stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from...
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Beyond Diagnostics: Insights and Recommendations from Remote SensingCIMMYT – México, December 2013
Pablo Zarco-Tejada (JRC IES & IAS-CSIC) http://quantalab.ias.csic.es
pablo.zarco@csic.es / pablo.zarco@jrc.ec.europa.eu
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
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
200 ha flight at 40 cm resolution
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
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
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
Zarco-Tejada et al. (2013)
NDVI PRIn
Relationships with Gs
PRIn is PRI normalized by strcture (RDVI) and chlorophyll (R700/R670)
Zarco-Tejada et al. (2012)
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)
Zarco-Tejada et al. (2013)
Ca+b Cx+c
Zarco-Tejada et al. (2012)
Development of Fluorescence maps water stress
Fluorescence Ψx
Zarco-Tejada et al. (2013)
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
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
pablo.zarco@csic.es / pablo.zarco@jrc.ec.europa.eu
Stress detection using fluorescence, narrow-band spectral indices and thermal
imagery acquired from manned and unmanned aerial vehicles
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