=Paper=
{{Paper
|id=Vol-2761/HAICTA_2020_paper33
|storemode=property
|title=Smart Water Management for Irrigation Purposes: The SWSOIP Project
|pdfUrl=https://ceur-ws.org/Vol-2761/HAICTA_2020_paper33.pdf
|volume=Vol-2761
|authors=Christiana Papoutsa,Christos Theocharidis,Maria Prodromou,George Papadavid,Dimitris Sykas,Silas Michailides,Michalakis Christoforou,George Kountios,Anastasis Kounoudes,Marios Milis,Diofantos Hadjimitsis
|dblpUrl=https://dblp.org/rec/conf/haicta/PapoutsaTPPSMCK20
}}
==Smart Water Management for Irrigation Purposes: The SWSOIP Project==
https://ceur-ws.org/Vol-2761/HAICTA_2020_paper33.pdf
Smart Water Management for Irrigation Purposes:
The SWSOIP Pproject
Christiana Papoutsa1,2, Christos Theocharidis1,2, Maria Prodromou1,2, George
Papadavid3, Dimitris Sykas1, Sιlas Michailides1,2, Michalakis Christoforou1,2, George
Kountios1, Anastasis Kounoudes4, Marios Milis4, and Diofantos G. Hadjimitsis1,2
1
Department of Civil Engineering and Geomatics, Cyprus University of Technology,
Limassol, Cyprus; e-mail: d.hadjimitsis@cut.ac.cy
2
Eratosthenes Centre of Excellence, Limassol, Cyprus
3
Agricultural Research Institute, Nicosia, Cyprus
4
SignalGeneriX Ltd, Limassol, Cyprus
Abstract. It seems that the future scenarios for water resources management are
characterized by increasing demand and by the short-term unsustainability of
many reservoirs in the Mediterranean basin. To address these scenarios,
improved management of water resources was needed for water economy, and
water recycling policies. Furthermore, agriculture characterized as the largest
water user worldwide and the monitoring of the agriculture via remote sensing
techniques is an enormous subject where it used for special scientific
applications such as irrigation, precision farming, yield prediction, estimation of
evapotranspiration etc. The main objective of this paper is to present the current
situation of water resources in the Mediterranean region and present the
methodology and main objectives of the SWSOIP project which aims to develop
a smart watering system for the irrigation process based on the estimation of
evapotranspiration using both in-situ data (spectroradiometric, LAI, CH and
meteorological) and Sentinel satellite data.
Keywords: water management; agriculture; remote sensing; smart watering
system.
1 Introduction
It is indisputable that water is an invaluable element for the smooth running of our
planet's life. It is the vital resource for ecosystems while at the same time, the basic
needs of the human population are met by it, thus being the key to the development of
fisheries, agriculture, energy production, industry, transport and tourism. While water
characterized as a renewable resource, it cannot be considered inexhaustible. The
seeming abundance of water has resulted in a man being considered a given good and
being replaced by nature for free, leading to irrational use and pollution (Fragkou and
Kallis, 2010).
215
According to IPCC (2008), the term climate change refers to the difference in the
state of the global climate, which is expressed by significant fluctuations in the average
meteorological conditions that extend over decades or even more years. These
changes, have a direct impact on water resources and the global hydrological cycle,
exacerbating the water crisis caused by poor management with a high cost to people
who do not already have access to clean water (UNFCCC, 1992).
The impact of climate change is harmful to agriculture. The degradation of
agricultural water resources as well as the loss of fertile soil, are events that require the
adoption of strategies aimed at ensuring protect food security and rural vitality. These
strategies, achieved by limiting the consumption of natural resources through the
promotion of agro-environmental practices, alternative agricultural methods, crop
diversification and water and soil conservation while limiting the use of natural
resources fulfil (FAO and Plan Bleu, 2018).
Agriculture existed several thousand years ago, and its development is mostly
guided and influenced by the climatic differences of cultures and the existing
technology in them. However, agriculture inextricably linked to the techniques for
expanding and managing soils suitable for growing domesticated plant species.
Furthermore, a significant link exists between agriculture and water also, where
according to the Food and Agriculture Organization of the United Nations, agriculture
characterized as the largest water user worldwide (Dubois, 2011). Future estimations
indicate that the world population will reach between 8.4 and 8.6 billion people by
2030 and 9.5 and 13.3 billion in 2100 (Nations, 2015), thus before that happened, to
fulfil the growing needs, global agriculture production will have to increase by 60 per
cent from 2005/2007 levels (Alexandratos and Bruinsma, 2012). However, the
expected increase in agricultural production must be followed up by vital management
of agrarian lands since it has adverse effects to the quality/quantity of water and soil
resources, biodiversity, greenhouse gas emission or land degradation (Gomiero et al.,
2011).
In agriculture processes, the optimal management of water in agrarian lands has
always been of great importance, specifically to the most water-intensive ones. Crop
health problems are more likely to relate to the lack of or overflowing in water
irrigation. Thus, estimation of evapotranspiration (ET) tends to be a necessary process,
to face water management problems and find a viable solution in croplands.
ET from agriculture lands, “plays” crucial role to the terrestrial hydrological cycle.
Definition of ET is the loss of water from the ground, lake or vegetation regions to the
atmosphere through the evaporation of liquid water. Therefore, evaporation and
transpiration are the component key of ET in agroecosystems. It is momentous to keep
a water balance between protecting the sustainability and productivity of the
agroecosystems (Irmak, 2008).
Monitoring water resources traditionally determined by collecting samples from the
field campaigns, where the biological, physical, and chemical properties of water
examined through laboratory analyses of these samples. Although these in-situ
measurements provide high accuracy, they lag in spatial analysis and present
difficulties of successive and integrated sampling. Also, traditional methods cannot
determine spatio-temporal variations in water quality required for a comprehensive
assessment and management of water resources. In other words, there cannot provide
216
a simultaneous database corresponding to a regional or a broader scale (Duan et al.,
2013a, 2013b; Gholizadeh et al., 2016).
A significant problem nowadays since climate change began is water scarcity and
drought. Water scarcity refers to the non-existence of water in a water supply system
which may lead to limitations on consumptions which caused by drought and human
activities such as overpopulation or unfair access to water (El Kharraz et al., 2012).
Drought and scarcity have a massive impact on the environmental and socio-economic
aspects of the Mediterranean countries. The Middle East is the area with the most
severe water scarcity in the world while at the same time, critical water shortages
located in the Eastern Mediterranean region (Jägerskog, 2003; Tropp and Jagerskog,
2006). Innovative water strategies required to encounter the environmental issues in
the Mediterranean region (Ferragina, 2010).
For the prevention and suppression of the problems mentioned above; near-real-
time monitoring needed. Remote sensing is a vital tool to handle this situation, which
is used since the 1970s and continues widely used up to date. Remote sensing
techniques can effectively and efficiently monitor water resources and detect any
problems from local to a global scale with high spatial-temporal analysis. (Anding and
Kauth, 1970; Giardino et al., 2014; Hadjimitsis and Clayton, 2009; Saad El-Din et al.,
2013).
Although the era of satellite remote sensing began in 1957 by the Russians with the
launch of Sputnik-1, the first satellite explicitly designed for Earth observation was
Vanguard-2, which replaced by TIROS meteorological satellite series in 1960 and
continued with Landsat multispectral and thermal sensors since 1972 (Tatem et al.,
2008). These sensors were the start for mapping, analyze and estimate ET across broad
spatial and temporal scales. From that moment, a variety of satellites mission launched,
and numerous scientific researches took place trying to estimate evapotranspiration
accurately. Many different models conducted to measure ET. Specifically, there are
temperature-based ET models and conductance-based ET models. The latter category
has been discovered first, where Penman (1948) combined the effects of atmospheric
drying power and available energy on evapotranspiration. Then Monteith modified the
Penman equation by including stomatal resistance, surface control and replacing wind
speed dependent coefficient, to make the equation more suitable for terrestrial surfaces,
creating the Penman-Monteith ET model (Monteith, 1973). On the other hand, the first
temperature-based model conducted with the help of a thermal scanner mounted on an
airplane (Bartholic et al., 1972).
A vast number of empirical methods have been deployed since Penman made a start
and numerous scientists and specialists worldwide tried to estimate evapotranspiration
from areas with a different climate. Some early studies used the Penman-Monteith
model to derive ET from croplands via meteorological conditions (wind speed,
radiation, temperature humidity) by scaling it using a crop coefficient (Bausch, 1993;
Choudhury et al., 1994; Jackson et al., 1980). In 1990, FAO organized a conference
with experts and researchers where the Penman-Monteith combination method
recommended as the new standard for reference evapotranspiration. Also, an update
made in the procedures for calculation of the various parameters (Allen et al., 1998).
In the sequel, this method became the basis on which a lot of alternative methods were
developed (Allen et al., 1998; Arain et al., 2002; Liu et al., 2003) and comparisons
made (Luo et al., 2018).
217
2 The SWSOIP Project
As mentioned above, water is a very important factor in agriculture. The savings of
water in areas that are facing with water scarcity problems like Cyprus, requires the
adoption of measures that will serve to conserve water. Agriculture characterized as
the largest water user worldwide and the monitoring of the agriculture via remote
sensing techniques is an enormous subject where it used for special scientific
applications such as irrigation, precision farming, yield prediction, estimation of
evapotranspiration etc. For these purposes, to protect the water resources, the SWSOIP
project was used on a pilot basis. The SWSOIP project is based on remote sensing
techniques and focuses on water management.
SWSOIP is used in this paper as the abbreviation of the: ‘Smart Watering System
for Optimizing Irrigation Process’. The main goal of the SWSOIP Project aims to
provide ‘new’ irrigation data based on the indirect estimation of evapotranspiration
using both satellite and meteorological inputs. This data can be used to inform the
producers and the decision–makers for the water demand of their crops aiming to better
and more rational management of irrigation water. The ‘Smart Watering System’ will
automatically estimate the water demand for irrigation purposes and will release
automatically the optimum water quantity for each crop-type through the ‘Smart
CropWATER Valve’ without any human intervention.
SWSOIP platform (https://www.swsoip.com/) consists from the frontend and
backend system. The SWSOIP frontend aims to communicate with the farmers in order
to collect inputs related to the farmers and their crops such as farmers’ id; crop type;
cultivation date; plot area; etc. and provide outputs to the farmers related to the water
needs of their crops. The SWSOIP backend aims to gather and process all the data such
as satellite; meteorological and in-situ (spectroradiometric; LAI; CH) based on the
inputs of the farmers. Then the backend system will be able to estimate the irrigation
demand for each farmer and plot and will communicate this output both with the
frontend system to inform the farmer and the WISENSE Platform which will transfer
this information to the CropWATER Valve to provide automatically the estimated
water quantity to the crops without any human intervention.
The proposed product is expected to contribute and have an effective impact on
water saving and smart management of water resources since lack of water is one of
the most serious problems that Cyprus has been facing for centuries and agriculture
accounts for about 69% of the total water consumption.
3 Methodology
The proposed ‘Smart Watering System’ will consist of 3 Stages. The processing
workflow shown in Fig. 1 is divided into 10 steps. The 3 Stages are defined as follows:
(i) Input (Steps 1-3) (ii) Reading / Processing (Steps 4-7) and (iii) Output (Steps 8-10).
The overall methodology consists of the following 10 steps:
218
• End-users login / Inputs from 5 end-users (such as crop-types, cultivated area, phenological
stage, water-cost, comments or needs, contact details)
• Cross-check of inputs
Input • Create unique ID-code for each end-user
• Processing Sentinel data on 'spot' - retrieve crop parameters readings
• Retrieve meteorological data on 'spot' - retrieve meteorological readings
• Field measurements at the same time with Sentinel acquisition
Reading / • Processing readings, calculate and send the estimated water demand to the sms card for each
Processing end-user (ID-code)
• Development of the Smart 'CropWATER' Valve: reading inputs from sms card & release the
required amount of water
• Development of the 'CropWATER' app which will inform the end-users for the irrigation
demand of their fields
Output • Validation of results / product - Feedback from the end-users (interviews)
Fig. 1. Flow chart of the proposed methodology.
The methodology will be applied for selected crop types. The Penman-Monteith
algorithm will be applied individually for each crop employing the necessary crop
parameters using Sentinel data. The input parameters will differ according to the crop
type for example leaf area index (LAI), crop height (CH), albedo (crop parameters)
takes various values. Since Sentinel images will be acquired every week the
development stages of the crops will be immediately identified. For the
implementation of this study, selected farmers will collaborate with Agricultural
Research Institute (ARI) and all the necessary parameters such as the crop-type,
planting day, phenological stage and crop area will be given. The steps for the
implementation of the SWSOIP is given in Fig. 2.
List of input parameters:
Monteith (Monteith and Unsworth, 1990), is a function of climate data such as
temperature (T), humidity (RH%), solar radiation (Rs) and wind speed (U) and crop
parameters, such as the surface albedo (a), the leaf area index (LAI) and the crop height
(CH) which can be used to predict ETc:
ETc = f ( a, LAI ,CH, T, RH%, Rs, U ) (1)
Penman-Monteith adapted to satellite data algorithm:
Penman-Monteith method adapted to satellite data will be used to estimate ETc in
mm/day. The specific equation needs both meteorological and remotely sensed data to
be applied. The ETc is estimated using remote sensing after CH and LAI maps
are created to specify these parameters spatially through Vegetation Indices. The
algorithm provides at the end of the procedure, direct values of daily ETc through maps
of evapotranspiration.
219
Insert & Validate Farmers Inputs
o Personal information (such as name & contact information) – Farmer ID
o Crop type
o Plantation date Front-end
o Size & location of field system
Data Collection
o Meteorological through meteorological & telemetry station
o Ground field data: derived spectroradiometric
o Satellite images to retrieve CH, LAI & albedo using the developed
models
Back-end
system
Retrieval of ETc
o ETc = f (albedo, CH, LAI & Meteorological)
Send information to end-users
Front-end
o SMS to farmers through CropWATER Mobile-App
system
o Info to Smart CropWATER Valve
Fig. 2. Implementation plan of the methodology.
4 Current Status of the project
4.1 Contact frontiers farmers and start registered them to the SWSOIP Platform
Experimental fields were selected after the evaluation of farmers’ questionnaire.
Farmers growing potatoes, onions, alpha-alpha, ground nuts, black-eyed beans and
beans in large plots (< 50m X 50m) where asked and agreed to add their field in to
SWSOIP experimental plan. Farmer were trained to add their fields into the SWSOIP
platform following the JOIN SWSOIP menu button which is available through the
SWSOIP website (swsoip.com) (see Fig. 3). The link is provided both in Greek and
English language. Farmers were asked to add their full name and email address, type
of crop, date of planting and expected date of harvesting and finally to add a kmz or
kml file indicating with a polygon the cultivated surface are of the crop. Farmers
agreed to provide all necessary information regarding the crop cultivation, irrigation
and nutrition stages. Furthermore, farmers agreed to provide access to SWSOIP
researchers into their plots in order to collect ground data (Spectroradiometric / LAI /
220
CH) and install necessary equipment (smart irrigation system combined with a
2”electric valve connected with a 2”hydrometer and a controller).
Fig. 3. Front End-User Registration form.
4.2 Collecting field data
During the SWSOIP project the following crops will be examined: potatoes, ground
nuts, beans, alpha-alpha, onions and black-eyed beans. The selection of the crops was
based on the input received by the frontiers farmers and are the crops more frequently
cultivated by the farmers during the study period in the selected study area which is
the Mandria village in Paphos, Cyprus. The crops phenological cycles was observed
following the BBCH Monograph. The extended BBCH-scale is a system for a uniform
coding of phenologically similar growth stages of all mono- and dicotyledonous plant
species (Hack et al., 1992).
Data were collected in different plots within the study site area as also from fields
in different cultivations areas in Cyprus. Observations began after the seeding date of
each crop. The seeding time depends on the microclimate of each area. Field
measurements including LAI, spectroradiomeric and CH data (Fig. 4) have been
completed covering the phenological cycle for potatoes and onions; they are on-going
for ground-nuts and they will start according to the plantation period for the rest of the
crops. The spectral signatures of the potatoes, onions and ground nuts related to the
BBCH are presented in Fig. 5.
221
Fig. 4. (a) Potato: LAI data collection below canopy and inter row; (b) Potato: Data collection
using a GER1500 spectroradiometer in potatoes during BBCH19 (left) and BBCH 39 (right);
(c) Potato BBCH 38: 80% of plants meet between rows, measuring plant diameter
Fig. 5. Spectral signatures following the BBCH phenological cycle: (a) Potato; (b) Onions and
(c) Ground nuts.
222
4.3 System Architecture
Following you can see a presentation of the SWSOIP software system
architecture. In Fig. 6 the high-level overview of the system is presented.
Fig. 6. SWSOIP software system architecture.
Up to now, the following sub-systems have already been developed:
i. The User Registration Front-End is the system that registers the farmers
and their crops to the SWSOIP system. Due to lack of technological
adoption by the farmers, it was found that the most practical way to register
farmers together with their parcels (as geometries) requires a very
simplified approach. For this reason, the form type registration was chosen
instead of an interactive complex webGIS tool.
ii. Currently a total of 21 farmers have been registered and imported to the
SWSOIP system through the above described front-end.
iii. The Mobile Application has been designed and is now on the
implementation phase and the ‘beta-release of CropWATER Mobile-app.
Based on the user responses, a simple parcel oriented mobile app is needed.
The mobile app screens are the following: Login Screen; User information
overview; Parcel(s) information overview; Current Parcels status; Past
parcels status and Notification page
iv. The components of the Virtual Machine – Back End System such as the
Application Server; the Database Server and Storage and the Daemon
Service and scripts are currently under design and development phase.
223
5 Conclusions
The main goal of the SWSOIP Project aims to provide ‘new’ irrigation data based
on the indirect estimation of evapotranspiration using both satellite and meteorological
inputs. The ultimate goal is the development of a Smart Irrigation System for
EFFICIENT and EFFECTIVE Water Resources Management. The ‘Smart Watering
System’ will automatically estimate the water demand for irrigation purposes and will
automatically release the optimum water quantity for each crop-type without any
human intervention through the ‘Smart CropWATER Valve’. The proposed product is
expected to contribute and have an effective impact on water saving and smart
management of water resources since lack of water is one of the most serious problems
that Cyprus has been facing for centuries and agriculture accounts for about 69% of
the total water consumption.
Acknowledgments. This research is supported by ESA for funding the SWSOIP
Project ‘Smart Watering System for Optimising Irrigation Process’, Contract No.
AO/1-8770/16/NL/SC, 1st Call for Outline Proposals under the Plan for European
Cooperating States (PECS) in Cyprus (https://www.swsoip.com/).
This paper was developed within the framework of the EXCELSIOR project, that
has received funding from the European Union’s Horizon 2020 research and
innovation programme under Grant Agreement No 857510 and from the Government
of the Republic of Cyprus through the Directorate General for the European
Programmes, Coordination and Development (https://excelsior2020.eu/).
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