The document discusses the use of remote sensing techniques, including fluorescence and thermal imagery, to detect crop stress related to physiological conditions. It outlines the objectives, methods, and results of research conducted with various imaging platforms, emphasizing the effectiveness of using low-cost UAVs for precision agriculture and phenotyping studies. The findings suggest that these technologies can successfully generate stress maps and relate well to physiological indicators.

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

17. 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

19. Low-cost UAV platforms
(“cost-effective”)

20. CropSight
(1 h endurance)

21. Viewer
(1.5-3 h endurance)

24. From small fields …

25. … to larger areas …
4000 ha at 50 cm
resolution

26. Outline
1. Introduction
 IAS-CSIC & QuantaLab
 Stress indicators
2. Objectives
3. Methods
 Cameras
 Platforms
 Studies
4. Results
5. Conclusions

27. Are these just
“pretty” pictures ?

28. Hyperspectral (narrow-band) Indices
Zarco-Tejada et al. (2013)

29. Results
Fluorescence
Zarco-Tejada et al. (2013)
Ca+b Cx+c
PRI
Suarez et al. (2009)
Zarco-Tejada et al. (2005)

30. Relationships with Gs
NDVI
PRIn
PRIn is PRI normalized by strcture (RDVI) and chlorophyll (R700/R670)
Zarco-Tejada et al. (2013)

31. Relationships with Fluorescence
Gs
Ψx
Zarco-Tejada et al. (2012)

32. 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)

33. Development of Fluorescence maps  water stress
Fluorescence
Ψx
Zarco-Tejada et al. (2012)

34. Chlorophyll & Car content maps  nutrient stress
Zarco-Tejada et al. (2013)

35. 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)

36. Application in Phenotyping studies
Chlorophyll Fluorescence
Temperature

37. Application in Phenotyping studies
Chlorophyll Fluorescence
Temperature

38. 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

39. 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

40. 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