Revisiting SAMIR parameters setting for evapotranspiration and irrigation spatialization

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Direct measurements of ET

The ET measurement methods are based on concepts which can be critical under semi-arid and arid environments for several reasons: (i) representativeness (ii) instrumentation (iii) microclimate and (iv) applicability. Therefore, to establish the degree of accuracy of the obtained ET measurement and the validity of a method, it is necessary to consider all these parameters (Allen et al., 2011b).

Hydrological approach: Weighing lysimeters

Weighing lysimeters have been developed to give a direct measurement of ET. In general, it is a device, a tank or container, to define the water movement across a boundary (depth level of the soil). Lysimeters of many different designs, sizes, shapes, and measurement systems have been built over the years (Howell et al., 1991). The main advantage of the lysimeter in situ measurements is that water consumption of vegetation can be performed under approximately realistic field conditions. However, a lysimeter measurement requires elaborate preparation. Moreover it is typically limited to only few individual trees or a small surface area of agricultural crops (Verstraeten et al., 2008). A Major limitation of lysimeters is that capillary rise is not taken into consideration because the water table can be supposed to be at a considerable depth (Makkink, 1959); moreover, root extension is sometimes limited.

Plant physiology approaches

Methods based on plant physiology either measure the water loss from a whole plant or a group of plants. They may include methods such as tracer technique and porometry but here, only two of the most common methods will be analysed: the sap flow method and the chamber system.

Sap flow method

Sap flow measures only plant transpiration by means of simple accurate models; sap flow can be measured by two basic methods: (i) heat pulse and (ii) heat balance. The most popular sap flow method is the heat balance method, based on the concepts proposed by Čermák et al. (1973) and Steinberg et al. (1990). The plant transpiration can be estimated by determining the sap mass flow; this is done using gauges that are attached to or inserted in the plant stem. For the heat balance method, a heater element is placed around the plant stem to provide energy to the system. Thermocouples are used to determine how much heat is lost by conduction up, down and radially in the stem from the heater element. The difference between the heat input and these losses is assumed to be dissipated by convection with the sap flow up the stem and may be directly related to water flow (Kjelgaard et al., 1997). The mass flow rate F [g.t-1] is expressed by the relationship: (1. 3).
where Qh is input heat, Qv is vertical conductive heat, Qr is radial heat loss to environment, cw [J.g-1.K-1] is specific heat of water and δT is the temperature difference between the upstream and downstream thermocouples.
Direct measurements of actual transpiration can also be performed with the heat pulse-sap flow technique, which has been applied in vineyards (Yunusa et al., 2004) and olive groves (Testi et al., 2006; Williams et al., 2004). Sap flow method is a very good alternative to lysimeter experiments; however, operation of sap-flow sensors requires a vast technical input and maintenance effort.

Remote sensing based method for ET estimation

Direct measurement of ET is only possible at local scale (for a single plot mostly); it is much more difficult at larger scales (irrigated perimeter or watershed) due to the complexity of hydrological processes (Minacapilli et al., 2007). Moreover, at these scales, land cover is usually heterogeneous and this affects the land-atmosphere exchanges of heat, water and other constituents (Giorgi and Avissar, 1997). ET estimates for various temporal and spatial scales, from hourly to monthly to seasonal time steps, and from field to global scales, are required for hydrologic applications in water resource management (Anderson et al., 2011). Techniques using remote sensing (RS) information are therefore essential when dealing with processes that cannot be represented by point measurements only. In fact, RS capabilities for monitoring vegetation and its physical properties on large areas have been identified for years now (Tucker, 1978). Jackson et al. (1977) were ones of the major pioneers in determining ET by remote sensing, with the use of infrared thermometry for the estimation of wheat water consumption.
As explained in the introduction of this thesis, RS provides periodic data about some major ET drivers, amongst others, land surface temperature and vegetation properties (e.g. NDVI and Leaf Area Index LAI) from plot to regional scales (Li et al., 2009; Mauser and Schädlich, 1998). Many methods using remotely-sensed data to estimate ET are reviewed in Courault et al. (2005;) and Liou and Kar (2014). According to Courault et al. (2005), these methods are difficult to classify because their complexity depends on the balance between the empirical and physically based used modules.

Surface energy budget methods

The quantity of water released by a surface into the atmosphere can be directly related to the energy that was necessary for its transformation into vapor by the following equation: LE=ρ (1. 15). Where LE [W.m-2] is the latent heat flux expressed in, L [] is the latent heat of vaporization of water, representing the energy required for the vaporization of one kilogram of water. ET represents the evaporation rate of the surface in cubic meter of water per square meter of surface per unit of time and ρ [kg.m-3] is the density of the water .
The vegetation cover intercepts only part of the radiative energy emitted by the sun and the atmosphere, the complementary part being reflected towards the atmosphere. A small fraction of the energy absorbed by vegetation (usually neglected in energy balance expression) is used for photosynthesis, which is crucial for crop development. Most of the intercepted radiative energy is redistributed by the vegetation cover in its near environment under different propagation modes: emission of radiative energy (thermal infrared), conduction of heat into the ground or convection in the atmosphere. All these components allow introducing the concept of energy balance based on the principle of energy conservation in the environment. Conventionally, the radiative fluxes received by the surface are positive, whereas those emitted are negative. More generally, the heat fluxes are positive if they represent a loss of energy for the surface and negative for a contribution. Assuming an uniform and vegetation cover of large extension, and therefore no advection of heat from neighbouring areas, the energy exchange can be considered as purely vertical, and the equation of the energy balance at the surface is written as follows: (1. 16).
where LE is the latent heat flux, H is the sensible heat flux, Rn is the net radiation and G is the conduction flux in the soil. The difference (Rn-G) is called the available energy, an amount that is converted into H+λE, the turbulent fluxes. This latter denomination is used because they depend largely and in the same way on the intensity of convection in the atmospheric surface boundary layer. (Rn-G) is partitioned according to the surface water status between the two turbulent fluxes H and LE. When water is present in the soil in sufficient quantity, available energy is mostly converted into LE, while under water stress, this distribution is instead in favor of H.

Soil water balance method: crop coefficient approach

The most common and practical approach used for estimating ET is the FAO-56 method (Allen et al., 1998), previously adopted by Doorenbos and Pruitt (1977), on which is based the SAMIR model used in this dissertation. It is used to estimate crop water requirements based on the reference evapotranspiration (ETo) and crop coefficients.
According to the FAO drainage and irrigation paper N°56 (Allen et al., 1998), distinctions are made between reference crop evapotranspiration (ETo), crop evapotranspiration (ETc) and actual evapotranspiration (ETa). ETo is the evapotranspiration rate from a hypothetical grass reference crop with an assumed crop height of 0.12 m, a fixed surface resistance of 70 s.m-1 and an albedo of 0.23. ETo was often confused with potential ET (ETp) (Douglas et al., 2009; Torres et al., 2011; Zhang et al., 2010). ETc is the water lost by crops that are grown in large fields under optimum soil moisture, excellent management and environmental conditions, and achieve full production under the given climatic conditions. ETa involves all conditions of the vegetated surface. Due to sub-optimal crop management and environmental constraints that affect crop growth and limit evapotranspiration, ETa is generally smaller than ETc.
The FAO-56 method is applied in three steps: (i) determine the climatic reference ETo, ii) apply a single (Kc) or double crop coefficient (Ke and Kcb) to get ET of a given crop under standard conditions i.e. optimal agronomic conditions (ETc), and (iii) apply a water stress coefficient Ks to get ET under non-standard conditions i.e. stress conditions (ETa) also called actual ET (ETcadj).
The FAO-56 method is based on two underlying assumptions: (1) ETo represents the climate effect and (2) Kc (and Kcb) varies mainly with the specific characteristics of the crop, allowing the use of these coefficients regardless of the climates and environmental conditions (Allen et al., 1998).

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Complementarily of the FAO-56 method and remote sensing

The FAO-56 method has long been used to monitor plot scale water budget with tools like CROPWAT (Clarke, 1998) using most of the time crop coefficients taken from the FAO tables. RS provides spatial and updated information about vegetation. The satellite images not only allow distinguishing different types of land use, but also, provide further information about the actual development of vegetation. This information is well correlated with the photosynthetic activity of plants, which is itself determinant of ET.
To compute ET on larger areas, some tools have been developed based mainly on the use of thermal remote sensing for energy balance methods, like METRIC (Allen Richard et al., 2007). However, the increasing availability of high-resolution NDVI time series arose the coupling of the FAO-56 with remotely sensed crop coefficients (González-Dugo et al., 2013; Mateos et al., 2013; Neale et al., 2007; Simonneaux et al., 2008). This trend lead to the emergence of tools like the DEMETER concept (Calera-Belmonte et al., 2005) providing Kc to farmers, the HYDROMORE tool (Sánchez et al., 2010) computing the water budget of crops, or the TOPS-SIM “system-of-system” for irrigation management (Melton et al., 2012). Zhang and Wegehenkel (2006) developed a model based on the FAO-56, lumped in a more complex model including the whole watershed system, and using essentially low-resolution MODIS images. Minacapilli et al. (2008) proposes the SIMODIS model based on a variant of FAO-56 where Kc is determined analytically using vegetation and climatic parameters, and a more physical soil modeling.
Spatio-temporal monitoring of vegetation dynamics by remote sensing is possible through the use of reflectance combinations through vegetation indices (VI). VIs are based essentially on the red band (R) corresponding with a high absorption of the radiation and the infrared band (NIR) offering a high reflectance. VIs determined over the last thirty years by different authors and their fields of application are summarized in annex 3.
The first researches relating vegetation development and canopy reflectance was carried out during the 1970s (Kanemasu, 1974; Tucker, 1979), It has been shown that the crop coefficients were linked to the VIs (Er-Raki et al., 2007; Glenn et al., 2011; Hunink et al., 2017; Hunsaker et al., 2005a; Hunsaker et al., 2005b; Simonneaux et al., 2008). The arguments in favor of the causal Kcb-VI relationship include the direct relationship between Kcb and the fraction of photosynthetic active radiation absorbed by the canopy (fPAR) and the relationship of these parameters with the VIs (Calera et al., 2017). Ke is linked to the bare soil fraction, complementary of the fractional vegetation cover (fc) which can also be related to visible RS data (Huete et al., 1985). Although the relations proposed between Kcb, fc and VIs are not theoretically fully linear, they can usually be approximated by linear relations (Choudhury et al., 1994; Gonzalez-Dugo and Mateos, 2008). One pitfall of RS methods based on crop coefficient is their requirement for crop specific relations providing crop coefficient from RS data (Gowda et al., 2008). Some relations may be found in the literature, either directly estimating crop coefficients from VIs, or assessing them from vegetation parameters like the leaf area index (LAI) or fc. This difficulty also applies to the relation between VIs and fractional vegetation cover (fc) required running the FAO-56. Furthermore, establishing a unique relationship between crop coefficient and spectral vegetation indices is an ongoing research topic (Er-Raki et al., 2010) and many empirical linear relationships available in the literature have been derived experimentally. The interest for coupling the FAO-56 method (Allen et al, 1998) with remotely-sensed crop coefficients is raising alongside the increasing availability of high- resolution NDVI time series (González- Dugo et al. , 2013; Mateos et al., 2013; Neale et al., 2007; Simonneaux et al., 2008). The low availability of such data, for financial as well as technical reasons, combined with the intermittent presence of cloud, has been a restraint to their use (J. Trout et al., 2008; Pinter et al., 2003; Takeuchi et al., 2003). However, the recently lunched Sentinel-2 mission offers a unique opportunity to improve this monitoring thanks to high-resolution (10 m) and high repetitivity (5 days) visible and near infrared (VIS-NIR) remote sensing. Main VIS-NIR missions are detailed in annex 2.

Table of contents :

1Chapter 1: Soil water balance components’ estimation methods
1.1 Soil water storage
1.2 Evapotranspiration
1.2.1 Direct measurements of ET Hydrological approach: Weighing lysimeters Plant physiology approaches Micrometeorological approaches
1.2.2 Remote sensing based method for ET estimation Surface energy budget methods Soil water balance method: crop coefficient approach Deterministic methods Inter comparisons of ET estimation methods
1.3 Irrigation
1.3.1 How much water is given?
1.3.2 When water is given?
1.3.3 How often water is given?
1.3.4 Plant response to water stress
1.3.5 Irrigation efficiencies
1.4 Synthesis
Chapter 2: Study area and data processing
2.1 Study area description
2.1.1 Geographic location
2.1.2 Climat data Rainfall Temperature Relative humidity Wind conditions
2.1.3 Water Resources Surface water resources Groundwater resources
2.2 Land use maps
2.3 Observed irrigation data
2.4 Remote sensing data
2.4.1 High-resolution satellite imagery
2.4.2 Low-resolution satellite imagery
2.5 In situ data
2.5.1 Meteorological data
2.5.2 Flux and soil moisture data
2.5.3 Extra large aperture scintillomter (XLAS) Scintillometer derived fluxes XLAS footprint computation XLAS derived latent heat flux
2.6 Synthesis
Chapter 3: Evapotranspiration and irrigation volumes estimation at high spatial resolution: application of the soil water balance model SAMIR
3.1 SAMIR model description
3.2 Irrigation volumes results validation at perimeter scale: Published results (article)
3.3 Unpublished results and additional analyzes
3.3.1 Irrigation volumes results validation at perimeter scale for the 2013- 2014 season
3.3.2 Irrigation volumes results validation at field and farm scales
3.3.3 Evapotranspiration results validation using the XLAS data
3.4 Synthesis and partial conclusion
Chapter 4: Revisiting SAMIR parameters setting for evapotranspiration and irrigation spatialization
4.1 SAMIR model calibration
4.1.1 Second calibration on cereals fields
4.1.2 Calibration for the olive orchard
4.2 Model parameters setting
4.3 Validation of new modeled irrigation volumes at perimeter scale
4.4 New evapotranspiration results validation using the XLAS data
4.5 Synthesis and partial conclusion
Chapter 5: Energy fluxes estimation at low spatial resolution: Application of the energy balance model SPARSE
5.1 SPARSE model description
5.1.1 Input data
5.1.2 Algorithm
5.2 Validation of instantaneous and daily SPARSE model estimates using the XLAS data: Published results (article)
5.3 Synthesis and partial conclusion
General conclusion and perspectives
Major findings
Limitations of the methods and models
Perspectives and future plans


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