Eddy Covariance fluxes versus satellite-based modelisation in a deficit irrigated orchard
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Eddy Covariance fluxes versus satellite-based
modelisation in a deficit irrigated orchard
Daniela Vanella1*, Simona Consoli1
Abstract: This study describes a combined approach to determine actual evapotranspiration (ETa) fluxes in a deficit
irrigated orchard in Sicily, Italy. Different ET models mostly based on satellite data (i.e., surface energy balance
models: one source ET1S - two source ET2S, and vegetation indexes derived crop coefficient model, ETVI) were compared
with both direct ET measurements obtained in situ by the Eddy Covariance (EC) technique (ETEC) and crop ET (ETc)
calculated by FAO-56 approach. Results of the comparisons show a general overestimation of the latent heat flux (or
ET) by the satellite models and estimated ETc, respect to the direct ETEC data. This discrepancy can be the consequence
of the deficit irrigation regimes (i.e. ranging from a minimum reduction of about 15% to a maximum of 45% of ETc)
applied at the experimental orange orchard, which may have caused a high source of sensible heat flux, driven by the
temperature gradient between plant canopy-atmosphere continuum.
Keywords: Evapotranspiration, eddy covariance, remote sensing, deficit irrigation.
Riassunto: Lo studio descrive un approccio combinato per determinare i flussi effettivi di evapotraspirazione (ETa) in
un frutteto irrigato in condizioni di deficit idrico in Sicilia, Italia. Diversi modelli di stima di ET basati principalmente
su dati satellitari (modelli di bilancio energetico superficiale: mono-dimensionale ET1S e bi-dimensionale ET2S e
un modello basato sulla determinazione del coefficiente colturale mediante indici di vegetazione, ETVI) sono stati
confrontati con le misurazioni dirette di ET ottenute in situ mediante la tecnica Eddy Covariance (EC). I risultati dei
confronti mostrano una sovrastima generale del flusso di calore latente (o ET) da parte dei modelli satellitari rispetto
alle misure dirette da Eddy Covariance. Questa discrepanza può essere la conseguenza dei regimi di irrigazione
deficitaria (che impongono riduzione idriche dal 15% al 45% di ETc) applicati alle colture in studio, che può aver
causato incrementi di calore sensibile e un elevato gradiente di temperatura nel continuum vegetazione-atmosfera.
Parole chiave: Evapotranspirazione, eddy covariance, telerilevamento, irrigazione deficitaria.
1. INTRODUCTION citrus orchards), together with the adoption of micro-
Water deficit is a major limiting factor of crop irrigation methods, can allow a significant saving of
productivity especially in semi-area climates (Singh et water resources, while optimizing production, avoiding
al., 2017). At global scale, irrigation sector is responsible waste (Consoli et al., 2017).
of 70% of water demand to sustain 40% of the food Applied researches have confirmed by an accurate
production (FAO, 2003). Nowadays, the competition monitoring of the continuum soil-plant-atmosphere
for water among the different users, and the greater (SPAC) system, that the WUE deficit irrigation (DI)
water resources depletion due to climate changes strategies are followed is greatly increased when (Capra
conditions require the application of water optimization et al., 2008; Abrisqueta et al., 2015; Consoli et al., 2017;
measures (Wada et al., 2013), among which the Ruiz-Sánchez et al., 2018). In particular, among the
Italian Journal of Agrometeorology - 2/2018
water saving measures in irrigation agriculture may different fluxes of mass and energy exchanges within
Rivista Italiana di Agrometeorologia - 2/2018
be included. These strategies offer opportunities for the SPAC system, evapotranspiration (ET) is the main
increasing the water use efficiency (WUE) in the one, which accounts for the water demand of the plant
agriculture sector, reserving water amounts for other system (Swank and Douglass, 1974).
priority uses, including those related to the ecosystem ET measures and/or estimates can be obtained at
service needs, thus contributing to the sustainability of different spatial and temporal scales with many levels
the primary sector (Capra et al., 2013; Power, 2010). of accuracy. As examples, direct ET measurements
In this view, the application of planned water deficit can be determined by using Eddy Covariance (EC)
conditions (i.e. RDI –Regulated Deficit Irrigation, technique, Bowen Ratio (BR), weighing lysimeters,
PRD – Partial Root-Zone Drying) to specific crops or scintillometer systems. Indirectly, ET estimates
of the Mediterranean environment (i.e. including may be retrieved from changes in soil water, via water
balance or through the surface energy budget, using
respectively the conservation of mass and energy
* Corresponding author’s e-mail: d.vanella@unict.it laws. In most practical situations, where the available
1 Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A),
Università degli Studi di Catania, Catania, Italy. instrumentation or resources are not sufficient to direct
Submitted 15 April 2018, accepted 23 May 2018 measure ET or when a wide spatial scale estimation of
DOI:10.19199/2018.2.2038-5625.041 41
AGRO 2-18_IIIA BOZZA.indb 41 18/07/18 16.22ET area required (i.e. regional), the adoption of remote (RS), i.e. surface energy balance (SEB) models such
modelling is generally pursued. In particular, at wider as one source (ET1S) (e.g. Barbagallo et al., 2009), two
scale of application than that of the single plant, in last sources (ET2S) (e.g. Li et al., 2005); and VI based models
three decades numerous methods based on remote as the dual Kc method (ETVI) (e.g. Gonzalez-Dugo, et
sensing (RS) data acquisition (e.g., empirical, residual, al., 2009). Daily estimates of ET by RS approaches and
deterministic models and inference methods) have measured ETEC were compared with crop ET (ETc)
been developed for deriving spatially distributed ET obtained by the FAO-56 model.
estimates. Tab. 1 reports some of these methods with
their strengths and limitations (Courault et al., 2005). 2.1. Field site description
All these methods need several inputs that sometimes The study was carried out in an orange orchard (Citrus
are not easily obtained at the appropriate space/ sinensis (L.) Osbeck) of around 1 ha, located in Eastern
temporal scale, and therefore increasing the difficulty Sicily, Italy (latitude 37° 20ʹ N, longitude 14° 53ʹ E).
for their applicability. Among the Vegetation Index (VI) Mature orange trees (Tarocco Sciara C1882 grafted on
based methods, the dual crop coefficient (Kc) approach Citrange Carrizo), with spacing of 6 x 4 m, were irrigated
proposed by FAO (Allen et al., 1998) is extensively with different strategies: (i) full irrigation (control, T1)
applied to derive ET in irrigated agriculture conditions to replace 100% of ETc using a surface drip irrigation
(Gonzalez-Dugo et al., 2009; Consoli and Vanella, system; (ii) sustained deficit irrigation treatment (SDI,
2014a, b). This approach is often preferred due to its T2) irrigated to replace 75% of ETc using a sub-surface
simplicity and robustness for operational applications. drip irrigation system; (iii) a regulated deficit irrigation
It requires fewer input data, and provides acceptable treatment (RDI, T3) irrigated to replace 50% of ETc
ET estimates when compared to heavily parameterized in those plant vegetative phases less sensitive to water
physically based models (Er-Raki et al., 2010). stress conditions (Pérez-Pérez et al., (2008); (iv) partial
The motivation of this study was to evaluate how robust root-zone drying treatment (PRD, T4) alternatively the
a satellite-based estimation of ET and estimates of ETc irrigated side of the root-zone at 50% of ETc, while the
could be, for the specific conditions of the experimental other side was kept dry (Consoli et al., 2014; Consoli et
site, when compared with ET data from Eddy al., 2017). The study was performed during the irrigation
Covariance (EC) during deficit irrigation conditions. seasons (June-September) of the years 2016 and 2017.
The climate of the experimental site is typical semi-arid
2. MATERIALS AND METHODS Mediterranean, with hot and dry summers. Maximum
This section focuses on the description of the materials air temperature during the irrigation seasons reached
and methodologies applied to determine ET at the about 30°C, with mean values of VPD and RH of 0.62
experimental field site by using EC technique (ETEC) kPa and 69.8%, respectively; and mean value of rain
and by combining different ET satellite-based models of 68 mm.
Methods Strengths Limitations
RS data are introduced
directly in semi-empirical
Empirical Applicable from local Need of ground ancillary data;
models to estimate ET
direct to regional scales spatial variation of parameters
Italian Journal of Agrometeorology - 2/2018
(e.g., relationship using RS
and meteorological data)
Rivista Italiana di Agrometeorologia - 2/2018
Integration of empirical
relationships and physical
Applicable if combined Require detection of wet
Residual models, using remotely
with ground measurement and dry pixels
sensed data (e.g. surface
energy balance models, SEB)
Vegetation Use of surface reflectance
Require calibration
Index (VI) data to compute reduction Applicable if combined
for each crop type, Kc varies
or inference factors (crop coefficient, Kc) with ground measurement
according to water stress
method for ET estimation
Based on more complex models Permit the estimation
such as Soil–Vegetation– of intermediate variable
Need of ground ancillary data;
Deterministic Atmosphere Transfer (SVAT), such as leaf area index (LAI);
require accurate RS data
which computes the different possible links with climate
components of energy budget and/or hydrological models
Tab. 1 - Remote sensing-based methods for ET estimation: strengths and limitations.
Tab. 1 - Metodi basati su dati satellitari per la stima di ET: vantaggi e limiti.
42
AGRO 2-18_IIIA BOZZA.indb 42 18/07/18 16.22ECH2O probe (Decagon, Inc.) located at different
depths (0.15-0.40 m) of the soil profile were used
to monitor soil moisture variations at the different
treatments.
2.2. ET estimates by the FAO-56 approach
An automatic meteorological station at the
experimental field was used to monitor hourly the
weather conditions (i.e. solar radiation, Rs, W m-2, air
temperature, Ta, °C, relative humidity, RH, %, wind
speed, u, ms-1 and direction and rainfall, P, mm) during Fig. 1 - Footprint of the EC surface energy fluxes at the
the experimental period. Climatic data were used to experimental site.
calculate the reference evapotranspiration (ET0, mm Fig. 1 - Impronta della torre Eddy Covariance press il sito
d-1) rates, using the Penman-Monteith equation (Allen in studio.
et al., 1998; Allen et al., 2006).
ETc was obtained by multiplying daily ET0, by the surface flux layer and the surface energy balan-
seasonal Kc for orange orchard. The seasonal Kc was ce closure were also evaluated (Kaimal and
assumed of 0.7 (Consoli et al., 2006, Consoli and Finningan, 1994).
Papa, 2013). Because tree size was smaller than fully Eddy Covariance sensible heat flux (H, W m-2) was
developed orange trees, a reduction coefficient (Kr) computed as:
was applied (Fereres et al., 1981). Kr was assumed
equal to 0.66. H cp wT (1)
Irrigation rates were determined by ETc and adju-
sted according to rainfall. Irrigation was supplied where ρ (g m-3) is the air density, cp (1004 J g-1 K-1) is the
to the orchard early in the morning, 3 times per air specific heat capacity at constant pressure and σwT
week. (m ET
s-1 K) is thewqcovariance between the vertical wind
speed and air temperature.
H c p wT
2.3. Eddy Covariance ET fluxes The vertical flux of water vapour content, i.e.
In May 2016, EC system (Fig. 1) was installed at the the latent heat flux (lET, W m-2), was expressed as:
H ET
experimental site on a micrometeorological tower at 7 m CR
Rn G
above the ground (about two times the canopy height). HET cp wq
wT
(2)
The EC system was equipped by a three dimensional
sonic anemometer (CSAT3-3D, Campbell Scientific where λ (J g-1) is the latent heat of vaporization and σwq
Inc.) and an infrared open-path gas analyzer (Li-7500, (g m−2 s−1)) is the covariance between the vertical wind
Li-cor Biosciences Inc.) to obtain high frequency ET and
speed H waterETwq vapour density.
CR
measurements of the three wind components and the R n Genergy balance closure ratio (CR) is
The surface
H2O and CO2 concentrations, respectively. The sample expressed as:
frequency for the raw data was 10 Hz (high frequency LE = R n − G − H
H ET
Italian Journal of Agrometeorology - 2/2018
data). Low frequency data (30-min) were obtained for: CR (3)
Rn G
Rivista Italiana di Agrometeorologia - 2/2018
net radiation (Rn , W m-2,net radiometer CNR-1 Kipp
& Zonen, located 7 m above the ground) and soil heat H allows
and c p forwTthe determination of how well the
flux (G, W m-2) obtained by self-calibrated soil heat flux
turbulent fluxes of heat and water vapour account for
plates (HFP01SC, Hukseflux) placed in the exposed, LEavailable
= R n − Genergy.
− H The ratio, as suggested by Prueger
the
half-exposed and shadowed soil, at a depth of about
0.05 m. ET (2005), was
et al., wq
performed only when Rn is greater
than 100 W m-2.
High and low frequency data were recorded and stored
Thirty-minute fluxes data were aggregated to a daily
in a CR1000 logger (Campbell Scientific Inc.). LE =and
scale R n latent
−G − H heat fluxes, acquired in W m-2, were
The standard EUROFLUX rules (Aubinet et al.,
H ET to equivalent depth of ET (mm d-1).
then transformed
2000) were adopted for EC measurements and CR
Using the R nETG EC measures of actual evapotran-spiration
data processing. Common errors in the measu- by the eddy covariance (ETa, EC), the Kc was estimated
red high frequency data, such as running means as in the follows:
for detrending, three angles coordinate rotations
and despiking, were removed during the post (4)
processing by quality checks. The stationary of the
43
LE = R n − G − H
AGRO 2-18_IIIA BOZZA.indb 43 18/07/18 16.222.4. Remote sensing approaches DOY Acquisition date
The RS approaches adopted in this study were 180 28/06/2016
implemented by using Landsat 8 Operational Land 203 21/07/2016
Imager (OLI) and Thermal Infrared Sensor (TIRS) 219 06/08/2016
images (https://landsat.usgs.gov/landsat-8) (Tab. 2). 235 22/08/2016
Tab. 3 reports the day-of-year (DOY) and the 251 07/09/2016
acquisition dates of the 10 selected images (based 150 30/05/2017
on clear sky condition) within the monitoring period
166 15/06/2017
(irrigation seasons 2016-17).
182 01/07/2017
Row data were re-calibrated in radiance for visible
189 17/07/2017
(VIS), near-infrared (NIR) and thermal infrared
(TIR) bands, on the basis of spectral specifications 214 02/08/2017
and the gain settings (values given in metadata of each Tab. 3 - Dates of the images selected during the irrigation
image). Reflectance in VIS-NIR bands was calculated seasons 2016-17.
Tab. 3 - Date di acquisizione delle immagini satellitari utiliz-
by the declination angle correction (USGS, 2016). zate nel corso della stagione irrigua 2016-17.
Vegetation indexes (VIs, i.e. Normalized Difference
H
Vegetation c p Index
wT
– NDVI; Soil Adjusted Vegetation (1)
These methods estimate surface resistances (surface
Index – SAVI; Leaf Area Index - LAI), Fraction cover and aerodynamic resistances to heat or water vapor,
(Fc) and albedo (a) were assessed starting from VIS- rah and rs) adopting various schemes (Zhang et al,
NIR reflectance bands (Consoli and Vanella 2014b). 2016).
ET
Radiometric surface
wq temperature (Ts) was calculated (2)
In the study, two SEB models were applied to estimate
from band 10 by using the model developed by Sobrino ET: the one-source (ET1S) and the two-sources (ET2S)
et al., in (2004). models. A full overview of the used algorithms is
H ET described in Consoli and Vanella, (2014b). The main
CR Surface energy balance models
2.4.1 difference between both SEB models (3) consists in the
R n GET
for estimating
radiometric surface temperature (Ts) schematization.
Surface energy balance (SEB) models combine the
In particular, the one-source (or one-dimensional)
use of satellite information (surface reflectance in VIS/
NIR and Ts) with ancillary data (weather data) to derive model refers Ts to a single layer which include soil
surface energy fluxes and instantaneous ET, the latter and vegetation together, while in (4) the two-source
as a residual term of the energy balance equation: model different Ts components were referred to soil
and vegetation separately. Figg. 2 and 3 report in flow
LE = R n − G − H (5) charts the main inputs (satellite and meteorological)
required for the application of the(5)
one (1S) and two-
where: LE is latent heat flux (W m2), Rn is net radiation sources (2S) models.
(W m2), G is soil heat flux (W m2), H is sensible heat Both the approaches (i.e. 1S and 2S) provided
flux (W m2). instantaneous values of the LE (or ET)
(6) corresponding
Italian Journal of Agrometeorology - 2/2018
Bands Wavelength (μm) Resolution (m)
Rivista Italiana di Agrometeorologia - 2/2018
Band 1 - Coastal aerosol 0.43 - 0.45 30
Band 2 – Blue 0.45 - 0.51 (7)30
Band 3 – Green 0.53 - 0.59 30
Band 4 – Red 0.64 - 0.67 30
Band 5 - Near Infrared (NIR) 0.85 - 0.88 30
Band 6 - SWIR 1 1.57 - 1.65 30
Band 7 - SWIR 2 2.11 - 2.29 (8)30
Band 8 – Panchromatic 0.50 - 0.68 15
Band 9 – Cirrus 1.36 - 1.38 30
Band 10 - Thermal Infrared (TIR) 1 10.60 - 11.19 100 * (30)
Band 11 - Thermal Infrared (TIR) 2 11.50 - 12.51 100 * (30)
*TIR bands are acquired at 100 m resolution, but are resampled to 30 m in delivered data product.
Tab. 2 - Technical specifications of Landsat 8 (OLI and TIRS).
Tab. 2 - Specifiche tecniche delle immagini satellitari Landsat 8 (OLI and TIRS).
44
AGRO 2-18_IIIA BOZZA.indb 44 18/07/18 16.22Fig. 2 - Lay-out
of the main input/output
H cp wT (1)
parameters
in the 1S model.
Fig. 2 - Schema
dei principali parametri
H cp wT di input/output utilizzati
ET wq (2)
nel modello mono-
dimensionale.
ET wq
H ET
CR (3)
Rn G
H ET
CR
Rn G
(4)
toLE Rn − G
the= time H satellite overpass (about 9:30 a.m.
of −the meteorological and soil hydraulic characteristics
(5) (Fig. 4,
local standard time). Daily ET values were computed, Tab. 4). The FAO-56 dual crop coefficient approach, in
as follows: the form popularized by the FAO 56 manual (Allen et
LE = R n − G − H
al., 1998), describes the relationship between daily ET
(6) of a given crop (ETVI) and ET0 by separating
(6) the single
crop coefficient (Kc) into the basal crop coefficient
where ETinst and ET0 are the instantaneous crop (Kcb), soil water evaporation (Ke) coefficient and water
and reference ET values, respectively, and ET0 24 is stress coefficient (Ks). Crop transpiration, represented
daily ET0. Note that the ratio ETinst/ET0 in Eq. (6) is by Kcb is separated from soil surface(7) evaporation as
equivalent to the Kc for the considered day. follow:
2.4.2 Vegetation index RS approach (7)
ETVI was obtained by combining a VIS/NIR-RS
procedure with ancillary ground based data on where ETVI and ET0 are in mm d-1.
(8)
Fig. 3 - Lay-out
of the main input/output
parameters in the 2S model.
Fig. 3 - Schema
dei principali parametri
di input/output utilizzati
Italian Journal of Agrometeorology - 2/2018
nel modello
Rivista Italiana di Agrometeorologia - 2/2018
bi-dimensionale.
45
AGRO 2-18_IIIA BOZZA.indb 45 18/07/18 16.22H ET
CR (3)
Rn G
Fig. 4 - Lay-out
(4) input/output
of the main
parameters
in the VI-satellite
based model.
LE = R n − G − H Fig. 4 - Schema
(5) parametri
dei principali
di input/output utilizzati
nel modello VI.
(6)
(7)
The reduction of actual evaporation (Kr) when the 5). ET0 was well above 700 mm; ETc, obtained by a
amount of water in the surface soil layer decreases was corrected Kc of 0.46, reached about 300 mm during
accounted as: both studied irrigation seasons.
Average values of soil moisture content at the different
(8) irrigation treatments are reported in(8)Tab. 6.
In all the investigated soil profiles, soil water content
where REW (mm) is the readily evaporable water remained between the FC and WP (Fig. 5).
(10 mm, by Allen et al, 1998) , TEW is the maximum
cumulative depth of evaporation from the soil surface 3.2. Micrometeorological results
layer (0.1 m) and De,i (mm) is the cumulative depletion Fig. 6 shows the surface energy balance closure from
at the end of the 1-th day. The soil parameters used in EC measurements at the field site during the 2016 (a)
the calculation of Kr, as field capacity (FC, 0.28 cm3 and 2017 (b) irrigation seasons (June – September).
cm-3) and permanent wilting point (WP, 0.14 cm3 cm-3), The slope of the regression forced through the origin
were determined by laboratory analyses for the soil- was around 0.82 in both the study periods, with
type under study (Consoli et al., 2017). determination coefficients (r2) of about 0.88 and 0.90
in 2016 and 2017, respectively. The results confirmed
3. Results the good quality of the flux sources within the area of
3.1. Climatic data and soil moisture dynamics at footprint of the micrometeorological EC tower (Twine
the experimental site during deficit irrigation et al., 2000).
The climate of the site during the two irrigation The net radiation, Rn, was as expected the dominant
seasons in 2016 and 2017 was quite typical of semi-arid surface energy balance component, driving the energy
Mediterranean conditions. No significant variations budget. Observing the energy fluxes reported in Fig.
were detected during the two periods of trial (Tab. 7, it can be noted that the most part of the available
Italian Journal of Agrometeorology - 2/2018
wind
Rivista Italiana di Agrometeorologia - 2/2018
Year DOY Ta Tdew ET0 RHmin Rain Kcb Kc max Kr Ks
speed
°C ms-1 °C mm d-1 % mm
180 33.8 2.8 13.6 8.4 29.6 0.0 0.72 1.29 0.08 0.94
203 34.7 1.5 13.2 7.8 27.4 0.0 0.72 1.25 0.07 1.00
2016 219 34.4 1.9 17.7 7.5 37.3 0.0 0.72 1.22 0.13 1.00
235 34.5 2.1 16.7 7.4 34.7 0.0 0.72 1.24 0.29 1.00
251 26.9 1.3 15.4 3.9 49.4 2.4 0.72 1.15 1.00 1.00
150 21.8 1.5 10.4 6.8 48.0 0.0 0.55 1.16 0.20 1.00
166 25.6 1.4 13.2 7.7 46.1 0.0 0.55 1.16 0.04 1.00
2017 182 26.4 2.3 20.0 6.9 68.0 0.0 0.55 1.11 0.04 1.00
189 24.9 1.8 13.5 6.7 49.1 0.0 0.55 1.17 0.27 0.97
214 32.0 1.8 16.4 7.9 39.1 0.0 0.55 1.21 0.64 1.00
Tab. 4 - Main inputs for the dual-Kc FAO-56 approach.
Tab. 4 - Principali input del modello dual-Kc FAO-56.
46
AGRO 2-18_IIIA BOZZA.indb 46 18/07/18 16.22Parameter 2016 2017 is plausible to figuring out a significant reduction of
ET0 (mm) 723.3 782.9 latent heat flux, LE, (mean value of 60.2 ± 74.3 W m-2
ETc=ET0*Kc (mm) 334.2 361.7 in 2016 and 59.5 ± 71.2 W m-2 in 2017) in benefit of H
ETEC (mm) 237.8 235.0 increments (mean value of 86.4 ± 126.7 W m-2in 2016
Rainfall (mm) 99.6 36.8 and 98.0 ± 138.8 W m-2 in 2017).
Tair (°C) 24.9(±2.5) 24.9(±2.5)
Therefore, during the two irrigation seasons daily
measured ETEC values ranged from 0.8 to 3.6 mm
RH (%) 60.1(±10.1) 60.0(±10.1)
day-1, with a mean of 2.1(± 0.6) mm day-1. Total ETEC
Tab. - 5 ET rates and meteorological data (mean ± standard during the monitoring periods were 237.8 and 235.0
deviation) for the study site in 2016 and 2017.
Tab. - 5 Flussi di ET e grandezze meteorologiche (media ± devi-
mm in 2016 and 2017, respectively (Tab. 4).
azione standard) per il sito in studio nel periodo in 2016-2017. As expected, estimated daily ETc values by the FAO-
56 approach (Table 4) were higher than ETEC of about
36% and 46% in 2016 and 2017, respectively. Fig. 8 (a
Irrigation Soil water content (m3 m-3)
treatment and b) reports the comparison between the Kc value
2016 2017
(about 0.5) adopted during the study to correct the
T1:100% ETc 0.26 0.26
reference ET and the crop coefficient derived from EC
T2:75% ETc 0.26 0.26
(Kc, EC) during the study. The average values of Kc, EC
T3:RDI 0.20 0.20 (ETEC/ET0) were of 0.34(± 0.1) and 0.31(± 0.1) in 2016
T4: PRD 0.21 0.17 and 2017, respectively. As expected these values of Kc,
Tab. 6 - Mean values of soil water content (m3 m-3) at the EC have been significantly different from the single Kc,
different irrigation treatments during the years 2016 and accounting for deficit irrigation conditions.
2017 obtained by ECH20.
Tab. 6 - Valori medi del contenuto idrico del suolo (m3 m-3)
misurati da sonde ECH20 per i diversi trattamenti irrigui 3.3. Remote sensing results
nel periodo 2016-17 Fig. 9 (a- d) compares the instantaneous surface energy
fluxes (W m-2) directly measured by EC and estimated
energy (Rn-G) is transformed into sensible heat (H). from SEB models (i.e. 1S and 2S). The RS-based
This behaviour is typical of advective conditions, in models tend to overestimate the available energy (Rn-
which a high heat rate is exchanges by the plant system G) and LE flux, and to underestimate H flux.
to the atmosphere, corresponding to crop temperature The comparison between daily surface energy
increase. In our experimental conditions, where the fluxes estimated by FAO-56, dual Kc (VI) and SEB
footprint of the EC tower is mainly irrigated under approaches (1S and 2S) and measured by EC system is
deficit conditions (i.e. treatments T2, T3 and T4), it reported in Fig. 10.
Fig. 5 - Evolution of soil
water content (m3 m-3)
in the irrigation treatments
and rainfall in 2016 (a)
and 2017 (b) at the study
site. FC = field capacity
Italian Journal of Agrometeorology - 2/2018
(0.28 m3 m-3), WP = wilting
point (0.14 m3 m-3).
Rivista Italiana di Agrometeorologia - 2/2018
Fig. 5 - Precipitazione
e evoluzione del contenuto
idrico del suolo (m3 m-3)
nei diversi trattamenti
irrigui nel 2016 (a)
e nel 2017 (b). FC= capacità
di campo (0.28 m3 m-3),
WP = punto critico colturale
(0.14 m3 m-3).
47
AGRO 2-18_IIIA BOZZA.indb 47 18/07/18 16.22Fig. 6 - Surface energy balance closure of the EC measurements at the field site. Half-hourly data refer to irrigation seasons
(June – September) in 2016 (a) and 2017 (b).
Fig. 6 - Chiusura del bilancio energetico da misure EC presso il sito in studio. Dati alla mezz’ora relativi alle stagioni irrigue
(Giugno-Settembre) del 2016 (a) e del 2017 (b).
Fig. 7 - Example of hourly surface energy fluxes (W m-2) measured at the field site by the EC technique. Data refer to June 2017.
Fig. 7 - Esempio di flussi energetici orari (W m-2) da EC presso il sito in studio - Giugno 2017.
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Rivista Italiana di Agrometeorologia - 2/2018
Fig. 8 - Estimated Kc for the orange orchard using the FAO-56 approach, actual Kc, EC and rainfall (mm) during 2016 (a)
and 2017 (b) periods.
Fig. 8 - Stime di Kc da modello FAO-56, Kc effettivo da EC e precipitazione (mm) nel 2016 (a) and nel 2017 (b).
At the daily scale, ET fluxes obtained by RS measured ETEC and estimated fluxes (ET1S, ET2S,
approaches (ET1S, ET2S, ETVI) resulted greater than ETVI).
the ETEC fluxes respectively of 38.2, 44.8 and 49.1%. Some examples of ET maps obtained by applying the
Fig. 11 a shows the scatter plot between the daily RS techniques (dual Kc, 1S and 2S models) are shown
48
AGRO 2-18_IIIA BOZZA.indb 48 18/07/18 16.22Fig. 9 - Instantaneous
estimated versus measured
surface energy fluxes
(W m-2) at the field site
in the study period.
Fig. 9 - Stime istantanee
e misure di flussi energetici
(W m-2) presso il sito
in studio.
in Figg. 12 and 13, referring to a couple of DOYs in conducted by the same authors (Barbagallo et al.,
2016 and 2017, respectively. 2009; Consoli and Vanella 2014a; 2014b) in well-
irrigated orange orchards revealed a quite good
4. DISCUSSION accordance between the satellite estimates and the
The most interesting result of this study is the “truth” in the field. In this case, the deficit irrigation
discrepancy between RS-based estimates of surface conditions applied at the field during the two
energy fluxes and the EC ET data. Previous studies consecutive irrigation seasons played a strategic role in
Italian Journal of Agrometeorology - 2/2018
Rivista Italiana di Agrometeorologia - 2/2018
Fig. 10 - Daily ET fluxes during the monitoring period 2016-17. EC refers to ET measured by EC; ETc estimated by FAO
– 56 approach, VI is the dual-Kc satellite approach; 1S and 2S refer to ET derived by SEB models.
Fig. 10 - Flussi giornalieri di ET nel periodo 2016-17. EC indica la misura diretta di ET da Eddy Covariance; FAO indica
le stime di ETc da modello FAO 56, VI è il modello dual-Kc da satellite; 1S and 2S sono le stime di ET da bilancio energetico
satellitare (SEB).
49
AGRO 2-18_IIIA BOZZA.indb 49 18/07/18 16.225. CONCLUSION
The main objective of the study was to investigate the
potential of different satellite-based models (FAO-56
dual-Kc approach and SEB models) to provide accurate
ET estimates in a deficit irrigated orange orchard under
semi-arid Mediterranean climate conditions. Satellite-
based estimates of ET were compared with direct ET
measures from EC.
Results were generally unsatisfactory due to the
overestimation of the ET fluxes provided by the
satellite-based approaches. The approaches were, in
fact, not able to capture the relative high ratio H/LE
due to the induced water stress conditions.
Fig. 11 - Comparison between ETEC and ET estimated by
RS approaches.
The results deserve a certain interest for the need to
Fig. 11 - Confronto tra ETEC and le stime di ET da satellite. deepen the nature of the advective phenomenon, that
may have a role in the physiological and hormonal (i.e.
altering the normal ration between the energy balance abscisic acid and proline production) response of the
surface fluxes, determining a fairly high sensible heat crop to sustain water stress.
and a ET reduction due to the imposed water stress
conditions. This behaviour was poorly captured by ACKNOWLEDGMENTS
the remote estimates (except for few cases) and was The authors wish to thank the personnel of Centro
exacerbated by the intrinsic nature of the semi-arid di ricerca Olivicoltura, Frutticoltura e Agrumicoltura
climate characterizing the field site. In fact, errors of the Italian Council for Agricultural Research and
in satellite ET estimates are generally large in these Agricultural Economics Analyses (CREA-OFA,
climate conditions due to the presence of mixed Acireale) for their hospitality at the field site.
pixels, i.e. characterizing by fully irrigated fields The authors thank the EU and the Italian Ministry
usually surrounded by dry landscape that accounting of Education, Universities and Research for funding,
for differences in cover ground and moisture content as part of the collaborative international consortium
(Liu and Luo, 2010). IRIDA (‘‘Innovative remote and ground sensors,
Analysing the results of the satellite modelling data and tools into a decision support system for
per se, it can be noted that in general the dual-Kc agriculture water management”), financed under the
approach overestimates ET after irrigation events ERA-NET Cofund WaterWorks 2014. This ERA-
and the estimates provided by the 2S model were in NET is an integral part of the 2015 Joint Activities
good accordance with the results of the simplified developed by the Water Challenges for a Changing
1S approach. Therefore, the use of 2S model can be World Joint Programme Initiative (Water JPI).
questioning at the light of the required computation The authors also acknowledge support from the
effort and inputs parameters. ERANET-MED project WASA (‘‘Water Saving in
Italian Journal of Agrometeorology - 2/2018
Fig. 12 - Examples
Rivista Italiana di Agrometeorologia - 2/2018
of ET maps obtained
by applying the RS
approaches. Data refer
to DOY 180 and 219, 2016.
Fig. 12 - Esempio di mappe
di ET ottenute applicando
la modellazione da satellite.
Giorni dell’anno 180
and 219, 2016.
50
AGRO 2-18_IIIA BOZZA.indb 50 18/07/18 16.22Fig. 13 - Examples of ET
maps obtained by applying
the RS approaches.
Data refer to DOY 150
and 180, 2017.
Fig. 13 - Esempio di mappe
di ET ottenute applicando
la modellazione da satellite.
Giorni dell’anno 150
and 180, 2017.
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