1. Geospatial Technologies for Mapping and
Monitoring Irrigated Agricultural Areas
Christopher M. U. Neale
Professor
Dept. of Civil and Environmental Engineering
Irrigation Engineering Division
Utah State University
2. Outline of the Presentation
Describe an international project: the
development of a digital cadastre of irrigation
water users in the Dominican Republic
Airborne remote sensing of agricultural and
natural resources
Estimation of Crop Evapotranspiration and
Yield with Remote Sensing
3. Estudios Básicos Para El Manejo de Los
Sistemas de Riego
Programa de Administración de Sistemas de
Riego por los Usuarios (PROMASIR)
Préstamo BID 905/OC-DR
Instituto Nacional de Recursos Hidráulicos
INDRHI
Dominican Republic
4. Basic Studies for Management of Irrigation Systems
• Contract between INDRHI and (Utah State University) funded
by the Inter-American Development Bank
• $7.9 million dollars
• Duration: 4 ½ years
• Comprised 4 studies:
1. Aerial Photography of the Entire Country
2. Cadastre of Water Users (Padrón de Usuarios)
3. Hydro-Agricultural Information System
4. Monitoring of Salinity and Drainage Problems
These studies had the objective of providing basic information
required for the transfer of Operation and Maintenance of 30
canal-based irrigation systems from the government control
(INDRHI) to newly formed Irrigation Water User Associations
5. Purpose of the studies:
• In the Dominican Republic water is still distributed in
fixed amounts and charged according to area served
and not based on volume. Therefore a good estimate
of irrigated area for each property is needed for proper
assessment of system operation and maintenance fees.
• An up-to-date cadastre of irrigators is necessary in the
transfer of publicly operated system to private
irrigator cooperatives so that appropriate Irrigation
Systesm Operation and Maintenance charges can be
estimated
• Problems with salinity needed to be identified prior to
the transfer in order to be included in the civil works
and mitigation plans
6. Final Results of the Aerial Photography Campaign
99.6 % of the Country covered with color photographs at 1:20000 scale
7. Remote Sensing based Products
Color aerial photographs at 1:20,000 scale of the entire
country in digital format
Color contact prints of the aerial photography at 1:20,000
Color and Black and White diapositives at 1:20,000
Digital orthophotoquads at 1:5000 for the irrigated areas of
the country: (4400 Km2)
Contour curves at 1-meter intervals, of the irrigated areas
(ortofotos)
High resolution multispectral image mosaics of the
irrigated areas
Digital Cadastre of Irrigation Water Users and Information
Database for all the irrigation systems of the country
8. Digital Aerial Photograph at 1:20,000 scale
The Basic Product
Resulting from the
scanning of the
negative with
software producing a
color digital positive
10. Diapositive of the original photo:
Used in analytical stereoplotters for the production of
orthophotos and updating of topographic maps
Diapositives are transparent
photos (like a slide)
11. Surveying of Ground Control Points used in the
aerial triangulation and production of orthophotos
High Precision Dual frequency GPS
systems were used to obtain the
ground control points for mapping
12. Installation of GPS base station network
to support aerial photography and ground control activities
Data from the base stations were used to correct the positions of the
mobile units based on differential correction techniques, resulting in high
positional accuracy. The network of GPS base stations installed was similar
to the CORS network in the US.
13. Location of the GPS Base Station in the DR
San Cristóbal, Barahona, San Juan, Villa Vázquez, La
Vega, Nagua, Higuey
16. After the development of the digital products, planning for irrigation of new
systems was done digitally, such as the Azua II Irrigation Project
Digital Orthophoto Mosaic with Contour Curves at 1-meter intervals
19. What is a Digital Cadastre of Irrigators?
It is a database containing information on all the
property owners and irrigation water users within an
irrigation project or system. It consists of:
An up-to-date property boundary map containing
information on every property owner or water user
Information on total area and irrigated area of each
property
Information on the canal system that delivers water to the
property
=> Because of the geographic and distributed nature of the
information as well as the requirement to have an
associated database, USU opted in using Geographic
Information System (GIS) technology to develop this
product.
20. Digital Orthophoto at 1:5000 scale
The map base for the cadastre of irrigation water users
The orthophoto is a
digital photograph with
geographic coordinates
that has been adjusted
to the terrain so that
areas can be correctly
measured
21. Printed Orthophoto at 1:4000 scale were used for field
verification of property boundaries
Field brigades used printed and
laminated maps to identify the
property boundaries together with
the land owner or a local facilitator
that has a good knowledge of the
irrigation system (president of a
water association, ditch rider)
A brigade consisted of 4 to 6
trained cartographers with one 4x4
dual cabin pickup and 3 or 4 cross-
country motorbikes
22. Survey Form used in Information Gathering by the Cartographers
The cartographers conduct the survey
with the property owner to obtain
basic information on the water user
(complete name, nickname, address)
and on the property (delivery canal,
water distribution problems, crops
planted, salinity or drainage problems)
Information was digitized using an
Access DB application to automate
the data entry. This work was done in
the Dominican Republic at a Lab in
USU’s local office.
23. On-screen digitizing of the
parcel boundaries in
ArcInfo using
the digital ortho as a
backdrop
Most of the digitizing was
done at USU using
battalions of graduate and
undergraduate student
technicians. Also the
merging with survey
database and Quality
Control
Observation: We were very good
clients of FEDEX!
24. Some characteristics of the digital
cadastre of irrigation water users
• Union of the geographic layer containing the property
boundaries with the information in the surveys
• The geographic coordinates come from the digital
orthophotos
• The property area is exact
• The database is complete because all properties within
the command area of a main canal or irrigation system
are included
• Can be readily and easily updated
• The property boundaries can be updated if a
consolidation or division of properties occurs
25. The Water Users Database Prior to the
Estudios Basicos Project
Essentially a listing of users => Rapidly became obsolete, areas were not
correct, water users were repeated, owners of multiple properties
sometimes were not registered and did not pay for the water. Some
irrigation systems had property boundary maps but were static in time.
26. Delivered Products
Water Users Database and Property Boundary Layers
Study Goals Goals Accomplished
Cadastre maps for Cadastre maps for
81,000 properties over 84,500 properties over
4400 Km2 of 4460 Km2 of
orthophotos of orthophotos (net) of
irrigated areas irrigated areas
Irrigator database for Irrigator database for
all the irrigation 278 irrigation systems
systems within 4400 over 5400 Km2 of
Km2 of orthophotos orthophotos (gross)
of irrigated areas
27. The Hydro-Agricultural Information System was
created to allow easy access to the Cadastre of Water
Users Database and to Manage the Information
Software developed in MapObjects and Visual Basic
Allowed the search of irrigators by first and last name,
nickname etc.
Visualize the irrigation control structures for maintenance
purposes
GIS Layers: Cadastre (property boundary), canal and drain
system, hydraulic control structures, crop and land use
layer, soil layers
Allowed for the updating of the crop agricultural statistics
Estimates the irrigation water demand through ET
calculations on a canal command area level and also by
lateral and entire project
Product installed at the Water Users Associations and
Irrigation Districts
37. Airborne Multispectral Mosaics of the
Irrigated Areas
Acquired with the USU digital airborne
multispectral system
Used in the study of salinity and drainage
problems of the irrigation areas of the country
Used to obtain the crop cover and land use for the
hydro-agricultural database system
Maps of areas with salinity impacts and drainage
problems
39. Multispectral Mosaic of the Manzanillo Area used
for Salinity Impact studies
Soil sampling sites
Results were verified with intensive soil sampling
40. Training Activities:
Creation of the Geomatics Laboratory at INDRHI
• Repository of the printed and digital products
developed though the project
• Care and protect the information in a climatized,
safe environment
• Use the products and technical staff of the laboratory
to support the different Departments of INDRHI
• Maintain and update the water users cadastre
database by supporting the water users associations
• Development of a methodology for the expansion of
the available digital cadastre for the entire country
41. Present Situation
• A modern geomatics lab with state-of-the-art
hardware and software and well-trained
technical staff serving the institution
42. Training Strategy
Combination of short and medium term training
6 Technicians and Engineers were trained in Utah:
Geographical Information Systems (GIS), Image Processing,
Computer Programming (Visual Basic, Avenue),
Development of the products: water users database and agro-
hyrdological software
Additional technicians were trained in Santo Domingo by
USU
Side-by-side training with direct participation in the
production
Responsibility for coordination with the Water Users
Associations
Responsibility for future training
43. Training of Water User Association Personnel and
INDRHI Irrigation District Personnel
Basic Windows: 4
Use of the Agro-hydrological System software: 4
Updating the Water Users Database: 4
Updating the Agricultural Statistics: 4
44. Training of Personnel from the
Soil and Water Division of
INDRHI on the use of the
Hydro-agricultural
System Software
45. Services Offered by the Geomatics
Laboratory at INDRHI
Sale of contact prints of the aerial photographs to
the public
Support to the different Departments of INDRHI
in the production of maps, geomatic products, GIS
and verification of cartografic information
Support to different institutions in the agricultural
sector of the country
Goal => Offer the products for sale over the internet,
sale of the digital products, sale of value added
products to generate funds to keep the laboratory
equipment updated and protect products
46. Final Observations
The Geomatics Laboratory at INDRHI with its
highly trained technical personnel and serving as a
repository for all digital and printed products
supports the different departments of INDRHI and
the general public and has the capacity to offer
services to other goverhment institutions.
Became a new Department at INDRHI called the
Department of Digital Cartography
47. Final Observations
The cadastre was necessary information in the
transfer of the irrigation systems from government
control the Water Users Associations
USU facilitated the transfer of 30 irrigation
systems in the Dominican Republic, setting up the
democratic structure, training the WUA staff on
operation and maintenance of the irrigation
system, administration and governance
The use of the digital cadastre of irrigators and
related database by the transferred Water User
Associations increased their O&M assessment
income by up to 70% in some cases
49. Remote Sensing Services Laboratory - RSSL
USU Cessna TU206
Remote Sensing Aircraft
USU Multispectral Digital System
equipment rack with FLIR SC640
thermal infrared camera in the
foreground.
Detail of Multispectral Cameras
53. Details on MS Camera System
– Four co-boresighted multispectral cameras
• 16 megapixels each for IR, R, G, B bands
• 64 megapixels total Camera Hardware
• Integrated with lidar
• Calibrated
Sample of Our Custom 16 mp System
(R,G,B channels shown)
54. ROW 3D Mapping System
– 3D Lidar
• Based on Riegl Q560
• 150,000 measurements/second (300 kHz laser)
• 25 mm range accuracy at any range
• 31 mm footprint @ 100 m range
•
Helicopter swath through Bonanza Power
60 degree swath angle Plant, Uintah Basin, Utah
• Integrated with cameras
Helicopter swath through Utah State University from 200 m
using Riegl Q560 lidar system
59. Evapotranspiration Analysis of
Saltcedar and Other Vegetation in the
Mojave River Floodplain, 2007 and 2010
Mojave Water Agency Water Supply Management Study
Phase 1 Report
60. Study Overview
• Analyses included:
– 2007 and 2010 classification of native
and non-native vegetation
– Vegetation evapotranspiration
modeling
– Lidar elevation map development
– Groundwater mapping
– Water evapotranspiration cost
calculations
• Results are presented as a
whole and also by Mojave
Water Agency Alto, Alto
Saltcedar (Tamarix) Transition, Centro, and Baja
subarea boundaries.
61. Multispectral
Ortho Imagery
Block 1 and 2
Ortho-rectification
using direct geo-
referencing with Lidar
point cloud data
65. SEBAL Model Bastiaansen et al (1995)
One-layer models
Uniquely solve for H using the dT method, a linear
relationship between air temperature and surface temperature
obtained through a linear transformation generated from surface
temperatures observed over selected “hot” and “cold” pixel in the
satellite image.
Advantages:
Do not require absolute calibration of the thermal imagery
Well suited for irrigated areas under well watered conditions
Provides actual evapotranspiration of the crops
Disadvantages:
Requires experienced operator to identify the “hot and cold” pixels
May not work well in water limited, semi-arid natural vegetation
66. Two-Source Model Normal et al (1995)
Kustas et al (1999)
Li et al (2005)
Advantages:
Well suited for modeling sparse canopies either in the agricultural or
natural vegetation context where water could be limited
Has a more diverse ecosystem area of application
Provides actual evapotranspiration of the vegetation
Works better with higher spatial resolution thermal infrared imagery
Disadvantages:
Requires carefully calibrated and atmospherically corrected satellite or
airborne imagery
More complex to program
67. Two-Source Energy Balance Model (TSEB)
Interpreting thermal remote sensing data
TRAD( ) ~ fc(( )Tcc + [1-fcc( )]Ts s
fc )T + [1-f( )]T
(two-source approximation)
Norman, Kustas et al. (1995)
Treats soil/plant-atmosphere
coupling differences explicitly
TAERO Accommodates off-nadir
thermal sensor view angles
Provides information on
soil/plant fluxes and stress
68. System and Component Energy Balance
TRAD
SYSTEM
RN = H + E + G Derived states
= = =
CANOPY
RNC = HC + EC
TAERO TC
+ + +
SOIL
RNS = HS + ES + G
Derived fluxes TS
69. SEBAL ET Results for Block 1
0–1 mm/day
1–2 mm/day
2–3.3 mm/day
3.4–7.1 mm/day
7.1–9 mm/day
>9 mm/day
70. Seasonal ET Estimation using ET fractions (crop coefficients)
derived from remotely sensed ET
Kc = ETa / ET0
ETa = Actual ET from
Energy Balance Model
ET0 = Reference ET from
CIMMIS Weather Station
Phenology Dates Code Greenup Begins Peak ET Senescence Begins Senescence Ends
Salt Cedar (Tamarisk) SC 3/1 5/1 9/1 11/1
Mesquite MS 4/1 5/15 8/1 9/15
Cottonwood CW 4/1 5/15 9/15 11/1
Desert Scrub DS 3/1 4/15 7/1 8/1
Decadent Vegetation VD 4/1 5/15 8/1 9/15
Mesophytes MP 4/1 5/15 7/1 8/1
Conifer CO 3/1 5/15 10/1 11/15
Arundo AR 4/1 6/1 10/1 11/1
72. Block 1 Seasonal ET results for Tamarisk using both energy
balance models
The Two-source model was selected for all estimates due to processing
speed and expediency
75. Potato Yield Model Development Using High
Resolution airborne Imagery
Different areas of interest are selected in the field for sampling based
on rate of growth and duration of the crop
76. Empirical Relationship
3 date ISAVI
SAVI
LAI
Time
Time
LAD => Yield
Yield = f(VI) 8 date ISAVI
77. Spot yield sampling location Site OI6 Field
• Locations marked by flags
• Four rows (10 *12 ft)
• GPS readings recorded
78. Spot yield sampling locations based on variability OI6 Field
Jul 30, 2004
Jul 05, 2004 Aug 31, 2004
Late Emergence Early Senescence – low yield spot
79. Spot yield sampling locations based on variability OI6 Field
Jul 05, 2004 Jul 30, 2004 Aug 31, 2004
Early Emergence Late Senescence – high yield spot
80. Spot yield sampling locations based on variability OI6 Field
Aug 31, 2004
Jul 05, 2004 Jul 30, 2004
Early Emergence Early Senescence – medium yield spot
81. Spot yield sampling locations based on variability OI1 Field
July 08,2005 August 05,2005 August 25,2005
Late Emergence Late Senescence – medium yield spot
82. Model development and Validation
2004
8.00 • model developed using 16 hand
y = 16.03x - 1.427 dug samples from two center pivots
Actual yield kg/sqm
7.00
R² = 0.810
6.00 2004 season (7/05, 7/30, 8/31 2004)
5.00
4.00 • SE of yield estimate of 0.41 kg/sq.m
3.00
0.300 0.350 0.400 0.450 0.500 • ISAVI significant parameter (p< 0.05)
ISAVI
2005
7.00
R-square = 0.89 • 18 hand dug samples from two center
Actual Yield Kg/sqm
MBE = 0.07 Kg/sqm
6.00
RMSE = 0.24 pivots 2005 season (7/08, 8/04,8/25)
5.00
• Model slightly over predicted at higher
4.00
end
3.00
3.00 4.00 5.00 6.00 7.00
Estimated Yield Kg/sqm
83. 3-Date ISAVI model to 2004 and 2005 whole field
2004 Whole field data 2004 Whole field data in MT
8.00 4500
Actual Production (MT)
R-square = 0.89 4000 R-square = 0.95
7.00 MBE = 0.16 kg/sqm 3500 MBE = 66.47 MT
Actual yield kg/sqm
RMSE = 0.29 RMSE = 144.63
6.00 3000
2500
5.00 2000
1500
4.00 1000
3.00 500
500 1000 1500 2000 2500 3000 3500 4000 4500
3.00 4.00 5.00 6.00 7.00 8.00
Estimated yield kg/sqm Estimated Production (MT)
2005 Whole field data in MT
2005 Whole field data
5500
9
Actual Production (MT)
R-square = 0.96
R-square = 0.81 4500 MBE = -60.59 MT
Actual Yield kg/sqm
MBE = -0.15 kg/sqm
8 RMSE =105.98
RMSE = 0.25 3500
7 2500
6 1500
500
5
500 2500 4500
5 6 7 8 9
Estimated Yield kg/sqm Estimated Production (MT)
• 2004 3-date ISAVI model applied to 2004 and 2005 whole field data
• Differences in yield prediction - ISAVI model less dates
• More number of images covering emergence to vine kill
84. FCC Aerial Images of HF19 field (Top) and WSA09 (Bottom) showing soil and crop growth
variations during the year 2004
July 05, 2004 July 30, 2004 August 31, 2004
July 05, 2004 July 30, 2004 August 31, 2004
85. HF 19 WSA 9 OI10
Actual yield 2607 MT Actual yield 2637 MT Actual yield 2956 MT
Estimated yield 2765 MT Estimated yield 2714 MT Estimated yield 2901 MT
Yield map showing the variability in yields for field HF19, WSA9
during 2004 and OI10 during 2005 season
90. Soil Water Balance in the Study Area
• The soil water balance model:
SWj+1 = SWj + I +P – ETc – DP
SW represents the available soil water,
j and j+1 - soil water content in the current time and a succeeding time
j+1.
I represents the irrigation amount applied
P - precipitation. ETc represents the crop evapotranspiration, and DP
is the deep percolation.
• Reference ET - 1982 Kimberly-Penman method
Contd..
91. Actual ET calculation
Canopy-Based Reflectance Method
• Kcrf linear transformation of SAVI corresponding to bare soil and
SAVI at effective full cover with the basal crop coefficient (Kcb)
values corresponding to bare soil and effective full cover (EFC).
• The resulting transformation is:
Kcrf = 1.085 x SAVI + 0.0504 (Jayanthi et al. 2007 )
• Basal crop coefficient method
ETcrop = Kc * ETref
Kc = Kcb * Ka + Ks
Ka = ln(Aw + 1)/ln(101)
Ks = (1-Kcb) [1-(tw/td)0.5] * fw
92. Incorporating Actual ET into Yield model
• Actual ET computed for all hand dug samples from 2004
season
• ISAVI values extracted for all the AOIs
• Multiple linear regression (MLR)
• ISAVI and ET independent variable
• Yield dependent variable
• Significance of variables analyzed using ANOVA
• MLR model applied to 2004 and 2005 satellite images
and tested
93. Yield model with Actual ET and ISAVI
10.00
9.00
LEES
8.00
7.00 EEES
6.00 EELS
5.00 LELS
ET (mm)
4.00
3.00
2.00
1.00
0.00
75 100 125 150 175 200 225 250 275 300
DOY
8.00
Actual Yield Kg/sqm
7.00
R² = 0.87
6.00
5.00 Y = 5.56 * ISAVI + 0.0359 * ET – 21.397
4.00
3.00 R2 = 0.88
2.00
620 640 660 680 700 720
ET (mm)
94. 3 date ISAVI model to Satellite images
4000
R-square = 0.92
Actual Production (MT)
3500 MBE = 90.51 MT
RMSE = 203.74
3000 June 30, Aug
2500 01, Sep11, 2004
2000
1500
1500 2000 2500 3000 3500 4000
Estimated Production (MT)
5000
R-square = 0.61
Actual Production (MT)
4500 MBE = -150.33 MT
RMSE = 247.09
4000
July 03, Aug 04, Sep05 3500
2005 3000
2500
2000
2000 2500 3000 3500 4000 4500 5000
Estimated Production (MT)
96. FCC images of Airborne images (Top) comparing with LandSat TM5 (Bottom)
for WC4 field showing soil and crop growth variability during 2004 season
July 05, 2004 July 30, 2004 August 31, 2004
June30, 2004 August 01, 2004 September 11, 2004
97. Actual yield 2651 MT Actual yield 2651 MT
Estimated yield 2839 MT Estimated yield 2763 MT
Yield map showing the variability in yields for field WC4 using airborne
images(Left) and Satellite images (Right) during 2004 season
98. Water Balance of an Irrigated Area:
Palo Verde Irrigation District, CA
98
Contribution from Saleh Taghvaeian
99. Palo Verde Irrigation District (PVID)
• Location: imperial and Riverside counties, CA.
• Area: more than 500 km2.
• Elevation: 67 m at South to 88 m at North.
• 400 km of irrigation canals and 230 km of drains.
• Predominant crops: alfalfa, cotton, grains, melons.
• Alluvial soil with predominant sandy loam texture.
99
101. Water balance of irrigation schemes
I + P = ET + DP + RO + ΔS
I: Applied irrigation water;
P: Precipitation;
ET: Evapotranspiration;
DP: Deep percolation;
RO: Surface runoff; and,
ΔS: Change in soil water storage.
101
106. Remote Sensing of Energy Balance
Rn = H + G + LE
Rn: Net Radiation
H: Sensible Heat Flux
G: Soil Heat Flux
LE: Latent Heat Flux
Surface Energy Balance Algorithm for Land (SEBAL)
Developed by Dr. Wim Bastiaanssen, Wageningen, The Netherlands
106
117. Final Observations
Geo-spatial technologies have facilitated the design
and management of large irrigation systems
Estimating spatial ET from remote sensing is
becoming a common approach for irrigation water
management and basin water balance studies
