crops yield estimation through remote sensing
DESCRIPTION
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)TRANSCRIPT
Crops yield estimation through remote sensing
VICTOR M. RODRÍGUEZ MORENOLaboratorio Nacional de Modelaje y Sensores Remotos
//SIG y Percepción remota//
Diciembre de 2014
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THE MANAGEMENT SYSTEMS
COLABORATION TOOLS
Highly important for decision makersThey focus on all management functions:
PlanningOrganizingPoliciesControl 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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THE MANAGEMENT SYSTEMS
What do they offer ?
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 ?
• Colaboration• Dynamic integration of information• Administration and configuration• They adapt technologies in an integration context
FIELD DATA, IMAGE PROCESSING&YIELD MODEL
FIELD DATA
Stratified polygonsa. Enough number of sample polygons, previously stratified by
photointerpretation, randomly distributed on the agricultural areab. 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 driedd. Each field strata were treated the same way
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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
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
Livestock creek
wheat
wheat
wheat
alfalfa
alfalfa
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;
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
ESTIMATED WHEAT SURFACE. VALLE DE MEXICALI. CYCLE O-I 2007-2008 TOTAL: 95,804 Ha
Ha
OEIDRUS BC• Sown wheat: 100,000 ha; production:
527,768 t ; yield: 5.27 t / ha
ESTIMATED FROM IMAGE• Surface sown: 95,804 ha• Production: 605 634 t • Yield: 3.56 – 6.65 t/ha-1 (mean 6.32)
RESUMEN OEIDRUS vs INIFAP• Estimated sown wheat: 4, 196 ha• Production: + 77, 866 t • Yield: + 1.07 t / ha
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
ESTIMATED WHEAT YIELD (Kg). VALLE DE MEXCIALI AND SRC
IDENTIFIED PARCELS 4 329; PARCEL SIZE: 4, 059 < 20 Ha ~94%
WHEAT YIELD (kg). North West region
WHEAT YIELD (kg). East region
WHEAT YIELD (kg). Southern region
COMBINING THE YIELD GRID & GIS Environment
PARCELS GROUPED BY YIELD (Kg)
COMBINING THE YIELD GRID & GIS Environment
PARCELS GROUPED BY YIELD (Kg)
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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
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
RESUMEN
Thanks for your [email protected]
PRODUCTION UNITS
YIELD (Min) YIELD (Max) YIELD (Mean)
6, 790 50.0 t 78.5 t 54.1 t