Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. Crops yield estimation
through remote sensing
VICTOR M. RODRÍGUEZ MORENO
Laboratorio Nacional de Modelaje y Sensores Remotos
//SIG y Percepción remota//
Diciembre de 2014

2. THE MANAGEMENT SYSTEMS
COLABORATION TOOLS
Highly important for decision makers
They focus on all management functions:
Planning
Organizing
Policies
Control of resources
To cover goals and objectives of the enterprise
• They have the properties to interact with their data handling, as well
as other information systems to provide administrative and
operational processes
• Its origin is the interaction between people, processes and
technology in a collaborative environment. Management systems are
working tools useful to track the interests of organizations
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3. THE MANAGEMENT SYSTEMS
What do they offer ?
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•
•
•
Colaboration
Dynamic integration of information
Administration and configuration
They adapt technologies in an integration context
The main directives involving an MS applied on agricultural policy is
that they allow the decision makers to apply their own analysys
criteria to get answers. In example, about the producers:
•
•
•
•
Who sow ?
How much of the agricultural land were sowed?
What crop was planted
What was the yield of the crop ?
3

4. FIELD DATA, IMAGE PROCESSING&YIELD MODEL
FIELD DATA
Variables highly correlated with yield
• Leaf Area Index
• PAR_up, PAR dwn
• Affectations to crop’s production system: plagues, water
deficit, diseases, etc.
• Sample yield
• Sow date
• Phenologic stage
Stratified polygons
a. Enough number of sample polygons, previously stratified by
photointerpretation, randomly distributed on the agricultural area
b. The production system of each strata were followed during the cycle
c. The yield of each strata were collected in fresh (15 days before
regional harvest) and subsequently dried
d. Each field strata were treated the same way
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5. FIELD DATA, IMAGE PROCESSING&YIELD MODEL
IMAGE PROCESSING
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•
•
•
•
All the images were corrected for:
Radiometry
Orthorectification
Atmosphere, & -- substracting the darkest pixel value
Topography –illumination
alfalfa
Livestock
wheat
wheat
creek
alfalfa
wheat
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6. FIELD DATA, IMAGE PROCESSING&YIELD MODEL
YIELD MODEL
• Using the all field data dates of PAR_up and PAR dwn  field
fAPAR  sample yield  linear regression model.
R2= 0.97
• PROBLEM: The tendency analisys was incosistent with notoriously
aberrant data on the output thematic yield image
• A second order equation was obtained:
R2= 0.89
• From both Eq., x = fAPAR data; y = yield;

7. STUDY OF CASES
WHEAT. VALLE DE MEXICALI (BAJA CALIFORNIA & SAN LUIS RIO COLORADO
• Phase I
• Classify the satellite images. Supervised classification. Each of the srata
was declared as a trainning field.  Kappa= 0.865; SE 0.041
• The class image is in terms of DAS (Days after sown date)
• image acquisiton match with highest peak in photosynthetic activity.
Physiological maturity
OEIDRUS BC
• Sown wheat: 100,000 ha; production:
527,768 t ; yield: 5.27 t / ha
RESUMEN OEIDRUS vs INIFAP
• Estimated sown wheat: 4, 196 ha
• Production: + 77, 866 t
• Yield: + 1.07 t / ha
Ha
ESTIMATED FROM IMAGE
• Surface sown: 95,804 ha
• Production: 605 634 t
• Yield: 3.56 – 6.65 t/ha-1 (mean 6.32)
ESTIMATED WHEAT SURFACE. VALLE DE MEXICALI. CYCLE
O-I 2007-2008 TOTAL: 95,804 Ha

8. STUDY OF CASES
WHEAT. VALLE DE MEXICALI (BAJA CALIFORNIA & SAN LUIS RIO COLORADO
• Phase II. GIS& RS Thematic wheat
Wheat
fAPAR index
Valle de Mexicali

9. ESTIMATED WHEAT YIELD (Kg). VALLE DE MEXCIALI
AND SRC
IDENTIFIED PARCELS 4 329;
PARCEL SIZE: 4, 059 < 20 Ha  ~94%

10. WHEAT YIELD (kg). North West region

11. WHEAT YIELD (kg). East region

12. WHEAT YIELD (kg). Southern region

13. COMBINING THE YIELD GRID & GIS Environment
PARCELS GROUPED
BY YIELD (Kg)

14. COMBINING THE YIELD GRID & GIS Environment
PARCELS GROUPED
BY YIELD (Kg)

15. WHAT DO WE GET?
• Identify and locate within the agricultural area, with a good degree of
confidence, the leading producers , ie , those who are distinguished
for being innovative and apply cutting-edge production techniques
• Identify and locate areas of opportunity to direct institutional support
programs to producers , either for the adoption of appropriate
technology package or to plan annual activities program, in order to
increase the producers income; via to promote the use of more
suitable genetic materials in accordance with soil, climate and water
availability, promoting agricultural practices, to enhance the
importance of strength the production chain, etc.
• From the authorities, they are able to follow-up if the funding
programs were applied or not
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16. ANOTHER STUDY OF CASE. MAIZE.
VALLE DE AGUASCALIENTES
•
Phase I
• Classify the satellite images. Supervised classification. Each of the srata was declared as a
trainning field.  Kappa= 0.893; SE 0.030
• The class image is in terms of DAS (Days after sow date)
• image acquisiton match with highest peak in photosynthetic activity: Floration

18. RESUMEN
PRODUCTION YIELD (Min) YIELD (Max)
UNITS
6, 790
50.0 t
78.5 t
YIELD
(Mean)
54.1 t
Thanks for your attention
rodriguez.victor@inifap.gob.mx