scholarly journals The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

2015 ◽  
Vol 19 (11) ◽  
pp. 4653-4672 ◽  
Author(s):  
G. Boulet ◽  
B. Mougenot ◽  
J.-P. Lhomme ◽  
P. Fanise ◽  
Z. Lili-Chabaane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Series Resistance ◽  
Water Status ◽  
Soil Evaporation ◽  
Stress Levels ◽  
Dual Source ◽  
Rainfed Wheat

Abstract. Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m−2 from values of the order of 50–80 W m−2), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution.

scholarly journals The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

2015 ◽  
Vol 12 (7) ◽  
pp. 7127-7178 ◽  
Author(s):  
G. Boulet ◽  
B. Mougenot ◽  
J.-P. Lhomme ◽  
P. Fanise ◽  
Z. Lili-Chabaane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Series Resistance ◽  
Water Status ◽  
Soil Evaporation ◽  
Stress Levels ◽  
Dual Source ◽  
Rainfed Wheat

Abstract. Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow deriving a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a arallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Norman et al., 1995) model rationale as well as state of the art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in-situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two datasets that the series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m-2 from values of the order of 50–80 W m-2), and that soil evaporation retrieval is globally consistent with an independent estimate from observed soil moisture evolution.

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 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 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>

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).

Intercomparison of Spatially Distributed Models for Predicting Surface Energy Flux Patterns during SMACEX

2005 ◽  
Vol 6 (6) ◽  
pp. 941-953 ◽  
Author(s):  
Wade T. Crow ◽  
Fuqin Li ◽  
William P. Kustas
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Energy Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Energy Fluxes ◽  
Spatially Distributed

Abstract The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.

On the use of different approaches based on photochemical reflectance index and surface temperature to monitor the water status of winter wheat in semi-arid regions

2020 ◽  
Author(s):  
Zoubair Rafi ◽  
Valérie Le Dantec ◽  
Olivier Merlin ◽  
Said Khabba ◽  
Patrick Mordelet ◽  
...  
Keyword(s):  
Water Stress ◽  
Winter Wheat ◽  
Surface Temperature ◽  
Water Status ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Temperature Index ◽  
Water Stress Index ◽  
Semi Arid

&lt;p&gt;Agriculture is considered to be the human activity that consumes the most mobilized water on a global scale. However, crops planted in semi-arid areas regularly face periods of moderate to extreme water stress. Such water stress periods have a considerable impact on the seasonal yield of these crops. In order to participate in a more rational irrigation water management, monitoring of the rapid changes in plant water status is necessary. For this purpose, the combination of two different wavelength ranges will be explored : an index based on Xanthophyll cycle (Photochemical Reflectance Index, PRI) and a commonly-used index from thermal infrared spectral range (LST). An experiment on winter wheat was carried out over two agricultural campaigns (2016 to 2018) in the Haouz basin, which is located in the Marrakech region, to better assimilate the temporal dynamics of PRI and surface temperature. In this study, four different approaches are proposed to study the functioning of wheat : 1- an approach based on solar angle to remove the structure effect (PRI&lt;sub&gt;0&lt;/sub&gt;) from the PRI signal and to derive a water stress index PRI&lt;sub&gt;j&lt;/sub&gt;, 2- an approach based on global radiation (R&lt;sub&gt;g&lt;/sub&gt;) to extrapolate a theoretical PRI (PRI&lt;sub&gt;th&lt;/sub&gt;) for R&lt;sub&gt;g&lt;/sub&gt; equal to zero and to calculate a water stress index PRI&lt;sub&gt;lin&lt;/sub&gt;, 3- an approach that determines an optimal PRI (PRI&lt;sub&gt;pot&lt;/sub&gt;) on the basis of the available water content (AWC) criterion in order to derive a stress index I-PRI and 4- an energy balance approach to extract dry and wet surface temperatures in order to establish a normalized surface temperature index (T&lt;sub&gt;norm&lt;/sub&gt;). The results of this work show a strong correlation between the PRI&lt;sub&gt;0&lt;/sub&gt; and the Leaf Area Index with a coefficient of determination equal to 0.92, indicating that it is possible to isolate the structural effects of wheat on the PRI signal. In addition, over the range of variation in AWC, a significant correlation with PRI&lt;sub&gt;j&lt;/sub&gt;, PRI&lt;sub&gt;jlin&lt;/sub&gt; and I-PRI was observed with coefficients of determination of 0.71, 0.42 and 0.24, respectively. In contrast to the T&lt;sub&gt;norm&lt;/sub&gt;, which varies only for values of AWC below 30%, a coefficient of determination of 0.22 is obtained. Finally, the PRI allows us to acquire early and complete information on the response of wheat to change in AWC as opposed to the surface temperature index, revealing the potential of the PRI to monitor the water status of plants and their responses to changing environmental conditions.&lt;/p&gt;

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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. &amp;#160;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.&lt;/p&gt;&lt;p&gt;The modeled ET results have been validated with field observations (Santa Clotilde; 38&amp;#186;12&amp;#8217;N, 4&amp;#186;17&amp;#8217; 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.&lt;/p&gt;

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