Detection of Chlorophyll fluorescence at crop canopies level: Remote Sensing of Photosynthesis

Transcription

1 Detection of Chlorophyll fluorescence at crop canopies level: Remote Sensing of Photosynthesis Part II : Techniques that asses passive fluorescence emission from plants qualitatively and quantitatively Oded Liran 1

2 Spectroscopy is the science which researches the interaction between electromagnetic radiation and matter Emitted light Incoming light Transmitted light Reflected light 2

3 Reflectance spectroscopy compares between light reflected from an object with the same light reflected from a reference target Downwelling Irradiance Upwelling radiance (Novoa S, 2015) 2

4 Different objects obtain unique spectral signature which contains superimposed information regarding the physical properties as well as chemical composition and interactions on molecular level C 1 : Atmospheric corrections C 2 : Crop geometry (Malinowski M, 2015) Humboldt State University (Conforti M, 2015) 4

5 Fluorescence Fluorescence emission can be extracted both qualitatively and quantitatively from the vegetation reflectance spectrum Pigments absorption peak Water absorption bands Humboldt State University 5

6 Fluorescence emission reports on the carbon fixation step and therefore it is a proxy to gross primary production (Roth MS, 2014) 6

7 Technique 1: Isolation of the fluorescence effect from the apparent reflectance profile Fluorescence hν PSII Excitation Light is blocked Wavelength (µm) 7

8 Technique 1: Apparent reflectance profile show great promise at leaf level, with high correlation when compared to active fluorescence measurement at leaf level (Zarco-Tejada, 2000a) (Zarco-Tejada, 2000b) 8

9 Technique 1: Apparent reflectance profile show great promise at leaf level, with low correlation when compared to active fluorescence measurement at canopy level Canopy Level Leaf Level (Zarco-Tejada, 2000a) 9

10 Technique 2: Isolation of the fluorescence effect from the red edge double peak derivation Red Edge (Gale J, 2017) The wavelengths magnitude of the photon energy hitting the plant is of the same order as the leaf physical properties cell size, organelles, air pockets, lignin, proteins etc. (Gates 1965, Curran PJ 1989) 10

11 Technique 2: The vegetation red edge properties are attenuated with chemical properties and phenological stage of the crop Chlorophyll Nitrogen Crop Age Content content (Gittelson (Fillela I, AA, 1994) 1996) (De Oliviera LFR 2017) 11

12 Technique 2: Isolation of the fluorescence effect on the red edge double peak derivation hν Prolonged stress conditions: Decreased humidity/ Elevated temperatures 12

13 Technique 2: Isolation of the fluorescence effect on the red edge double peak derivation (Zarco Tejada, 2003) 13

14 Technique 2: The red-edge derivation show great promise at canopy Level, but correlation is reduced with time due to many stress factors overlapping each other Canopy Canopy level level geometry during stress change 14

15 Technique 3: The Fraunhoffer Lines Discrimination Technique uses atmospheric properties in order to extract the fluorescence emission from the reflectance profile Fondriest.com Booktwo.org 15

16 Technique 3: The fluorescence emission light up the dark O 2 -A absorption line of the atmospheric oxygen (Liu, X 2015) 16

17 Technique 3: The differences in depths between the downwelling irradiance and the upwelling radiance in the O 2 A absorption well, establish the fluorescence signal value in quantitative light flux units. (Alonso A, 2008) 17

18 Technique 3: The emitted fluorescence depends on the physiological state of the photosynthetic apparatus at leaf level (Wyber R, 2017) 18

19 Technique 3: GOME-2 satellite s spectroradiometer records the SIF signal at a very low spatial resolution (Guanter L, 2015) 19

20 Remaining Challenges in terms of: temporal scale Fluorescence is re-absorbed by excess of chlorophyll pigments (Gitelson AA, 1998) 20

21 Remaining Challenges in terms of: temporal scale Chlorophyll pigment concentrations are seasonally attenuated, therefore correction factors are required per species involved (Gond V,1999) 21

22 Remaining Challenges in terms of: Spatial scale Fluoresce more Red wavelengths (Malenovsky Z, 2009) Flouresce more Far-Red Wavelengths 22

23 Remaining Challenges in terms of: Spatial scale Radiative Transfer Model software Corn Field Sorghum Field AgroDrone Mid-west laboratories (Verrelst J, 2016) 23

24 Remaining Challenges in terms of: Photosynthesis machinery state 1. Difference between PSII and PSI fluorescence 2. Alternative electron transport pathways that decouple GPP from SIF 3. Down regulation of PSII at temperatures change 4. Energy transfer between photosystems decouple PAR from SIF 5. Light Harvesting Complexes transfer between Photosystems 24

25 Summary of the second part of the lecture: Remote Sensing of photosynthesis tries to relate SIF signals to Gross Primary Production rates which tells a lot about the driver of the global carbon cycle There are multiple techniques to determine the chl fluorescence both qualitatively (Optical Indices) and quantitatively (Sun Induced fluorescence) Care should be taken when analyzing the signal scale-wise: Temporal, Spatial and mechanistic state of the photosynthetic apparatus Eventually, the SIF signal is a robust a index that predicts GPP on a global scale and a very low spatial resolutions (Large areas) 25

26 Lectures Summary: 1. The global Carbon cycle affects the greenhouse effect, ocean acidification and the global eco-physiology of all life on earth. 2. Therefore, it is of extreme importance to be able to track it on multiple spatial scales. 3. Sun Induced Fluorescence (SIF) shares a new hope among the Remote Sensing community as a powerful index that is relatively easy to record and reports on gross primary production. 4. However, care should be exercised with the analysis of the SIF signal, as it is attenuated by a lot of optical and physiological variables. Thank you for your attention 26

27 References: 1. Conforti, M., Castrignanò, A., Robustelli, G., Scarciglia, F., Stelluti, M., & Buttafuoco, G. (2015). Laboratory-based Vis NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. Catena, 124, Malinowski, R., Groom, G., Schwanghart, W., & Heckrath, G. (2015). Detection and delineation of localized flooding from WorldView-2 multispectral data. Remote Sensing, 7(11), Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., & Noland, T. L. (2000). Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation. Remote Sensing of Environment, 74(3), Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., & Sampson, P. H. (2000). Chlorophyll fluorescence effects on vegetation apparent reflectance: II. Laboratory and airborne canopy-level measurements with hyperspectral data. Remote Sensing of Environment, 74(3), Gale, J., & Wandel, A. (2017). The potential of planets orbiting red dwarf stars to support oxygenic photosynthesis and complex life. International Journal of Astrobiology, 16(1), Gates, D. M., Keegan, H. J., Schleter, J. C., & Weidner, V. R. (1965). Spectral properties of plants. Applied optics, 4(1), Curran, P. J. (1989). Remote sensing of foliar chemistry. Remote sensing of environment, 30(3), Oliveira, L. F. R. D., Oliveira, M. L. R. D., Gomes, F. S., & Santana, R. C. (2017). Estimating foliar nitrogen in Eucalyptus using vegetation indexes. Scientia Agricola, 74(2), Filella, I., & Penuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7), Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment, 58(3), Zarco-Tejada, P. J., Rueda, C. A., & Ustin, S. L. (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1),

28 References: Liu, X., Liu, L., Zhang, S., & Zhou, X. (2015). New spectral fitting method for full-spectrum solar-induced chlorophyll fluorescence retrieval based on principal components analysis. Remote Sensing, 7(8), Wyber, R., Malenovský, Z., Ashcroft, M. B., Osmond, B., & Robinson, S. A. (2017). Do daily and seasonal trends in leaf solar induced fluorescence reflect changes in photosynthesis, growth or light exposure?. Remote Sensing, 9(6), Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,... & Moran, M. S. (2014). Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences, Gitelson, A. A., Buschmann, C., & Lichtenthaler, H. K. (1998). Leaf chlorophyll fluorescence corrected for reabsorption by means of absorption and reflectance measurements. Journal of Plant Physiology, 152(2-3), Gond, V., de Pury, D. G., Veroustraete, F., & Ceulemans, R. (1999). Seasonal variations in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fapar and carbon balance in a multilayer, multispecies temperate forest. Tree physiology, 19(10), Malenovský, Z., Mishra, K. B., Zemek, F., Rascher, U., & Nedbal, L. (2009). Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. Journal of Experimental Botany, 60(11), Verrelst, J., Sabater, N., Rivera, J. P., Muñoz-Marí, J., Vicent, J., Camps-Valls, G., & Moreno, J. (2016). Emulation of leaf, canopy and atmosphere radiative transfer models for fast global sensitivity analysis. Remote Sensing, 8(8), Novoa, S., Wernand, M., & van der Woerd, H. J. (2015). WACODI: A generic algorithm to derive the intrinsic color of natural waters from digital images. Limnology and Oceanography: Methods, 13(12), Roth, M. S. (2014). The engine of the reef: photobiology of the coral algal symbiosis. Frontiers in microbiology, 5,