Beyond Diagnostics: Insights and Recommendations from Remote Sensing
CIMMYT – México, December 2013

Stress detection usin...
Institute for Sustainable
Agriculture (IAS)
National Research Council,
Spain (CSIC)

 40 tenured researchers

 250 staff...
Crop stress indicators from RS
T
• Transpiration and CO2
absorption reduction
• Photosynthesis
reduction

Under water str...
BOREAS – NASA
Project
Canadian contribution –
Airborne Hyperspectral
Imager

CASI hyperspectral imager –
228 spectral band...
AVIRIS NASA-JPL hyperspectral sensor 224 contiguous spectral channels
MIVIS / AHS / Daedalus – INTA
 INTA (Spain)
 DLR (Germany)
 NERC (UK)
Are these platforms
/ sensors “useful”
for our application ?
Questions
Are these platforms
and multi-million
sensors operational
for our purposes ?
Can we use less
expensive
approache...
Objectives
1. Identify pre-visual indicators of stress related to
physiological status (i.e. not only structure)
2. Evalua...
Outline
1. Introduction
 IAS-CSIC & QuantaLab
 Stress indicators

2. Objectives

3. Methods
 Cameras
 Platforms
 Stud...
Cameras for vegetation monitoring
 RGB / CIR cameras
 pNDVI & DSM generation

 Thermal Cameras
 Water stress detection...
12

7 January 2014
Cameras for vegetation monitoring
 RGB / CIR cameras
 pNDVI & DSM generation

 Thermal Cameras
 Water stress detection...
200 ha flight at 40 cm resolution
Cameras for vegetation monitoring
 RGB / CIR cameras
 pNDVI & DSM generation

 Thermal Cameras
 Water stress detection...
Cameras for vegetation monitoring
 RGB / CIR cameras
 pNDVI & DSM generation

 Thermal Cameras
 Water stress detection...
Low-cost UAV platforms
(“cost-effective”)
CropSight
(1 h endurance)
Viewer
(1.5-3 h endurance)
From small fields …
… to larger areas …

4000 ha at 50 cm
resolution
Outline
1. Introduction
 IAS-CSIC & QuantaLab
 Stress indicators

2. Objectives

3. Methods
 Cameras
 Platforms
 Stud...
Are these just
“pretty” pictures ?
Hyperspectral (narrow-band) Indices

Zarco-Tejada et al. (2013)
Results
Fluorescence

Zarco-Tejada et al. (2013)

Ca+b Cx+c

PRI

Suarez et al. (2009)

Zarco-Tejada et al. (2005)
Relationships with Gs

NDVI

PRIn

PRIn is PRI normalized by strcture (RDVI) and chlorophyll (R700/R670)
Zarco-Tejada et a...
Relationships with Fluorescence

Gs

Ψx

Zarco-Tejada et al. (2012)
Chlorophyll & Carotenoid content estimation

17

15

FLIGHT
y = 0.7077x + 3.7644
R2 = 0.46*** (p<0.001)
RMSE=1.28 g/cm2

E...
Development of Fluorescence maps  water stress

Fluorescence

Ψx
Zarco-Tejada et al. (2012)
Chlorophyll & Car content maps  nutrient stress

Zarco-Tejada et al. (2013)
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
...
Application in Phenotyping studies

Chlorophyll Fluorescence

Temperature
Application in Phenotyping studies

Chlorophyll Fluorescence

Temperature
Conclusions
1. Micro-hyperspectral and thermal cameras on board UAVs
and manned aircrafts enable the generation of stress ...
The QuantaLab – IAS – CSIC Team
Manned aircraft
facility

David Notario – Flight Operations

UAV facility

Alberto Hornero...
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
CIMMYT – México, December 2013

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

  1. 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. 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. 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. 4. BOREAS – NASA Project Canadian contribution – Airborne Hyperspectral Imager CASI hyperspectral imager – 228 spectral bands @ 2 m spatial resolution
  5. 5. AVIRIS NASA-JPL hyperspectral sensor 224 contiguous spectral channels
  6. 6. MIVIS / AHS / Daedalus – INTA  INTA (Spain)  DLR (Germany)  NERC (UK)
  7. 7. Are these platforms / sensors “useful” for our application ?
  8. 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. 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. 10. Outline 1. Introduction  IAS-CSIC & QuantaLab  Stress indicators 2. Objectives 3. Methods  Cameras  Platforms  Studies 4. Results 5. Conclusions
  11. 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. 12 7 January 2014
  13. 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. 14. 200 ha flight at 40 cm resolution
  15. 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. 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. 17. Low-cost UAV platforms (“cost-effective”)
  18. 18. CropSight (1 h endurance)
  19. 19. Viewer (1.5-3 h endurance)
  20. 20. From small fields …
  21. 21. … to larger areas … 4000 ha at 50 cm resolution
  22. 22. Outline 1. Introduction  IAS-CSIC & QuantaLab  Stress indicators 2. Objectives 3. Methods  Cameras  Platforms  Studies 4. Results 5. Conclusions
  23. 23. Are these just “pretty” pictures ?
  24. 24. Hyperspectral (narrow-band) Indices Zarco-Tejada et al. (2013)
  25. 25. Results Fluorescence Zarco-Tejada et al. (2013) Ca+b Cx+c PRI Suarez et al. (2009) Zarco-Tejada et al. (2005)
  26. 26. Relationships with Gs NDVI PRIn PRIn is PRI normalized by strcture (RDVI) and chlorophyll (R700/R670) Zarco-Tejada et al. (2013)
  27. 27. Relationships with Fluorescence Gs Ψx Zarco-Tejada et al. (2012)
  28. 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. 29. Development of Fluorescence maps  water stress Fluorescence Ψx Zarco-Tejada et al. (2012)
  30. 30. Chlorophyll & Car content maps  nutrient stress Zarco-Tejada et al. (2013)
  31. 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. 32. Application in Phenotyping studies Chlorophyll Fluorescence Temperature
  33. 33. Application in Phenotyping studies Chlorophyll Fluorescence Temperature
  34. 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. 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. 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

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