Crops yield estimation through remote sensing

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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)

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Crops yield estimation through remote sensing

  1. 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. 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 2
  3. 3. THE MANAGEMENT SYSTEMS What do they offer ? • • • • 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. 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 4
  5. 5. FIELD DATA, IMAGE PROCESSING&YIELD MODEL IMAGE PROCESSING • • • • • All the images were corrected for: Radiometry Orthorectification Atmosphere, & -- substracting the darkest pixel value Topography –illumination alfalfa Livestock wheat wheat creek alfalfa wheat 5
  6. 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. 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. 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. 9. ESTIMATED WHEAT YIELD (Kg). VALLE DE MEXCIALI AND SRC IDENTIFIED PARCELS 4 329; PARCEL SIZE: 4, 059 < 20 Ha  ~94%
  10. 10. WHEAT YIELD (kg). North West region
  11. 11. WHEAT YIELD (kg). East region
  12. 12. WHEAT YIELD (kg). Southern region
  13. 13. COMBINING THE YIELD GRID & GIS Environment PARCELS GROUPED BY YIELD (Kg)
  14. 14. COMBINING THE YIELD GRID & GIS Environment PARCELS GROUPED BY YIELD (Kg)
  15. 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 15
  16. 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
  17. 17. 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

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