ET - INIA

EARTH OBSERVATION FOR MONITORING WATER
RESOURCES AND IRRIGATION DEMAND
Guido D’Urso
University of Naples Federico II & ARIESPACE srl Academic Spin-off
Key-words:
Sustainable use of natural resources
Planning and management of land and water
resources
Which tools ?
During the last three decades, we have assisted two main developments:
a) a detailed knowledge of the land surface processes through their
mathematical description based on measurable parameters
b) the availability of new generations of sensors, with enhanced spectral
and spatial resolutions
GIS
+
Earth Observation
+ Models
u  z 
u*  z 
ln 

k  z0 m 
R
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T0
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R
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RS
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q(z,t)
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Agro-hydrological models and E.O.
techniques for improving water
management in agriculture
1. derivation of spatially distributed data concerning
land surface attributes, i.e. surface albedo,
fractional vegetation cover and Leaf Area Index –
VISIBLE-NEAR INFRARED wavelengths
2. estimation of instantaneous values of water balance
terms, i.e. actual evapotranspiration and soil
moisture - THERMAL IR wavelengths
We do not talk now about satellite sensors in the
thermal infrared (TIR):
temporal and spatial resolutions unsuitable for
applications at farm scale (in most cases)
MODIS (1000 m)
Landsat (100 m)
Observations in the VIS-NIR
Observations in the VIS-NIR
Satellite sensors in the VIS-NIR
Sensors
capabilities
improvements
Landsat 7 ETM+ => 30 m
SPOT5 => 10 m
Quick Bird => 2,8 m
Landsat 8 image availability from Oct. To Apr. South. URUGUAY :
http://earthexplorer.usgs.gov/
Path 224 Row 84
6
Landsat 8 image availability from Apr. to Sept. South. ITALY :
http://earthexplorer.usgs.gov/
Path 189 Row 32
6
Major recent breakthroughs in satellite Earth Observation:
RapidEye (2008):
 Constellation of 5 satellites
 Daily coverage for any location
 6.5 m spatial resolution
 First sensor with imagery in the
red-edge spectral region
(important for vegetation)
Major recent breakthroughs in satellite Earth Observation:
WorldView-2 (2009):
 Constellation of 5 satellites
 Daily coverage for any location
 0.5 m spatial resolution
 First sensor with 8 bands from
visible to near infrared
Sentinel-2
Launch:
Sentinel-2A in 11 June 2015
Sentinel-2B in 2016
13 spectral bands
spatial resolutions of 10 – 20 m
Potential applications for hyperspectral remote sensing in precision
agriculture:
Revisit time: 5 days with 2 satellites
• Crop N stress detection
• Chlorophyll content
• Weed mapping
• Pest & disease mapping
http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Last_stretch_before_being_packed_tight
• Bare soil imaging for management zones delineation
EO data provider
Delivery to final user
EO image processing center
The entire processing
chain (including delivery
to final user) can be
Map product
completed within few
hours from the satellite
acquisition
Which answers satellite Earth Observation
can provide to farmers’ questions:
•
•
•
•
How is the crop growing ?
Is growth uniform over my plot ?
How much irrigation apply ?
There are weeds or diseases
spreading?
Walnut in France: medium price €/kg
• soil cover, leaf area, biomass, water
stress, nutrient stress
• Crop vigour maps
• Evapotranspiration and irrigation
requirement maps
NNI (Nitrogen Nutrition
Gross Index)
income
€/ha from
obtained
remotely sensed data.
Variable rate N fertilization
map (kg N ha−1) on the
basis of the Nstatus value in
each pixel. The suggested
rate is shown only for
pixels belonging to N
deficient areas
FATIMA EU PROJECT
JUST STARTED
ON THIS ISSUE
Research development
Huge knowledge (more than 30 years)
on applications of optical remote
sensing for crop conditions assessment
Multispectral reflectance and temperature
of crop canopies relates to two basic
physiological processes: photosynthesis
and evapotranspiration.
In both processes Leaf Area Index (LAI) is
the fundamental canopy parameter.
(Moran et al., Remote Sensing Environm., 1997)
LAI measurements
SPARC 2003
campaign
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CLAIR model cal.
LAI (LICOR LAI-2000)
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WDVI
LAI = - ln( 1 )

WDVI 
G
ar
1
lic
fa
l fa
0.00
The final value of  was taken in correspondence of the minimum
error between observed and estimated LAI
14/07 - LAI map from CLAIR model (WDVI, Clevers, 1989)
LAI (CLAIR model)
7,000
6,000
y=0,89x
R^2=0,80
Alfalfa
Corn
Onion
Garlic
Potato
SugarBeet
1:1
5,000
4,000
3,000
2,000
1,000
0,000
0,000
1,000
2,000
3,000
4,000
5,000
LICOR LAI-2000 - Ground
measurements
6,000
7,000
RMSE=0.59
The empirical relationship has been verified by using 40 independent field
measurements.
Piana del Sele: Mappa del LAI derivata da immagini SPOT4 (risoluzione 20 m)
LAI maps for canopy and yield management
Irrigated vineyards, Sella e Mosca, Sardinia
P-M and vegetation parameters:
ETp under standard conditions (FAO 56)
This is the evapotranspiration from disease-free, well-fertilized crops, grown in
large fields, under optimum soil water conditions and achieving full production
under the given climatic conditions.
By multiplying ETo by the crop coefficient, ETp is determined.
Two calculation approaches are outlined: the single and the dual crop coefficient
approach.
This value can be used to define the maximum amount of
irrigation water to be applied.
P-M and vegetation parameters:
ETp under standard conditions
1 ( Rns  Rnl  G )  87.52  DE / ra
ET
E pp 

   (1  rc / ra )
Rns  (1  r ) St
Rt
rc 
0.5LAI
 zU  2 hc   zT  2 hc 
3  ln 
3 
ln 
 0.123hc   0.0123hc 
 

ra  
0.168U
Based on the definition of crop water requirements of
F.A.O. Paper 56 (1-step approach - Penman-Monteith equation):
P-M and vegetation parameters:
ETp under standard conditions
Experimental values of ‘crop coefficients’ Kc have been proposed by
Doorenbos and Pruitt (1977). Due to its simplicity, the crop coefficient
approach is still widely used in irrigation scheduling (FAO, 1998). In reality,
the value of Kc is related to the actual development of the canopy and to the
environmental conditions.
By combining ETp with ET0, Kc can be analytically defined as:
Kc  f ( K  ,Ta , RH ,U ; r, LAI , hc )
The Kc concept was introduced because of the difficulties related
with the measurements of vegetation parameters.
Earth Observation is the solution to this issue.
Estimation of potential evapotranspiration
(upper boundary)
MULTISPECTRAL
SATELLITE DATA
0.5
ALBEDO r MAP
0
0.5
1
1.5
AGROMETEO
DATA
LAI MAP
K, Ta, RH, U

ETp (K  ,Ta , RH ,U ;
IMAGE PROCESSING
hcrop MAP
r , LAI ,hc )
ETp MAP
ETo mm/d
non irriguo
< 1.5
1.5- 2.55
2.55 - 3.2
3.2 - 3.85
3.85 - 4.5
4.5 - 5.15
5.15 - 5.8
2 Kilometers
Methodological background (in brief)
Vuolo, D’Urso et al., Agric. Water Manag., 147: 82-95
http://dx.doi.org/10.1016/j.agwat.2014.08.004
-
Within 48 hours from
each image
acquisition:
a)
Pre-processing of EO
images;
b)
EO-based crop
development products;
c)
Calculation of CWR
and suggested
irrigation depth (pixelscale and plot scale);
d)
Delivery of information
to final users.
Validation of PenmanMonteith EO based on
irrigated crops (NO
STRESS)
ETactual from Eddy Cov.
Chicory
from Burba & Anderson
Maize
Alfalfa
Vineyard
Irrigated vineyards, Sardinia
ET = 0.56 ET
from doy 226 to doy 239 - Vigna
act
0.80
y = 0.56x
R2 = 0.58
0.6
from 7:00 to 11:00
0.60
0.5
from 12:00 to 16:00
from 17:00 to 19:00
0.4
0.40
ETa
ETact
ET
reale
p
0.3
0.2
0.20
0.1
0.00
0.00
0
0.20
0.40
ET potenziale
ETp
0.60
0.80
0
0.1
0.2
0.3
0.4
ETo * ETp
Kc (ASD)
0.5
0.6
D E M E T E R
Project co-funded by the European Commission
2002-2005
Satellite-Assisted Irrigation Advisory
Service e-SAIAS
SIRIUS: EO for river-basin governance (end 2014)
Space-assisted services for Sustainable Irrigation:
Tools & instruments for implementation of WFD &
Sustainable Development Strategy (SDS):
* water use monitoring, * water saving,
* enabling true participation/collaboration
SPIDER webGIS + ppgis +
+ multi-sensor constellation +
+ water footprint:
Service to water managers at
farm, irrigation scheme,
aquifer, river-basin
www.sirius-gmes.es
slide 31
Definition of Users’ requirement for satellite-based
information service for crop management
 Spatial resolution: between 1 and 20 m depending
on farm extension and management
 Temporal resolution: 3-8 days
 Delivery of product to final users: within 24-48 hours
from satellite acquisition (larger time lag depending
on applications)
 Product accuracy: ± 10%
Technological implementation = find a balance for the
following issues
i.
availability of ancillary input data, with no or minimal
contribution from end-users
ii.
elaboration and processing time, with minimum
possible time lag between E.O. acquisition date and
information delivery to final users
iii. accuracy of algorithms for deriving crop water
requirements, with minimum possible
parameterisation.
the NEW FARMER’ HOUSE: tools for enhancing the
traditional background experience with scientific
knowledge and new technologies
advancements in
farm management
technologies
(INTERNET - GPS)
development of
new imaging
Geographical
systems
Information Systems from space
Soil and crop
variability,
meteo data
scientific knowledge
of crop growth
processes
An example of….
How users access the data ?
www.irrieye.com
1.
Farmer access
2.
Admin access
Which data are given to the farmers ?
1
Mapping of the effective crop vigour
Which data are given to the farmers ?
2
Acquiring local meteorological data
… and now also weather forecast
ETcrop
Easy integration with other data at farmlevel (i.e. from soil moisture sensors)
Which data are given to the farmers ?
3
Irrigation advices:
maximum irrigation amount calculated by considering
the ACTUAL CROP DEVELOPMENT
EXAMPLE OF SATELLITE-BASED IRRIGATION
ADVISORY SERVICE IN ITALY and South AUSTRALIA
• The “irrisat” farmer…
80
700
START
70
600
500
50
400
40
300
30
200
20
Cumulative
(cubic meter / ha) .
ETP, water supply .
(mm/day)
60
Farm: ****
crop: Maize
ETP and water supply
supply
ETP
100
10
supply cumulative
ETP cumulative
0
0
1/6
8/6
15/6
22/6
29/6
6/7
days
13/7
20/7
27/7
satellite acquisition
• Another comparison …
Consorzio di Bonifica Destra Sele
Volumi irrigui specifici (in mm) e totali per i distretti irrigui della piana in
Destra Sele (Campania), per la stagione irrigua 2005 (Progetto DEMETER)
Volumi irrigui misurati e stimati con dati di Osservazione della Terra nel
distretto irriguo “Boscariello”, Consorzio di Bonifica Destra Sele, nel 2012
Lessons learned ….
Farmers want to know how their
crops are growing
(am I doing well?)
Integrate EO data with the
standard management procedures
of farmes
Need of regular contacts with
farmers for training in using new
technologies and HOW handling
the information they get
Some conclusions…
E.O. is now entered in the real world of agricultural
applications thanks to improved spatial and temporal
resolution of VIS-NIR data
More and more data available (cost tending to zero!), more
knowledge of processes and data interpretation, more
computation power, ICTs, spin-off and so on …
Farmers finally on the way to accept and implement
innovations in their current practices ….
www.irrimet.eu - www.irrieye.com
durso@unina.it