2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA) | 978-1-6654-9408-3/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICA-ACCA56767.2022.10006120
Remote sensing tools for monitoring water
requirements and water stress in vineyards and
fruit trees
S. Ortega-Farías, F. Fuentes-Peñailillo, K. Gutter, R. Vega-Ibáñez
Abstract— Chile's main agricultural areas will significantly
reduce rainfall (between 20-40%) due to global climate change.
Also, Chile is periodically affected by the climatic phenomenon of
“La Niña” (ENSO) and the Pacific Decadal Oscillation (PDO),
which have produced important droughts and economic losses in
most of the agricultural areas. Under these conditions,
sophisticated irrigation water management, such as regulated
deficit irrigation (RDI), will be required to optimize water
productivity (kg m-3) and to maintain sufficient levels of yield and
quality of wine and fruits. In this regard, this paper aims to
describe remote sensing tools for monitoring water requirements
(or evapotranspiration, ET) and plant water stress in vineyards
and fruit orchards.
keywords——crop coefficients, evapotranspiration, irrigation,
soil evaporation, transpiration
I. INTRODUCTION
M
ANY factors are forcing the agricultural industry to
produce food efficiently and with the least impact on the
environment. For this, it is necessary to implement new
methodologies in the management of the different tasks that are
carried out in the field. This is how the concept of Remote
Sensing arises, which involves the integration of a series of
technological developments in support of productive tasks,
such as sensors, satellites, automation, and robotics, among
others. The aforementioned becomes of great importance in the
current scenario of a growing population and limited resources,
where food security stands out as an important issue to be
managed considering sustainable production in the context of
climate change [1].
In this sense, the Food and Agriculture Organization of the
United Nations (FAO) estimates that the world population will
reach 8.5 billion by 2030, 9.7 billion by 2050, and 10.4 billion
by 2100 [2]. This, added to the economic growth of many
nations, puts pressure on agriculture, which should increase its
world production by 70% by 2050. Moreover, in the case of
This study was supported by the Chilean government through the project
REDES-ANID (No. REDES190072), AGCI-Chile/México (Incremento de la
eficiencia y cuidado del agua mediante experiencia conjunta entre comunidades
de Sonora-México y Linares-Chile). and PCI-ANID (No. NSFC190013).
S. Ortega-Farías K. Gutter and R. Vega are with Facultad de Agronomía,
Centro de Investigación y transferencia en Riego y Agroclimatología (CITRA),
Universidad de Talca, Casilla 747, Talca 3460000, Chile (sortega@utalca.cl,
kgutter@utalca.cl and rjvega@utalca.cl).
F. Fuentes-Peñailillo is with Instituto de Investigación Interdisciplinaria
(I3), Universidad de Talca, Talca, Chile & Faculty of Agricultural Sciences,
Research and Extension Center for Irrigation and Agroclimatology (CITRA),
Universidad de Talca, Talca, Chile (ffuentesp@utalca.cl).
Chile, climate change will bring uncertainty regarding the
behavior of rainfall, with prolonged periods of drought and a
decrease in the flows of the main hydrographic basins of the
central area of the country [3], [4]. Model-based projections
indicate that a reduction in mean annual precipitation of up to
40% relative to current values are expected for the second half
of this century under high emission scenarios [5].
Also, Chile is periodically affected by the climatic
phenomenon of “La Niña” (ENSO), a cyclic oceanicatmospheric phenomenon that occurs every 3-7 years and
considers a warming/cooling of the equatorial Pacific Ocean.
The warming phase of the Pacific corresponds to the so-called
“El Niño,” while the cooling phase corresponds to “La Niña.”
ENSO is currently considered the largest source of seasonal and
inter-annual climate variability over various regions of the
planet. This is how it has been possible to associate the
existence of a higher mean flow in various rivers in South
America when there are Niño events [6]. It has also been
established that a large part of Chile's winter and interannual
precipitation variability is linked to the seasonal aggregation of
El Niño/Southern Oscillation [7].
Another factor that modulates precipitation in Chile is the
Pacific Decadal Oscillation (PDO), a phenomenon of natural
origin that considers warm and cold phases, such as ENSO, but
its effect can prevail for decades. Cold periods are characterized
by an intensification of the South Pacific Anticyclone, which
causes drought conditions in the central zone [8]. The South
Pacific Anticyclone is an area where the vertical airflow that
promotes cloud formation is inhibited, therefore, it acts as a
kind of blockage of the systems associated with rain. So, a
stronger anticyclone causes less rain in winter. Data from the
Chilean Meteorological Directorate show that as of 2007, the
anticyclone has become increasingly intense and coincides with
the Niña events, which largely explains the prolonged drought
experienced in recent years [9].
In arid and semi-arid regions where grapes and other fruits
are grown, precipitations are scarce or non-existent during the
growing season, so it is necessary to evaluate irrigation
strategies that improve the efficiency in the use of water
resources and their productivity [10]. One of these strategies is
regulated deficit irrigation (RDI), which consists of maintaining
the plant's water status within predefined deficit limits during
certain phases of its development in which it is less sensitive to
water restriction [11]. For this, it is necessary to have
parameters that reveal the water status of the plant and serve as
indicators of convenience and distribution of irrigation in order
to reduce the risks in the development of the crop [12], [13].
978-1-6654-9408-3/22/$31.00 ©2022 IEEE
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Because of this, the main objective of this work is to
highlight the main remote sensing technologies applied in
agricultural production, particularly in vineyards and fruit trees,
in the context of climate change.
II. STATE OF THE ART
Agriculture has faced many challenges in the past few
decades, leading agriculture to embrace digitalization.
However, this isn’t something new, it has been happening since
the 80s with the concept of precision agriculture, which
involves the use of the GPS in agriculture equipment for the
mapping of yields and other variables of interest with the
intention of spatialized management, and which has reached a
peak with the development and use of a series of proxy and
remote detection methodologies for field monitoring [14].
Therefore, growing demand for tools that allow precise
monitoring of large extensions of crops is observed, where realtime monitoring becomes essential to generate management
strategies according to the needs of the crops [15], [16], also
highlighting the need for modernization and intensification of
agricultural practices and the need for highly efficient use of
water and other resources [17], [18].
In modern agriculture, irrigation monitoring is commonly
carried out through traditional techniques that determine the
soil water content and the atmospheric demand. Among them,
we find water content monitoring in the soil through
tensiometers, watermark probes, neutron probes, or TDR (Time
Domain Reflectometry) equipment, and water balance
modeling. Other approaches to quantify the water status of
plants include obtaining the relative water content (RWC), fresh
weight (FW), turgid weight (TW), dry weight (DW), and
equivalent water thickness (EWT) [19]. However, currently, it
is also preferred to evaluate the water status of the plant through
different physiological indicators [20] because the plant
considers all the conditions of its environment, that is, it
integrates the demand of the atmosphere and the soil water
content [21]. Usually, the evaluation of water requirements is
carried out by estimating the evapotranspiration of the crop
(ETa), whose methodology is standardized and defined in the
FAO-56 Manual [22]. For this, the reference evapotranspiration
values must be known (ET0), which is estimated from
meteorological data obtained in a particular locality, a value
that is adjusted by the crop coefficient (KC), which considers
the growth cycle of the plant and its variation over time [23],
[24]. ET0 is usually obtained through daily meteorological
variables (wind speed, solar radiation, air temperature, and
relative humidity) recorded by an automatic weather station
(AWS) under reference conditions (2 meters over an extensive
area of well-watered green grass) equipped with a thermometer,
hygrometer, anemometer, and pyranometer (Allen, Pereira,
Raes, Smith y W, 1998; Campos et al., 2016).
Another key physiological indicator suitable for describing
the water status of plants is the gas exchange. Gas exchange
depends, in the first place, on the opening of the stomata, which
can be affected by climate and soil conditions [27]. The opening
of the stomata allows an exchange of carbon dioxide (CO2) and
water vapor between the plant and the atmosphere, which
allows the processes of photosynthesis and transpiration to be
carried out.
The water status of the plant can also be evaluated using
physiological indicators such as basal water potential, leaf
water potential (Ѱ
), stem water potential (Ѱ
), stomatal
conductance, sap flow, and trunk diameter. These parameters
have the advantage of integrating climatic and soil conditions
[20], [28].
In viticulture, one of the most used is the water potential, which
involves measuring the pressure of the sap inside the xylem
through a pressure chamber [13], [29]. This physiological
variable can be obtained through the leaf water potential , stem
water potential, and the pre-dawn water potential before dawn
(Ѱ )[30]. However, under irrigation, it is preferred to use the
stem water potential since it presents less variation between
individual vine canopies compared to the leaf potential. [31]. In
this regard, [32] mention that the Ѱ
is considered a more
stable and integrating measure of the water status of the plant
compared to the Ѱ
. Table I summarizes the traditional
TABLE I
MAIN ADVANTAGES AND DISADVANTAGES OF MEASURING
SOIL WATER CONTENT, WATER BALANCE, WATER STATUS OF
THE PLANT, AND STOMATAL CONDUCTANCE FOR THE
IRRIGATION PROGRAMMING OF GRAPEVINES.
Methodology
I. Soil water
measurement
Soil Water
Content (Ej.
TDR, FDR)
II. Calculation
of the water
balance
Advantages
Easy to apply in the
field; can be very
precise; water content
measurement
indicates how much
water to apply; many
commercial systems
available; some
sensors already
automated
In principle easy to
apply; indicates how
much water to apply
(Requires
estimation of
evaporation
and
precipitation)
Disadvantages
Soil heterogeneity requires many
(sometimes expensive) sensors or
extensive monitoring programs;
Difficulty selecting positions that
are representative of the root zone;
the sensors generally do not
measure the water status at the
surface level of the roots (which
depends on the evaporative
demand)
Not as accurate as direct
measurements: it needs accurate
information on local rainfall; the
calculation of evapotranspiration
requires a good estimation of the
crop coefficients (which depend
on the development of the crop,
depth of roots, etc.); errors are
cumulative, so regular calibration
is required.
In general, it does not indicate
“how much water” to apply;
requires calibration to determine
"control thresholds"; still largely
in the research / development
stage and little used yet routinely
in agronomy (except for thermal
sensing)
III. Detection
of plant
"stress"
Directly measures the
plant's response to
stress; integrates
environmental
effects; potentially
very sensitive
(a) Water
status of
tissues (Ex.
pressure
chamber)
Widely accepted
reference technique:
more useful if the
stem water potential
is estimated, using
covered leaves
Slow and laborious (therefore
expensive, especially for pre-dawn
measurements); unsuitable for
automation
(b)
Physiological
response (Ex.
stomatal
conductance)
Potentially more
sensitive than
measurements of the
water status of tissues
(especially leaves)
Usually requires sophisticated or
complex equipment; requires
calibration to determine "control
thresholds"
Generally, a very
sensitive response,
except in some
anisohydric species
Large leaf-to-leaf variation
requires many replications to
obtain reliable data
Adapted from [21].
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methodologies to know the water status of crops, their
advantages, and their disadvantages.
However, as seen in Table I, there are several disadvantages
regarding using physiological measurements to assess plant
water status, mainly referred to as technical and economic
difficulties when applied on a large scale. Because of this, the
need for a tool to overcome these disadvantages is increasing.
In this sense, remote sensing appears as a tool to overcome the
difficulties in crop production, given that it has the advantage
of monitoring crops in a non-destructive way [1], providing
near real-time information depending on the scale at which this
tool is applied, generating recurrent information of different
scales systematically, enabling the characterization of the
spatiotemporal variability within a given area, in ways that are
impossible with traditional techniques [1].
Remote sensing considers proxy and teledetection
techniques, which usually consist of the non-contact
measurement of the radiation reflected or emitted from
agricultural orchards [29], [33]–[37]. Matter interacts with this
radiation, so through the use of instrumentation (sensors)
capable of simultaneously capturing the spatial and spectral
attributes of a scene, an object can be characterized [38]. This
involves an instrument or a sensor mounted on a platform, such
as a satellite, an aircraft, an UAV/UGV, or a probe [1].
Among the properties and processes of plants that can be
studied using remote sensing techniques, at a single plant level,
including changes in physiology (for example, photochemical
and non-photochemical quenching or NPQ), leaf temperature
changes due to stomatal conductance regulation, biochemistry
(for example, plant water content, altered pigment
composition), and canopy structure (such as leaf area index
(LAI), leaf size or leaf orientation). At the community level, the
distance between individual plants or plant morphology (for
example, plant height) could be impacted [39].
There have been three main spatial scales of remote sensing
incorporated into agricultural management:
• Satellite scale: for this case, the information is obtained
through sensors mounted on satellites or shuttles, which
over the last decades have had substantial improvements
in terms of spatial, temporal, and spectral resolution [40],
Today, a large range of satellite sensors provide us
regularly with data covering a wide spectral range (from
optical through microwave). Data are acquired from
various orbits and in different spatial and temporal
resolutions [41]. Some of these platforms are LANDSAT,
MODIS, and Sentinel. However, a significant
disadvantage of these systems is that monitoring is highly
dependent on the time series provided (revisiting time)
and the spatial resolution of the sensors [1]. For example,
the Landsat satellite, orbiting at 705 km above the Earth’s
surface, can record images with a 30 m × 30 m pixel size.
This can be very useful for the study and modeling of large
areas. However, it does not allow the more precise
management required in precision agriculture, particularly
in water management [42].
• Airborne scale: This scale refers mainly to unmanned
aerial vehicles (UAV). The use of this type of tools is
steadily increasing because of the main advantages of
these types of systems, which are the temporal frequency
(depending on the needs of the user) and the ability to
incorporate high-resolution sensors, providing high
spectral and spatial resolutions (Ali & Imran, 2021; Baluja
et al., 2012), providing near-real-time monitoring in key
phenological stages of the plant’s [44]–[50]. However,
these technologies also present some constraints in their
implementation at the field level, such as the cost of
instrumentation [51], the difficulties in the flight plan
programming [44], limited battery duration [52], restricted
cargo capacity [53] and data manipulation, correction and
interpretation [1].
• Ground-based systems: considers the use of portable
hand-held
devices
such
as
thermometers,
spectroradiometers, and porometers, which can provide a
rich quality of information, given that they perform
measurements almost in direct contact with the object of
study [40]. This type of sensor can detect plant stresses
using the imaging or spectrometry of plant leaves in the
visible (red-green-blue or RGB), near-infrared (NIR),
infrared (IR), and ultraviolet (UV) wavebands [54]. This
is possible because it is known that many physiological
and chemical properties of plants influence the way their
tissues interact with light. Consequently, different biotic
and abiotic factors alter this response [38].
Depending on the instrument selected for the spectral
measurement, a trade must be made between spatial,
radiometric, and spectral resolution. In this regard, the terms
multispectral and hyperspectral arise. They usually define
instruments according to the number of wavebands of
information that are recorded for each image pixel. The more
general adjective ‘multispectral’ describes instruments that
record information in only a small number of wavebands,
typically 2–10. Hyperspectral instruments record information
in many wavebands, typically greater than 10 [55]. This causes
the spectral profile of a plant to be more poorly described when
using a multispectral instrument.
Among plant measurement-based approaches to determining
water status, canopy temperature has received much attention
over the past two decades, especially with the advent of portable
infrared radiometers and thermal imaging cameras [56]–[58].
Through these sensors, it is possible to precisely monitor the
temperature of the canopy surface, which is related to the water
potential, through the calculation of the Crop Water Stress
Index (CWSI) [59]. The CWSI is based on the difference
between canopy temperature and air temperature (Tc-Ta),
normalized by the vapor pressure deficit (VPD), and is based
on the fact that water stress induces stomatal closure, thereby
decreasing evaporative cooling and increasing leaf temperature
[60]. This index has been widely used over several crops such
as grapevines [61]–[64], olive trees [42], [65], maize [66], [67],
soybean [56], among others, highlighting the usefulness of this
index and other remote sensing tools.
III. CONCLUSIONS
The accurate management of irrigation water to improve the
overall productivity of crops has become an issue of great
importance in the current scenario of population growth and
climate change. In the case of Chile, agriculture faces constant
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variations in climate derived from different atmospheric
phenomena. In this sense, the current tools used to assess plant
water status, such as physiological measurements and methods
to estimate ET0 have severe disadvantages, limiting their
application over large surfaces. Because of the above, it has
been demonstrated that incorporating new technologies in the
form of remote sensing can overcome the difficulties presented
in physiological measurements to assess the plant water status
of several crops in a fast and representative way.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
M. Weiss, F. Jacob, and G. Duveiller, “Remote sensing for
agricultural applications: A meta-review,” Remote Sens. Environ.,
vol. 236, no. December 2018, p. 111402, 2020, doi:
10.1016/j.rse.2019.111402.
United Nations, World Population Prospects 2022. USA: United
Nations Department of Economic and Social Affairs, 2022.
H. Peña, “Recursos naturales e infraestructura. Desafíos de la
seguridad hídrica en América Latina y el Caribe,” Santiago, 2016.
CEPAL, La economía del cambio climático en Chile. Santiago:
Naciones Unidas, 2012.
R. D. Garreaud, J. P. Boisier, R. Rondanelli, A. Montecinos, H. H.
Sepúlveda, and D. Veloso-Aguila, “The Central Chile Mega Drought
(2010–2018): A climate dynamics perspective,” Int. J. Climatol., vol.
40, no. 1, pp. 421–439, 2020, doi: 10.1002/joc.6219.
G. J. Berri, E. Bianchi, and G. V Müller, “El Niño and La Niña
influence on mean river flows of southern South America in the 20th
century,” Hydrol. Sci. J., vol. 64, no. 8, pp. 900–909, Jun. 2019, doi:
10.1080/02626667.2019.1609681.
R. Valdés-Pineda, J. B. Valdés, H. F. Diaz, and R. Pizarro-Tapia,
“Analysis of spatio-temporal changes in annual and seasonal
precipitation variability in South America-Chile and related ocean–
atmosphere circulation patterns,” Int. J. Climatol., vol. 36, no. 8, pp.
2979–3001, Jun. 2016, doi: 10.1002/joc.4532.
Center for Climate and Resilience Research, “La megasequía 20102015: Una lección para el futuro,” Inf. a la Nación, vol. 1, pp. 1–214,
2017.
J. Vicencio, “Por qué nos importa tanto el Anticiclón del Pacífico
Sur,” Meteochile Blog, 2018. .
M. Zúñiga, S. Ortega-Farías, S. Fuentes, C. Riveros-Burgos, and C.
Poblete-Echeverría, “Effects of Three Irrigation Strategies on Gas
Exchange Relationships, Plant Water Status, Yield Components and
Water Productivity on Grafted Carménère Grapevines,” Front. Plant
Sci., vol. 9, no. July, pp. 1–13, 2018, doi: 10.3389/fpls.2018.00992.
M. M. Chaves et al., “Grapevine under deficit irrigation: hints from
physiological and molecular data.,” Ann. Bot., vol. 105, no. 5, pp.
661–676, 2010, doi: 10.1093/aob/mcq030.
J. Cifre, J. Bota, J. M. Escalona, H. Medrano, and J. Flexas,
“Physiological tools for irrigation scheduling in grapevine (Vitis
vinifera L.): An open gate to improve water-use efficiency?,” Agric.
Ecosyst. Environ., vol. 106, no. 2-3 SPEC. ISS., pp. 159–170, 2005,
doi: 10.1016/j.agee.2004.10.005.
L. E. Williams, P. Baeza, and P. Vaughn, “Midday measurements of
leaf water potential and stomatal conductance are highly correlated
with daily water use of Thompson Seedless grapevines,” Irrig. Sci.,
vol. 30, no. 3, pp. 201–212, May 2012, doi: 10.1007/s00271-0110276-2.
M. Shepherd, J. A. Turner, B. Small, and D. Wheeler, “Priorities for
science to overcome hurdles thwarting the full promise of the ‘digital
agriculture’ revolution,” J. Sci. Food Agric., vol. 100, no. 14, pp.
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
5083–5092, 2020, doi: 10.1002/jsfa.9346.
T. Wheeler and J. von Braun, “Climate Change Impactson Global
Food Security,” Science (80-. )., vol. 341, no. August, pp. 479–485,
2013.
X. X. Zhou et al., “Research on remote sensing classification of fruit
trees based on Sentinel-2 multi-temporal imageries,” Sci. Rep., vol.
12, no. 1, pp. 1–14, 2022, doi: 10.1038/s41598-022-15414-0.
K. F. Davis, J. A. Gephart, K. A. Emery, A. M. Leach, J. N. Galloway,
and P. D’Odorico, “Meeting future food demand with current
agricultural resources,” Glob. Environ. Chang., vol. 39, pp. 125–132,
2016, doi: 10.1016/j.gloenvcha.2016.05.004.
A. Molotoks, P. Smith, and T. P. Dawson, “Impacts of land use,
population, and climate change on global food security,” Food
Energy Secur., vol. 10, no. 1, pp. 1–20, 2021, doi: 10.1002/fes3.261.
S. Ma et al., “Application of the water-related spectral reflectance
indices: A review,” Ecol. Indic., vol. 98, pp. 68–79, Mar. 2019, doi:
10.1016/J.ECOLIND.2018.10.049.
R. Gálvez, R. Callejas, and G. Reginato, “Comparación de la cámara
de presión tipo Scholander modelo Pump-up respecto a la cámara de
presión tradicional en vides de mesa Comparison,” IDESIA, vol. 29,
no. 2, pp. 175–179, 2011.
H. G. Jones, “Irrigation scheduling: Advantages and pitfalls of plantbased methods,” J. Exp. Bot., vol. 55, no. 407, pp. 2427–2436, 2004,
doi: 10.1093/jxb/erh213.
FAO, Evapotranspiración del cultivo. Guías para la determinación
de los requerimientos de agua de los cultivos. Roma: Organización
de las Naciones Unidas para la Agricultura y la Alimentación, 2006.
Y. Gao et al., “Crop coefficient and water-use efficiency of winter
wheat/spring maize strip intercropping,” F. Crop. Res., vol. 111, no.
1–2, pp. 65–73, 2009, doi: 10.1016/j.fcr.2008.10.007.
J. Pokorny, “Evapotranspiration,” in Encyclopedia of Ecology, B. B.
T.-E. of E. (Second E. Fath, Ed. Oxford: Elsevier, 2019, pp. 292–303.
I. Campos, C. Balbontín, J. González-Piqueras, M. P. GonzálezDugo, C. M. U. Neale, and A. Calera, “Combining a water balance
model with evapotranspiration measurements to estimate total
available soil water in irrigated and rainfed vineyards,” Agric. Water
Manag.,
vol.
165,
pp.
141–152,
2016,
doi:
10.1016/j.agwat.2015.11.018.
R. G. Allen, L. S. Pereira, D. Raes, M. Smith, and W. Ab, “Crop
evapotranspiration - Guidelines for computing crop water
requirements - FAO Irrigation and drainage paper 56 By,” pp. 1–15,
1998.
G. Gil, Fruticultura: El potencial productivo. Ediciones UC, 2009.
L. E. Williams and F. J. Araujo, “Correlations among Predawn Leaf,
Midday Leaf, and Midday Stem Water Potential and their
Correlations with other Measures of Soil and Plant Water Status in
Vitis vinifera,” J. Am. Soc. Hortic. Sci. , vol. 127, no. 3, pp. 448–454,
May 2002.
J. R. Rodríguez-Pérez, C. Ordóñez, A. B. González-Fernández, E.
Sanz-Ablanedo, J. B. Valenciano, and V. Marcelo, “Leaf water
content estimation by functional linear regression of field
spectroscopy data,” Biosyst. Eng., vol. 165, pp. 36–46, Jan. 2018, doi:
10.1016/j.biosystemseng.2017.08.017.
C. Acevedo-Opazo, S. Ortega-Farias, and S. Fuentes, “Effects of
grapevine (Vitis vinifera L.) water status on water consumption,
vegetative growth and grape quality: An irrigation scheduling
application to achieve regulated deficit irrigation,” Agric. Water
Manag., vol. 97, no. 7, pp. 956–964, 2010, doi:
10.1016/j.agwat.2010.01.025.
J. R. . Rodríguez-Pérez, D. . Riaño, E. . Carlisle, S. . Ustin, and D. R.
. Smart, “Evaluation of hyperspectral reflectance indexes to detect
grapevine water status in vineyards,” Am. J. Enol. Vitic., vol. 58, no.
Authorized licensed use limited to: Amrita School Of Engineering - Kollam. Downloaded on January 11,2023 at 07:13:59 UTC from IEEE Xplore. Restrictions apply.
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
3, pp. 302–317, 2007.
R. De Bei et al., “Non-destructive measurement of grapevine water
potential using near infrared spectroscopy,” Aust. J. Grape Wine Res.,
vol. 17, no. 1, pp. 62–71, 2011, doi: 10.1111/j.17550238.2010.00117.x.
M. Fang, W. Ju, W. Zhan, T. Cheng, F. Qiu, and J. Wang, “Remote
Sensing of Environment A new spectral similarity water index for the
estimation of leaf water content from hyperspectral data of leaves,”
Remote Sens. Environ., vol. 196, pp. 13–27, 2017, doi:
10.1016/j.rse.2017.04.029.
A. B. González-Fernández, J. R. Rodríguez-Pérez, V. Marcelo, and
J. B. Valenciano, “Using field spectrometry and a plant probe
accessory to determine leaf water content in commercial vineyards,”
Agric. Water Manag., 2015, doi: 10.1016/j.agwat.2015.03.024.
C. Acevedo-Opazo, B. Tisseyre, S. Guillaume, and H. Ojeda, “The
potential of high spatial resolution information to define withinvineyard zones related to vine water status,” Precis. Agric., vol. 9, no.
5, pp. 285–302, 2008, doi: 10.1007/s11119-008-9073-1.
D. J. Mulla, “Twenty five years of remote sensing in precision
agriculture: Key advances and remaining knowledge gaps,” Biosyst.
Eng., vol. 114, no. 4, pp. 358–371, 2013, doi:
https://doi.org/10.1016/j.biosystemseng.2012.08.009.
J. Jensen, Remote sensing of the environment: An Earth Resource
Perspective, 3th Editio. USA: Prentice Hall, 1996.
R. Pu, Hyperspectral remote sensing: Fundamentals and practices,
1st Editio. Boca Raton: CRC Press, 2017.
A. Damm et al., “Remote sensing of plant-water relations: An
overview and future perspectives,” J. Plant Physiol., vol. 227, pp. 3–
19, Aug. 2018, doi: 10.1016/J.JPLPH.2018.04.012.
A. Ali and M. Imran, “Remotely sensed real-time quantification of
biophysical and biochemical traits of Citrus (Citrus sinensis L.) fruit
orchards – A review,” Sci. Hortic. (Amsterdam)., vol. 282, no.
February, p. 110024, 2021, doi: 10.1016/j.scienta.2021.110024.
C. Atzberger, “Advances in remote sensing of agriculture: Context
description, existing operational monitoring systems and major
information needs,” Remote Sens., vol. 5, no. 2, pp. 949–981, 2013,
doi: 10.3390/rs5020949.
J. A. J. Berni, P. J. Zarco-Tejada, G. Sepulcre-Cantó, E. Fereres, and
F. Villalobos, “Mapping canopy conductance and CWSI in olive
orchards using high resolution thermal remote sensing imagery,”
Remote Sens. Environ., vol. 113, no. 11, pp. 2380–2388, 2009, doi:
10.1016/j.rse.2009.06.018.
J. Baluja et al., “Assessment of vineyard water status variability by
thermal and multispectral imagery using an unmanned aerial vehicle
(UAV),” Irrig. Sci., vol. 30, no. 6, pp. 511–522, 2012, doi:
10.1007/s00271-012-0382-9.
J. Gago et al., “UAVs challenge to assess water stress for sustainable
agriculture,” Agric. Water Manag., vol. 153, pp. 9–19, 2015, doi:
10.1016/j.agwat.2015.01.020.
T. Zhao, B. Stark, Y. Q. Chen, A. Ray, and D. Doll, “More Reliable
Crop Water Stress Quantification Using Small Unmanned Aerial
Systems (sUAS),” IFAC-PapersOnLine, vol. 49, no. 16, pp. 409–414,
2016, doi: 10.1016/j.ifacol.2016.10.075.
H. Hoffmann, H. Nieto, R. Jensen, R. Guzinski, P. Zarco-Tejada, and
T. Friborg, “Estimating evaporation with thermal UAV data and twosource energy balance models,” Hydrol. Earth Syst. Sci., vol. 20, no.
2, pp. 697–713, 2016, doi: https://doi.org/10.5194/hess-20-697-2016.
R. Ballesteros, J. F. Ortega, D. Hernández, and M. Moreno,
“Characterization of vitis vinifera l. Canopy using unmanned aerial
vehicle-based remote sensing and photogrammetry techniques,” Am.
J. Enol. Vitic., vol. 66, no. 2, pp. 120–129, 2015, doi:
10.5344/ajev.2014.14070.
[48]
[49]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[60]
[61]
[62]
D. Turner, A. Lucieer, and C. Watson, “Development of an
Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard
mapping based on visible, multispectral, and thermal imagery,” Proc.
34th Int. Symp. Remote Sens. Environ., p. 4, 2010.
L. G. Santesteban, S. F. Di Gennaro, A. Herrero-langreo, C. Miranda,
J. B. Royo, and A. Matese, “High-resolution UAV-based thermal
imaging to estimate the instantaneous and seasonal variability of
plant water status within a vineyard,” Agric. Water Manag., vol. 183,
pp. 49–59, 2017, doi: 10.1016/j.agwat.2016.08.026.
D. Gomez-candon, S. Labbe, N. Virlet, A. Jolivot, and J. Regnard,
“High resolution thermal and multispectral UAV imagery for
precision assessment of apple tree response to water stress,” Int. Conf.
Robot. Assoc. High- Technol. Equip. Agric. For. RHEA, vol. May,
pp. 279–288, 2014.
Y. B. Huang, S. J. Thomson, W. C. Hoffmann, Y. Bin Lan, and B. K.
Fritz, “Development and prospect of unmanned aerial vehicle
technologies for agricultural production management,” Int. J. Agric.
Biol. Eng., vol. 6, no. 3, pp. 1–10, 2013, doi:
10.3965/j.ijabe.20130603.001.
D. Gómez-Candón, A. I. De Castro, and F. López-Granados,
“Assessing the accuracy of mosaics from unmanned aerial vehicle
(UAV) imagery for precision agriculture purposes in wheat,” Precis.
Agric., vol. 15, no. 1, pp. 44–56, Feb. 2014, doi: 10.1007/s11119013-9335-4.
J. Martínez, G. Egea, J. Agüera, and M. Pérez-Ruiz, “A cost-effective
canopy temperature measurement system for precision agriculture: a
case study on sugar beet,” Precis. Agric., vol. 18, no. 1, pp. 95–110,
2017, doi: 10.1007/s11119-016-9470-9.
A. V. Zubler and J. Y. Yoon, “Proximal Methods for Plant Stress
Detection Using Optical Sensors and Machine Learning,”
Biosensors, vol. 10, no. 12, 2020, doi: 10.3390/bios10120193.
A. Hall, D. W. Lamb, B. Holzapfel, and J. Louis, “Optical remote
sensing applications in viticulture - a review,” Aust. J. Grape Wine
Res., vol. 8, no. 1, pp. 36–47, Mar. 2008, doi: 10.1111/j.17550238.2002.tb00209.x.
B. N. Candogan, M. Sincik, H. Buyukcangaz, C. Demirtas, A. T.
Goksoy, and S. Yazgan, “Yield, quality and crop water stress index
relationships for deficit-irrigated soybean [Glycine max (L.) Merr.]
in sub-humid climatic conditions,” Agric. Water Manag., vol. 118,
pp. 113–121, 2013, doi: 10.1016/j.agwat.2012.11.021.
“Crop water stress index is a sensitive water stress indicator in
pistachio trees,” Irrig. Sci., vol. 26, no. 5, pp. 395–405, 2008, doi:
10.1007/s00271-008-0104-5.
M. Meron, M. Sprintsin, J. Tsipris, V. Alchanatis, and Y. Cohen,
“Foliage temperature extraction from thermal imagery for crop water
stress determination,” Precis. Agric., vol. 14, no. 5, pp. 467–477,
2013, doi: 10.1007/s11119-013-9310-0.
J. L. Idso, S. B., Jackson, R. D., Pinter, P. J., Reginato, R. J., &
Hatfield, “Normalizing the stress-degree-day parameter for
environmental variability*,” vol. 24, pp. 45–55, 1981.
P. J. Zarco-tejada et al., “Remote Sensing of Environment A PRIbased water stress index combining structural and chlorophyll effects:
Assessment using diurnal narrow-band airborne imagery and the
CWSI thermal index,” Remote Sens. Environ., vol. 138, pp. 38–50,
2013, doi: 10.1016/j.rse.2013.07.024.
C. Poblete-Echeverría, D. Espinace, D. Sepúlveda-Reyes, M. Zúñiga,
and M. Sanchez, “Analysis of crop water stress index (CWSI) for
estimating stem water potential in grapevines: Comparison between
natural reference and baseline approaches,” Acta Hortic., vol. 1150,
no.
January,
pp.
189–194,
2017,
doi:
10.17660/ActaHortic.2017.1150.27.
J. Bellvert, P. J. Zarco-Tejada, J. Girona, and E. Fereres, “Mapping
Authorized licensed use limited to: Amrita School Of Engineering - Kollam. Downloaded on January 11,2023 at 07:13:59 UTC from IEEE Xplore. Restrictions apply.
[63]
[64]
[65]
crop water stress index in a ‘Pinot-noir’ vineyard: Comparing ground
measurements with thermal remote sensing imagery from an
unmanned aerial vehicle,” Precis. Agric., vol. 15, no. 4, pp. 361–376,
2014, doi: 10.1007/s11119-013-9334-5.
J. Bellvert, J. Marsal, J. Girona, and P. J. Zarco-Tejada, “Seasonal
evolution of crop water stress index in grapevine varieties determined
with high-resolution remote sensing thermal imagery,” Irrig. Sci.,
vol. 33, no. 2, pp. 81–93, 2015, doi: 10.1007/s00271-014-0456-y.
J. H. Prueger et al., “Crop Water Stress Index of an irrigated vineyard
in the Central Valley of California,” Irrig. Sci., vol. 37, no. 3, pp.
297–313, 2019, doi: 10.1007/s00271-018-0598-4.
E. Akkuzu, Ü. Kaya, G. Çamoğlu, G. P. Mengü, and Ş. Aşik,
[66]
[67]
“Determination of Crop Water Stress Index and Irrigation Timing on
Olive Trees Using a Handheld Infrared Thermometer,” J. Irrig.
Drain. Eng., vol. 139, no. 9, pp. 728–737, 2013, doi:
10.1061/(asce)ir.1943-4774.0000623.
S. Taghvaeian, J. L. Chávez, and N. C. Hansen, “Infrared
thermometry to estimate crop water stress index and water use of
irrigated maize in northeastern colorado,” Remote Sens., vol. 4, no.
11, pp. 3619–3637, 2012, doi: 10.3390/rs4113619.
M. Han, H. Zhang, K. C. DeJonge, L. H. Comas, and S. Gleason,
“Comparison of three crop water stress index models with sap flow
measurements in maize,” Agric. Water Manag., vol. 203, no. March,
pp. 366–375, 2018, doi: 10.1016/j.agwat.2018.02.030.
Authorized licensed use limited to: Amrita School Of Engineering - Kollam. Downloaded on January 11,2023 at 07:13:59 UTC from IEEE Xplore. Restrictions apply.