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 attentionrodriguez.victor@inifap.gob.mx

PRODUCTION UNITS

YIELD (Min) YIELD (Max) YIELD (Mean)

6, 790 50.0 t 78.5 t 54.1 t