• This record comes from PubMed

Comparison of Reflectance Measurements Acquired with a Contact Probe and an Integration Sphere: Implications for the Spectral Properties of Vegetation at a Leaf Level

. 2016 Oct 28 ; 16 (11) : . [epub] 20161028

Language English Country Switzerland Media electronic

Document type Comparative Study, Journal Article

Laboratory spectroscopy in visible and infrared regions is an important tool for studies dealing with plant ecophysiology and early recognition of plant stress due to changing environmental conditions. Leaf optical properties are typically acquired with a spectroradiometer coupled with an integration sphere (IS) in a laboratory or with a contact probe (CP), which has the advantage of operating flexibility and the provision of repetitive in-situ reflectance measurements. Experiments comparing reflectance spectra measured with different devices and device settings are rarely reported in literature. Thus, in our study we focused on a comparison of spectra collected with two ISs on identical samples ranging from a Spectralon and coloured papers as reference standards to vegetation samples with broadleaved (Nicotiana Rustica L.) and coniferous (Picea abies L. Karst.) leaf types. First, statistical measures such as mean absolute difference, median of differences, standard deviation and paired-sample t-test were applied in order to evaluate differences between collected reflectance values. The possibility of linear transformation between spectra was also tested. Moreover, correlation between normalised differential indexes (NDI) derived for each device and all combinations of wavelengths between 450 nm and 1800 nm were assessed. Finally, relationships between laboratory measured leaf compounds (total chlorophyll, carotenoids and water content), NDI and selected spectral indices often used in remote sensing were studied. The results showed differences between spectra acquired with different devices. While differences were negligible in the case of the Spectralon and they were possible to be modelled with a linear transformation in the case of coloured papers, the spectra collected with the CP and the ISs differed significantly in the case of vegetation samples. Regarding the spectral indices calculated from the reflectance data collected with the three devices, their mean values were in the range of the corresponding standard deviations in the case of broadleaved leaf type. Larger differences in optical leaf properties of spruce needles collected with the CP and ISs are implicated from the different measurement procedure due to needle-like leaf where shoots with spatially oriented needles were measured with the CP and individual needles with the IS. The study shows that a direct comparison between the spectra collected with two devices is not advisable as spectrally dependent offsets may likely exist. We propose that the future studies shall focus on standardisation of measurement procedures so that open access spectral libraries could serve as a reliable input for modelling of optical properties on a leaf level.

See more in PubMed

Soukupova J., Rock B.N., Albrechtova J. Spectral characteristics of lignin and soluble phenolics in the near infrared—A comparative study. Int. J. Remote Sens. 2002;23:3039–3055. doi: 10.1080/01431160110104683. DOI

Malenovský Z., Albrechtová J., Lhotáková Z., Zurita-Milla R., Clevers J.G.P.W., Schaepman M.E., Cudlín P. Applicability of the PROSPECT model for Norway spruce needles. Int. J. Remote Sens. 2006;27:5315–5340. doi: 10.1080/01431160600762990. DOI

Kokaly R.F., Asner G.P., Ollinger S.V., Martin M.E., Wessman C.A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 2009;113:S78–S91. doi: 10.1016/j.rse.2008.10.018. DOI

Lhotáková Z., Brodský L., Kupková L., Kopačková V., Potůčková M., Mišurec J., Klement A., Kovářová M., Albrechtová J. Detection of multiple stresses in Scots pine growing at post-mining sites using visible to near-infrared spectroscopy. Environ. Sci. Process. Impacts. 2013;15:2004. PubMed

Yanez-Rausell L., Schaepman M.E., Clevers J.G.P.W., Malenovsky Z. Minimizing measurement uncertainties of coniferous needle-leaf optical properties, Part I: Methodological review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014;7:399–405. doi: 10.1109/JSTARS.2013.2272890. DOI

Medlyn B.E. Physiological basis of the light use efficiency model. Tree Physiol. 1998;18:167–176. doi: 10.1093/treephys/18.3.167. PubMed DOI

Cheng Y.-B., Middleton E.M., Hilker T., Coops N.C., Black T.A., Krishnan P. Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest. Meas. Sci. Technol. 2009;20:95107. doi: 10.1088/0957-0233/20/9/095107. DOI

Zhang Y., Guanter L., Berry J.A., Joiner J., van der Tol C., Huete A., Gitelson A., Voigt M., Köhler P. Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Glob. Change Biol. 2014;20:3727–3742. doi: 10.1111/gcb.12664. PubMed DOI

Xin Q., Gong P., Li W. Modeling photosynthesis of discontinuous plant canopies by linking the Geometric Optical Radiative Transfer model with biochemical processes. Biogeosciences. 2015;12:3447–3467. doi: 10.5194/bg-12-3447-2015. DOI

Zhao F., Guo Y., Huang Y., Reddy K.N., Lee M.A., Fletcher R.S., Thomson S.J. Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion. Int. J. Appl. Earth Obs. Geoinform. 2014;31:78–85. doi: 10.1016/j.jag.2014.03.010. DOI

Malenovský Z., Turnbull J.D., Lucieer A., Robinson S.A. Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data. New Phytol. 2015;208:608–624. doi: 10.1111/nph.13524. PubMed DOI

Martinez N.E., Sharp J.L., Kuhne W.W., Johnson T.E., Stafford C.T., Duff M.C. Assessing the use of reflectance spectroscopy in determining CsCl stress in the model species Arabidopsis thaliana. Int. J. Remote Sens. 2015;36:5887–5915. doi: 10.1080/01431161.2015.1110258. DOI

Shi T., Liu H., Wang J., Chen Y., Fei T., Wu G. Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants. Environ. Sci. Technol. 2014;48:6264–6272. doi: 10.1021/es405361n. PubMed DOI

Shi T., Liu H., Chen Y., Wang J., Wu G. Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. J. Hazard. Mater. 2016;308:243–252. doi: 10.1016/j.jhazmat.2016.01.022. PubMed DOI

Mesarch M.A., Walter-Shea E.A., Asner G.P., Middleton E.M., Chan S.S. A revised measurement methodology for conifer needles spectral optical properties. Remote Sens. Environ. 1999;68:177–192. doi: 10.1016/S0034-4257(98)00124-2. DOI

Homolová L., Lukeš P., Malenovský Z., Lhotáková Z., Kaplan V., Hanuš J. Measurement methods and variability assessment of the Norway spruce total leaf area: Implications for remote sensing. Trees. 2013;27:111–121. doi: 10.1007/s00468-012-0774-8. DOI

Lukeš P., Stenberg P., Rautiainen M., Mõttus M., Vanhatalo K.M. Optical properties of leaves and needles for boreal tree species in Europe. Remote Sens. Lett. 2013;4:667–676. doi: 10.1080/2150704X.2013.782112. DOI

Yanez-Rausell L., Malenovsky Z., Clevers J.G.P.W., Schaepman M.E. Minimizing measurement uncertainties of coniferous needle-leaf optical properties. Part II: Experimental setup and error analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014;7:406–420. doi: 10.1109/JSTARS.2013.2292817. DOI

Casa R., Castaldi F., Pascucci S., Pignatti S. Chlorophyll estimation in field crops: An assessment of handheld leaf meters and spectral reflectance measurements. J. Agric. Sci. 2015;153:876–890. doi: 10.1017/S0021859614000483. DOI

Eitel J.U.H., Gessler P.E., Smith A.M.S., Robberecht R. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For. Ecol. Manag. 2006;229:170–182. doi: 10.1016/j.foreco.2006.03.027. DOI

Kupková L., Potůčková M., Lhotáková Z., Kopačková V., Zachová K., Albrechtová J. Chlorophyll Determination in silver birch and scots pine foliage from heavy metal polluted regions using spectral reflectance data. EARSeL E-Proc. 2012;11:64–73.

Červená L., Lhotáková Z., Kupková L., Kovářová M., Albrechtová J. Models for estimating leaf pigments and relative water content in three vertical canopy levels of Norway spruce based on laboratory spectroscopy. In: Zagajewski B., Kycko M., Reuter R., editors. EARSeL 34th Symposium Proceedings, Proceedings of the 34th EARSeL Symposium 2014; Warsaw, Poland. 16–20 June 2014; Warsaw, Poland: EARSeL and University of Warsaw; 2014. pp. 6.1–6.8.

Potůčková M., Červená L., Kupková L., Lhotáková Z., Albrechtová J. Statistical comparison of spectral and biochemical measurements on an example of Norway spruce stands in the Ore Mountains, Czech Republic. Geoinform. FCE CTU. 2016;15:69–83. doi: 10.14311/gi.15.1.6. DOI

Castro-Esau K., Sanchez-Azofeifa G., Rivard B. Comparison of spectral indices obtained using multiple spectroradiometers. Remote Sens. Environ. 2006;103:276–288. doi: 10.1016/j.rse.2005.01.019. DOI

Jung A., Götze C., Cornelia G. A comparison of four spectrometers and their effect on the similarity of spectral libraries; Proceedings of the 6th EARSeL Imaging Spectroscopy SIG Workshop; Tel Aviv, Israel. 16–19 March 2009.

Kopačková V., Ben-Dor E. Normalizing reflectance from different spectrometers and protocols with an internal soil standard. Int. J. Remote Sens. 2016;37:1276–1290. doi: 10.1080/01431161.2016.1148291. DOI

Einzmann K., Ng W.-T., Immitzer M., Pinnel N., Atzberger C. Method analysis for collecting and processing in-situ hyperspectral needle reflectance data for monitoring Norway Spruce. Photogramm. Fernerkund. Geoinform. 2014;2014:423–434. doi: 10.1127/1432-8364/2014/0234. DOI

Mišurec J., Kopačková V., Lhotáková Z., Campbell P., Albrechtová J. Detection of spatio-temporal changes of norway spruce forest stands in ore mountains using landsat time series and airborne hyperspectral imagery. Remote Sens. 2016;8:92. doi: 10.3390/rs8020092. DOI

Porra R.J., Thompson W.A., Kriedemann P.E. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: Verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochim. Biophys. Acta BBA Bioenerg. 1989;975:384–394. doi: 10.1016/S0005-2728(89)80347-0. DOI

Wellburn A.R. The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. J. Plant Physiol. 1994;144:307–313. doi: 10.1016/S0176-1617(11)81192-2. DOI

ASD Inc. Integrating Sphere User Manual. Analytical Spectral Devices, Inc.; Boulder, CO, USA: 2008.

FieldSpec 4 Wide-Res Field Spectroradiometer. [(accessed 18 October 2016)]. Available online: http://www.asdi.com/products-and-services/fieldspec-spectroradiometers/fieldspec-4-wide-res.

Daughtry C.S.T., Biehl L.L., Ranson K.J. A new technique to measure the spectral properties of conifer needles. Remote Sens. Environ. 1989;27:81–91. doi: 10.1016/0034-4257(89)90039-4. DOI

Main R., Cho M.A., Mathieu R., O’Kennedy M.M., Ramoelo A., Koch S. An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J. Photogramm. Remote Sens. 2011;66:751–761. doi: 10.1016/j.isprsjprs.2011.08.001. DOI

Le Maire G., François C., Dufrêne E. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 2004;89:1–28. doi: 10.1016/j.rse.2003.09.004. DOI

Hernández-Clemente R., Navarro-Cerrillo R.M., Zarco-Tejada P.J. Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+DART simulations. Remote Sens. Environ. 2012;127:298–315. doi: 10.1016/j.rse.2012.09.014. DOI

Yi Q., Jiapaer G., Chen J., Bao A., Wang F. Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression. ISPRS J. Photogramm. Remote Sens. 2014;91:72–84. doi: 10.1016/j.isprsjprs.2014.01.004. DOI

Thenkabail P.S., Lyon J.G., Huete A., editors. Hyperspectral Remote Sensing of Vegetation. CRC Press; Boca Raton, FL, USA: 2012.

Wu C., Niu Z., Tang Q., Huang W. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agric. For. Meteorol. 2008;148:1230–1241. doi: 10.1016/j.agrformet.2008.03.005. DOI

Gitelson A.A., Zur Y., Chivkunova O.B., Merzlyak M.N. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 2002;75:272–281. doi: 10.1562/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2. PubMed DOI

Gitelson A.A., Keydan G.P., Merzlyak M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett. 2006;33:L11402. doi: 10.1029/2006GL026457. DOI

Hunt E., Rock B. Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sens. Environ. 1989;30:43–54.

Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J., Strachan I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004;90:337–352. doi: 10.1016/j.rse.2003.12.013. DOI

Gao B. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996;58:257–266. doi: 10.1016/S0034-4257(96)00067-3. DOI

ASD Inc. Technical Guide. 3rd ed. Analytical Spectral Devices, Inc.; Boulder, CO, USA: 1999.

Rautiainen M., Mõttus M., Yáñez-Rausell L., Homolová L., Malenovský Z., Schaepman M.E. A note on upscaling coniferous needle spectra to shoot spectral albedo. Remote Sens. Environ. 2012;117:469–474. doi: 10.1016/j.rse.2011.10.019. DOI

Kindel B.C., Qu Z., Goetz A.F.H. Direct solar spectral irradiance and transmittance measurements from 350 to 2500 nm. Appl. Opt. 2001;40:3483. doi: 10.1364/AO.40.003483. PubMed DOI

Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964;36:1627–1639. doi: 10.1021/ac60214a047. DOI

Steinier J., Termonia Y., Deltour J. Smoothing and differentiation of data by simplified least square procedure. Anal. Chem. 1972;44:1906–1909. doi: 10.1021/ac60319a045. PubMed DOI

Schneider F.D., Leiterer R., Morsdorf F., Gastellu-Etchegorry J.-P., Lauret N., Pfeifer N., Schaepman M.E. Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sens. Environ. 2014;152:235–250. doi: 10.1016/j.rse.2014.06.015. DOI

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...