1 / 16

Diego Rocha

APPLICATION OF AN AGROMETEOROLOGICAL MODEL (FAO # 33) FOR PREDICTING SUGARCANE CROPS USING THE NDVI S-10 AND DMP SPOT-VEGETATION PRODUCTS. Diego Rocha. 7 to 18 February, 2011.

bess
Download Presentation

Diego Rocha

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. APPLICATION OF AN AGROMETEOROLOGICAL MODEL (FAO # 33) FOR PREDICTING SUGARCANE CROPS USING THE NDVI S-10 AND DMP SPOT-VEGETATION PRODUCTS Diego Rocha 7 to 18 February, 2011

  2. The application of the Agrometeorological spectral model, based on Report No. 33 of FAO for Estimating the harvest productivity can help on improving the planning, monitoring and control of crops. RELEVANCE OF THE APPLICATION

  3. The aim of this application is apply the FAO # 33 agrometeorological model for estimating sugarcane crops by using the NDVI S-10, DMP SPOT Vegetation products. OBJECTIVE OF THE APPLICATION

  4. Flowchart of methodology

  5. STUDY AREA

  6. Local / Regional (in-situ) data DATA USED Sugarcane plantation in the city of Coruripe in Alagoas Sate, Brazil. The sugarcane parameters used are: BF = Factor breath (0,5 for temp. ≥ 20°C and 0,6 for temp <20°C (GOUVÊA, 2008)); APF = Agricultural Productivity Factor (2,9) (RUDDORF, 1985); Ky= yield response factor (DOORENBOS E KASSAM, 1979). Kc= culture of coefficient

  7. MATERIALS: • Remote Sensing Data: Vegetation-2, SEVERI • Satellite digital data : Spot-5, Meteosat 9 • Products : NDVI S10 and Production of Dry Matter (DMP) for South America, Land SafETo • Data acquisition : 2009 • Spatialresolution: 1Km (Spot-5) and 3-4 Km Meteosat-9 • Source: EUMETCast service installed at LAPIS (Laboratory of Analysis and Processing of Satellite Images) at http://www.lapismet.com at University of Federal of Alagoas (UFAL) and SPOT Vegetation VITO at http://free.vgt.vito.be/ Data from EUMETCast – GEONETCast – DevCoCast

  8. EQUATIONS (2) Yp =CGF*BF*APF*DMP (4) CGF = Compensation of Growth Factor; BF = Factorbreath (0,5 for temp. ≥ 20°C and 0,6 for temp <20°C); APF = AgriculturalProductivity Factor (2,9) propose by RUDORFF (1985); DMP = Productionofdry matter, on this point, the DMP Spot-Vegetation data initially processed are inserted. (5) Kc= Coeficient of Culture

  9. Agrometeorological spectral model proposed based on the report number 33 from FAO (DOORENBOS and KASSAM, 1979). Ye = Yield estimated by the model; Yp= Maximum yield potential; Ky= yield response factor (DOORENBOS e KASSAM, 1979); ETr/ETp= Relative evapotranspiration.

  10. Results

  11. Tabulation of results

  12. In blue the relationship between the first and last ten-day period analyzed corresponding to April and August, respectively, of an idea of the behavior of the sugarcane during phenological period.  And in red we have the relationship between periods of ten-days of lesser and greater productivity estimates that match the first ten days of April to the last of May respectively

  13. Estimated production during the study period in only one pixel sample.

  14. Yield estimated by the model June 2° Decade

  15. Conclusions Can use the ILWIS to perform  agricultural modeling studies using  products received by the SPOT Vegetation DevCoCast system. The study presents a good application potential, if properly implemented could help in forecasting and crop monitoring with good temporal scale.

  16. Thanks to all team for data, support and opportunity

More Related