stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from...

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

Pablo Zarco-Tejada (JRC IES & IAS-CSIC) http://quantalab.ias.csic.es

[email protected] / [email protected]

Stress detection using fluorescence, narrow-band spectral indices and thermal

imagery acquired from manned and unmanned aerial vehicles

40 tenured researchers

250 staff / 3 departments Agronomy Plant Protection Plant Breeding

RS Laboratory: 7-10 staff members

Institute for Sustainable Agriculture (IAS)

National Research Council, Spain (CSIC)

Crop stress indicators from RS

• Transpiration and CO2 absorption reduction

• Photosynthesis reduction

Temperature increases

Under nutrient stress conditions:

Photosynthetic pigment degradation

Under water stress:

T

BOREAS – NASA Project

Canadian contribution – Airborne Hyperspectral Imager

CASI hyperspectral imager – 228 spectral bands @ 2 m

spatial resolution

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 ?

Are these platforms and multi-million sensors operational for our purposes ?

Questions

From leaf to canopy can we “map” stress ? Scaling up ?

Can we use less expensive approaches ?

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

Objectives

1.Introduction IAS-CSIC & QuantaLab Stress indicators

2.Objectives

3.Methods Cameras Platforms Studies

4.Results

5.Conclusions

Outline

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

12April 11, 2023

200 ha flight at 40 cm resolution

Low-cost UAV platforms

(“cost-effective”)

CropSight(1 h endurance)

Viewer(1.5-3 h endurance)

From small fields …

4000 ha at 50 cm resolution

… to larger areas …

1.Introduction IAS-CSIC & QuantaLab Stress indicators

2.Objectives

3.Methods Cameras Platforms Studies

4.Results

5.Conclusions

Outline

Are these just “pretty” pictures ?

Hyperspectral (narrow-band) Indices

Zarco-Tejada et al. (2013)

Results

Zarco-Tejada et al. (2013) Suarez et al. (2009) Zarco-Tejada et al. (2005)

Fluorescence PRI Ca+b Cx+c


NDVI PRIn

Relationships with Gs

PRIn is PRI normalized by strcture (RDVI) and chlorophyll (R700/R670)


Relationships with Fluorescence

Gs Ψx

y = 1.2844x - 12.804R2 = 0.71

0

20

40

60

80

100

0 20 40 60 80 100

Cab (g/cm2) (estimated)

Ca

b (

g/cm

2)

(mea

sure

d)Chlorophyll & Carotenoid content estimation

FLIGHTy = 0.7077x + 3.7644R2 = 0.46*** (p<0.001)

RMSE=1.28 g/cm2

SAILHy = 0.9211x + 1.1824R2 = 0.4*** (p<0.001)RMSE=1.18 g/cm2

3

5

7

9

11

13

15

17

6 7 8 9 10 11 12

Measured Cx+c (g/cm2)

Est

imat

ed C

x+

c ( g

/cm

2)


Ca+b Cx+c


Development of Fluorescence maps water stress

Fluorescence Ψx


Chlorophyll & Car content maps nutrient stress

Al-

ww1

Al-d

Al-

ww2

Or-

ww1

Or-

ww2

Or-

ww3

Ap-d

Ap-ww

Le-d

Le-wwPe-ww1

Pe-ww2

Pe-d1

Pe-d1

CWSI

0.0

1.0V. Gonzalez-Dugo et al. (2013)

Map of CWSI – thermal-based indicator of stress

Application in Phenotyping studies

Chlorophyll Fluorescence Temperature

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

Conclusions

The QuantaLab – IAS – CSIC Team

Manned aircraft facility

UAV facility

Calibration Facility

Alberto Hornero – Software Engineer

Alfredo Gómez – Cartographic Engineer

David Notario – Flight Operations

Rafael Romero – Image Processing Analyst

Alberto Vera – Electronics IT

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

Pablo Zarco-Tejada (JRC IES & IAS-CSIC) http://quantalab.ias.csic.es

[email protected] / [email protected]

Stress detection using fluorescence, narrow-band spectral indices and thermal

imagery acquired from manned and unmanned aerial vehicles

stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from...

Education