale, this event was also the longest and most intense, with peak negative values in January–February and April–May of 2005, explaining its great impact on cereal production (up to 45 % reduction). During the drier events, the changes in vegetation ground cover over the months, with a preponderant presence of grasslands compared with those in which only oak trees were active, allowed a separate analysis of the strategies adopted by the two strata to cope with water stress. These results indicate that the drought events characterized for the period did not cause any permanent damage on the vegetation of dehesa systems. The approach tested has proved useful to provide insight into the characteristics of drought events over this ecosystem and will be helpful to identify areas of interest for future studies at finer resolutions.

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>

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