Stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from manned and unmanned aerial vehicles

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Remote sensing –Beyond images …

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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  • 1. Beyond Diagnostics: Insights and Recommendations from Remote Sensing CIMMYT – México, December 2013 Stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from manned and unmanned aerial vehicles Pablo Zarco-Tejada (JRC IES & IAS-CSIC) http://quantalab.ias.csic.es pablo.zarco@csic.es / pablo.zarco@jrc.ec.europa.eu
  • 2. Institute for Sustainable Agriculture (IAS) National Research Council, Spain (CSIC)  40 tenured researchers  250 staff / 3 departments  Agronomy  Plant Protection  Plant Breeding RS Laboratory: 7-10 staff members
  • 3. Crop stress indicators from RS T • Transpiration and CO2 absorption reduction • Photosynthesis reduction Under water stress: Temperature increases  Under nutrient stress conditions: Photosynthetic pigment degradation
  • 4. BOREAS – NASA Project Canadian contribution – Airborne Hyperspectral Imager CASI hyperspectral imager – 228 spectral bands @ 2 m spatial resolution
  • 5. AVIRIS NASA-JPL hyperspectral sensor 224 contiguous spectral channels
  • 6. MIVIS / AHS / Daedalus – INTA  INTA (Spain)  DLR (Germany)  NERC (UK)
  • 7. Are these platforms / sensors “useful” for our application ?
  • 8. Questions Are these platforms and multi-million sensors operational for our purposes ? Can we use less expensive approaches ? From leaf to canopy  can we “map” stress ? Scaling up ?
  • 9. Objectives 1. Identify pre-visual indicators of stress related to physiological status (i.e. not only structure) 2. Evaluate thermal and hyperspectral indices in the context of Precision Agriculture (and Phenotyping studies) 3. Test methods using micro-sensors on board UAVs and small manned aircraft platforms 4. Develop the facility to provide 24-hour turn-around times for flights conducted over thousands of hectares
  • 10. Outline 1. Introduction  IAS-CSIC & QuantaLab  Stress indicators 2. Objectives 3. Methods  Cameras  Platforms  Studies 4. Results 5. Conclusions
  • 11. Cameras for vegetation monitoring  RGB / CIR cameras  pNDVI & DSM generation  Thermal Cameras  Water stress detection / irrigation Multispectral cameras  Nutrient stress detection (Cab, Cx+c)  Physiological indices (PRI, F)  Canopy structure (NDVI, EVI)  Hyperspectral imagers  New indices / methods  Combined spectral indices
  • 12. 12 7 January 2014
  • 13. Cameras for vegetation monitoring  RGB / CIR cameras  pNDVI & DSM generation  Thermal Cameras  Water stress detection / irrigation Multispectral cameras  Nutrient stress detection (Cab, Cx+c)  Physiological indices (PRI, F)  Canopy structure (NDVI, EVI)  Hyperspectral imagers  New indices / methods  Combined spectral indices
  • 14. 200 ha flight at 40 cm resolution
  • 15. Cameras for vegetation monitoring  RGB / CIR cameras  pNDVI & DSM generation  Thermal Cameras  Water stress detection / irrigation Multispectral cameras  Nutrient stress detection (Cab, Cx+c)  Physiological indices (PRI, F)  Canopy structure (NDVI, EVI)  Hyperspectral imagers  New indices / methods  Combined spectral indices
  • 16. Cameras for vegetation monitoring  RGB / CIR cameras  pNDVI & DSM generation  Thermal Cameras  Water stress detection / irrigation Multispectral cameras  Nutrient stress detection (Cab, Cx+c)  Physiological indices (PRI, F)  Canopy structure (NDVI, EVI)  Hyperspectral imagers  New indices / methods  Combined spectral indices
  • 17. Low-cost UAV platforms (“cost-effective”)
  • 18. CropSight (1 h endurance)
  • 19. Viewer (1.5-3 h endurance)
  • 20. From small fields …
  • 21. … to larger areas … 4000 ha at 50 cm resolution
  • 22. Outline 1. Introduction  IAS-CSIC & QuantaLab  Stress indicators 2. Objectives 3. Methods  Cameras  Platforms  Studies 4. Results 5. Conclusions
  • 23. Are these just “pretty” pictures ?
  • 24. Hyperspectral (narrow-band) Indices Zarco-Tejada et al. (2013)
  • 25. Results Fluorescence Zarco-Tejada et al. (2013) Ca+b Cx+c PRI Suarez et al. (2009) Zarco-Tejada et al. (2005)
  • 26. Relationships with Gs NDVI PRIn PRIn is PRI normalized by strcture (RDVI) and chlorophyll (R700/R670) Zarco-Tejada et al. (2013)
  • 27. Relationships with Fluorescence Gs Ψx Zarco-Tejada et al. (2012)
  • 28. Chlorophyll & Carotenoid content estimation 17 15 FLIGHT y = 0.7077x + 3.7644 R2 = 0.46*** (p<0.001) RMSE=1.28 g/cm2 Estimated C ( g/cm2) x+c 13 11 9 SAILH y = 0.9211x + 1.1824 R2 = 0.4*** (p<0.001) RMSE=1.18 g/cm2 7 5 3 6 7 8 9 10 11 12 Measured Cx+c ( g/cm2) Ca+b Cx+c Zarco-Tejada et al. (2013)
  • 29. Development of Fluorescence maps  water stress Fluorescence Ψx Zarco-Tejada et al. (2012)
  • 30. Chlorophyll & Car content maps  nutrient stress Zarco-Tejada et al. (2013)
  • 31. Map of CWSI – thermal-based indicator of stress Alww2 Al-d AlOr- ww1 OrOr- ww3 ww2 CWSI 0.0 Ap-d ww1 Ap-ww Le-d Pe-d1 Pe-ww1 Le-ww Pe-ww2 Pe-d1 1.0 V. Gonzalez-Dugo et al. (2013)
  • 32. Application in Phenotyping studies Chlorophyll Fluorescence Temperature
  • 33. Application in Phenotyping studies Chlorophyll Fluorescence Temperature
  • 34. Conclusions 1. Micro-hyperspectral and thermal cameras on board UAVs and manned aircrafts enable the generation of stress maps 2. F, T and narrow-band indices demonstrate good relationships with physiological indicators such as Gs, x and Pn 3. F retrieval using the FLD principle from micro hyperspectral cameras is feasible from manned and UAVs 4. Low-cost remote sensing platforms and sensors can be used with success in precision farming, conservation agriculture and phenotyping studies
  • 35. The QuantaLab – IAS – CSIC Team Manned aircraft facility David Notario – Flight Operations UAV facility Alberto Hornero – Software Engineer Rafael Romero – Image Processing Analyst Calibration Facility Alfredo Gómez – Cartographic Engineer Alberto Vera – Electronics IT
  • 36. Beyond Diagnostics: Insights and Recommendations from Remote Sensing CIMMYT – México, December 2013 Stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from manned and unmanned aerial vehicles Pablo Zarco-Tejada (JRC IES & IAS-CSIC) http://quantalab.ias.csic.es pablo.zarco@csic.es / pablo.zarco@jrc.ec.europa.eu