A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300？m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R2 = 0.78 and RMSE = 0.39 for CCC and R2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g？m？2] achieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value < 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and Moderate Resolution Imaging Spectrometer (MODIS) LAI was performed by comparison with the fine spatial resolution LAI map. Results indicated a better accuracy in LAI estimation of MERIS (RMSE = 0.33) compared to MODIS (RMSE = 0.81) data.
References
[1]
Gobron, N.; Pinty, B.; Verstraete, M.; Govaerts, Y. The MERIS global vegetation index (MGVI): Description and preliminary application. Int. J. Remote Sens 1999, 20, 1917–1927.
[2]
Knyazikhin, Y.; Myneni, R.B.; Tian, Y.; Wang, Y.; Zhang, Y. Estimation of Vegetation Canopy Leaf Area Index and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation from Remotely Sensed Multi-Angle and Multi-Spectral Data. Proceedings of IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 11–13 March 1999; 3, pp. 1872–1874.
[3]
Dash, J.; Curran, P.J. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv. Space Res 2007, 39, 100–104.
[4]
Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens 2004, 25, 5403–5413.
[5]
Zhang, X.Y.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ 2003, 84, 471–475.
[6]
Global Terrestrial Observing System (GTOS). A Framework for Terrestrial Climate-Related Observations and the Development of Standards for the Terrestrial Essential Climate Variables: Proposed Workplan. GTOS-77; GTOS: Rome, Italy, 2010.
[7]
Boegh, E.; Soegaard, H.; Broge, N.; Hasager, C.B.; Jensen, N.O.; Schelde, K.; Thomsen, A. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sens. Environ 2002, 81, 179–193.
[8]
Gitelson, A.; Vina, A.; Verma, S.; Rundquist, D.; Arkebauer, T.; Keydan, G.; Leavitt, B.; Ciganda, V.; Burba, G.; Suyker, A. Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res.-Atmospheres 2006, 111, 1–13.
[9]
Aguirre, M.; Berruti, B.; Bezy, J.-L.; Drinkwater, M.; Heliere, F.; Klein, U.; Mavrocordatos, C.; Silvestrin, P.; Greco, B.; Benveniste, J. The ocean and medium-resolution mission for GMES operational services. ESA Bull 2007, 131, 25–30.
[10]
Gausman, H.W. Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agron. J 1977, 69, 799–802.
[11]
Dawson, T.P.; Curran, P.J. A new technique for interpolating the reflectance red-edge position. Int. J. Remote Sens 1998, 19, 2133–2139.
[12]
Delegido, J.; Verrelst, J.; Alonso, L.; Moreno, J. Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 2011, 11, 7063–7081.
[13]
Curran, P.J.; Dash, J.; Llewellyn, G.M. Indian ocean tsunami: The use of MERIS (MTCI) data to infer salt stress in coastal vegetation. Int. J. Remote Sens 2007, 28, 729–735.
[14]
Wu, C.Y.; Niu, Z.; Tang, Q.; Huang, W.J.; Rivard, B.; Feng, J.L. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agric. For. Meteorol 2009, 149, 1015–1021.
[15]
Zurita-Milla, R.; Kaiser, G.; Clevers, J.G.P.W.; Schneider, W.; Schaepman, M.E. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics. Remote Sens. Environ 2009, 113, 1874–1885.
[16]
Berberoglu, S.; Satir, O.; Atkinson, P.M. Mapping percentage tree cover from Envisat MERIS data using linear and nonlinear techniques. Int. J. Remote Sens 2009, 30, 4747–4766.
[17]
Asner, G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ 1998, 64, 234–253.
[18]
Asner, G.P.; Wessman, C.A.; Bateson, C.A.; Privette, J.L. Impact of tissue, canopy, and landscape factors on the hyperspectral reflectance variability of arid ecosystems. Remote Sens. Environ 2000, 74, 69–84.
[19]
Baret, F.; Houlès, V.; Guérif, M. Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management. J. Exp. Bot 2007, 58, 869–880.
[20]
Weiss, M.; Baret, F.; Smith, G.J.; Jonckheere, I.; Coppin, P. Review of methods for in situ leaf area index (LAI) determination part ii. Estimation of LAI, errors and sampling. Agric. For. Meteorol 2004, 121, 37–53.
Bullock, D.G.; Anderson, D.S. Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. J. Plant Nutrit 1998, 21, 741–755.
[23]
Chen, J.M.; Pavlic, G.; Brown, L.; Cihlar, J.; Leblanc, S.G.; White, H.P.; Hall, R.J.; Peddle, D.R.; King, D.J.; Trofymow, J.A.; et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens. Environ 2002, 80, 165–184.
[24]
Huang, D.; Yang, W.Z.; Tan, B.; Rautiainen, M.; Zhang, P.; Hu, J.N.; Shabanov, N.V.; Linder, S.; Knyazikhin, Y.; Myneni, R.B. The importance of measurement errors for deriving accurate reference leaf area index maps for validation of moderate-resolution satellite LAI products. IEEE Trans. Geosci. Remote Sens 2006, 44, 1866–1871.
[25]
Martinez, B.; Garcia-Haro, F.J.; Camacho-de Coca, F. Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site. Agric. For. Meteorol 2009, 149, 130–145.
[26]
Tian, Y.H.; Woodcock, C.E.; Wang, Y.J.; Privette, J.L.; Shabanov, N.V.; Zhou, L.M.; Zhang, Y.; Buermann, W.; Dong, J.R.; Veikkanen, B.; et al. Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment. Remote Sens. Environ 2002, 83, 414–430.
[27]
Baret, F.; Morissette, J.T.; Fernandes, R.A.; Champeaux, J.L.; Myneni, R.B.; Chen, J.; Plummer, S.; Weiss, M.; Bacour, C.; Garrigues, S.; et al. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: Proposition of the CEOS-BELMANIP. IEEE Trans. Geosci. Remote Sens 2006, 44, 1794–1803.
[28]
Dash, J.; Curran, P.J.; Tallis, M.J.; Llewellyn, G.M.; Taylor, G.; Snoeij, P. Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution. Int. J. Remote Sens 2010, 31, 5513–5532.
[29]
Morisette, J.T.; Baret, F.; Privette, J.L.; Myneni, R.B.; Nickeson, J.E.; Garrigues, S.; Shabanov, N.V.; Weiss, M.; Fernandes, R.A.; Leblanc, S.G.; et al. Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup. IEEE Trans. Geosci. Remote Sens 2006, 44, 1804–1817.
[30]
Breda, N.J.J. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. J. Exp. Bot 2003, 54, 2403–2417.
[31]
Jonckheere, I.; Fleck, S.; Nackaerts, K.; Muys, B.; Coppin, P.; Weiss, M.; Baret, F. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agric. For. Meteorol 2004, 121, 19–35.
[32]
Garriques, S.; Shabanov, N.V.; Swanson, K.; Morisette, J.T.; Baret, F.; Myneni, R.B. Intercomparison and sensitivity analysis of leaf area index retrievals from LAI-2000, Accupar, and digital hemispherical photography over croplands. Agric. For. Meteorol 2008, 148, 1193–1209.
[33]
Welles, I.J.; Norman, J.M. Instrument for indirect measurement of canopy architecture. Agron. J 1991, 83, 818–825.
[34]
Rich, P.M. A Manual for Analysis of Hemispherical Canopy Photography. Report LA-11733-M; Los Alamos National Laboratory: Los Alamos, NM, USA, 1989.
Sims, D.A.; Gamon, J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ 2002, 81, 337–354.
[37]
Netto, A.T.; Campostrini, E.; Oliveira, J.G.d.; Bressan-Smith, R.E. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Sci. Hort 2005, 104, 199–209.
[38]
Wang, Y.Z.; Hong, W.; Wu, C.Z.; Lin, H.; Fan, H.L.; Chen, C.; Li, J. Variation of SPAD values in uneven-aged leaves of different dominant species in Castanopsis carlessi forest in Lingshishan National Forest Park. J. For. Res 2009, 20, 362–366.
[39]
Markwell, J.; Osterman, J.C.; Mitchell, J.L. Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynth. Res 1995, 46, 467–472.
[40]
Gratani, L. A nondestructive method to determine chlorophyll content of leaves. Photosynthetica 1992, 26, 469–473.
[41]
Monje, O.A.; Bugbee, B. Inherent limitations of nondestructive chlorophyll meters: A comparison of 2 types of meters. Hortscience 1992, 27, 69–71.
[42]
Marquard, R.D.; Tipton, J.L. Relationship between extractable chlorophyll and an insitu method to estimate leaf greenness. Hortscience 1987, 22, 1327–1327.
[43]
Schaper, H.; Chacko, E.K. Relation between extractable chlorophyll and portable chlorophyll meter readings in leaves of 8 tropical and subtropical fruit-tree species. J. Plant Physiol 1991, 138, 674–677.
[44]
Castelli, F.; Contillo, R.; Miceli, F. Non-destructive determination of leaf chlorophyll content in four crop species. J. Agron. Crop Sci 1996, 177, 275–283.
[45]
Yamamoto, A.; Nakamura, T.; Adu-Gyamfi, J.J.; Saigusa, M. Relationship between chlorophyll content in leaves of sorghum and pigeonpea determined by extraction method and by chlorophyll meter (SPAD-502). J. Plant Nutrit 2002, 25, 2295–2301.
[46]
Uddling, J.; Gelang-Alfredsson, J.; Piikki, K.; Pleijel, H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res 2007, 91, 37–46.
[47]
De Michele, C.; Vuolo, F.; D’Urso, G.; Marotta, L.; Richter, K. The Irrigation Advisory Program of Campania Region: From Research to Operational Support for the Water Directive in Agriculture. Proceedings of 33rd International Symposium on Remote Sensing of Environment, Stresa, Italy, 4–8 May 2009; II, pp. 965–968.
[48]
D’Urso, G.; Belmonte, C.A. Operative approaches to determine crop water requirements from earth observation data: Methodologies and applications. AIP Conf. Proc 2006, 852, 14–25.
[49]
Leblanc, S.G.; Chen, J.M. A practical scheme for correcting multiple scattering effects on optical LAI measurements. Agric. For. Meteorol 2001, 110, 125–139.
[50]
Migdall, S.; Bach, H.; Bobert, J.; Wehrhan, M.; Mauser, W. Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield. Prec. Agr 2009, 10, 508–524.
[51]
Chen, J.M.; Black, T.A. Defining leaf area index for non-flat leaves. Plant Cell Environ 1992, 15, 421–429.
[52]
Neumann, H.H.; Den Hartog, G.; Shaw, R.H. Leaf area measurements based on hemispheric photographs and leaf-litter collection in a deciduous forest during autumn leaf-fall. Agric. For. Meteorol 1989, 45, 325–345.
[53]
Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sens. Environ 2008, 112, 2592–2604.
[54]
Richter, K.; Hank, T.B.; Vuolo, F.; Mauser, W.; D’Urso, G. Optimal exploitation of the Sentinel-2 spectral capabilities for crop leaf area index mapping. Remote Sens 2012, 4, 561–582.
[55]
Marchi, S.; Sebastiani, L.; Gucci, R.; Tognetti, R. Sink-source transition in peach leaves during shoot development. J. Am. Soc. Hort. Sci 2005, 130, 928–935.
[56]
Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Heterogeneity of CASI-Estimated Chlorophyll Content: Assessment and Comparison with Ground Truth from L’ACADIE GEOIDE Experimental Site. Presented at 23rd Canadian Symposium on Remote Sensing, Laval, QC, Canada, 20–24 August 2001.
[57]
Gandía, S.; Moreno, J.; Sagardoy, R.; Morales, F.; Verch, G. Crop Photosynthetic Pigment Composition and Calibration of an Instrument for Indirect Chlorophyll Content Determination. Proceedings of the Final Workshop for AGRISAR and EAGLE Campaigns, Noordwijk, The Netherlands, 15–16 October 2007.
[58]
Richter, R. Correction of satellite imagery over mountainous terrain. Appl. Opt 1998, 37, 4004–4015.
[59]
Bacour, C.; Baret, F.; Beal, D.; Weiss, M.; Pavageau, K. Neural network estimation of LAI, fAPAR, fcover and LAI×C(ab), from top of canopy MERIS reflectance data: Principles and validation. Remote Sens. Environ 2006, 105, 313–325.
[60]
Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.; Asner, G.; Francois, C.; Ustin, S. Prospect+SAIL models: A review of use for vegetation characterization. Remote Sens. Environ 2009, 113, S56–S66.
[61]
Verhoef, W. Light-scattering by leaf layers with application to canopy reflectance modeling—The SAIL model. Remote Sens. Environ 1984, 16, 125–141.
[62]
Myneni, R.B.; Hoffman, S.; Knyazikhin, Y.; Privette, J.L.; Glassy, J.; Tian, Y.; Wang, Y.; Song, X.; Zhang, Y.; Smith, G.R.; et al. Global products of vegetation leaf area and fraction absorbed par from year one of MODIS data. Remote Sens. Environ 2002, 83, 214–231.
[63]
Shabanov, N.V.; Huang, D.; Yang, W.Z.; Tan, B.; Knyazikhin, Y.; Myneni, R.B.; Ahl, D.E.; Gower, S.T.; Huete, A.R.; Aragao, L.E.O.C.; et al. Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests. IEEE Trans. Geosci. Remote Sens 2005, 43, 1855–1865.
[64]
Knyazikhin, Y.; Martonchik, J.V.; Myneni, R.B.; Diner, D.J.; Running, S.W. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res.-Atmospheres 1998, 103, 32257–32275.
[65]
Cohen, W.B.; Maiersperger, T.K.; Gower, S.T.; Turner, D.P. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sens. Environ 2003, 84, 561–571.
[66]
Wang, Y.J.; Woodcock, C.E.; Buermann, W.; Stenberg, P.; Voipio, P.; Smolander, H.; Hame, T.; Tian, Y.H.; Hu, J.N.; Knyazikhin, Y.; et al. Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote Sens. Environ 2004, 91, 114–127.
[67]
Huete, A.R. Soil and sun angle interactions on partial canopy spectra. Int. J. Remote Sens 1987, 8, 1307–1317.
[68]
Bacour, C.; Jacquemoud, S.; Leroy, M.; Hautecoeur, O.; Weiss, M.; Prevot, L.; Bruguier, N.; Chauki, H. Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne Polder data. Agronomie 2002, 22, 555–565.
[69]
Feret, J.B.; Francois, C.; Asner, G.P.; Gitelson, A.A.; Martin, R.E.; Bidel, L.P.R.; Ustin, S.L.; le Maire, G.; Jacquemoud, S. Prospect-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ 2008, 112, 3030–3043.
[70]
Kuusk, A. The Inversion of the Nilson-Kuusk Canopy Reflectance Model, A Test Case. Proceedings of IGARSS 91: Remote Sensing: Global Monitoring for Earth Management, Espoo, Finland, 3–6 June 1991; 1–4, pp. 1547–1550.
[71]
Kuusk, A. The Hot-Spot Effect in the Leaf Canopy. Proceedings of IGARSS 91: Remote Sensing: Global Monitoring for Earth Management, Espoo, Finland, 3–6 June 1991; 1–4, pp. 1555–1557.
[72]
Vuolo, F.; Atzberger, C.; Richter, K.; D’Urso, G.; Dash, J. Retrieval of Biophysical Vegetation Products from RapidEye Imagery. Proceedings of ISPRS TC VII Symposium “100 Years ISPRS”, Vienna, Austria, 5–7 July 2010; pp. 281–286.
[73]
Huang, J.F.; Wang, F.M.; Wang, X.Z.; Tang, Y.L.; Wang, R.C. Relationship between narrow band normalized deference vegetation index and rice agronomic variables. Commun. Soil Sci. Plant Anal 2004, 35, 2689–2708.
[74]
Gower, S.T.; Kucharik, C.J.; Norman, J.M. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens. Environ 1999, 70, 29–51.
[75]
Chapman, S.C.; Barreto, H.J. Using a chlorophyll meter to estimate specific leaf nitrogen of tropical maize during vegetative growth. Agron. J 1997, 89, 557–562.
[76]
Campbell, R.J.; Mobley, K.N.; Marini, R.P.; Pfeiffer, D.G. Growing conditions alter the relationship between SPAD-501 values and apple leaf chlorophyll. Hortscience 1990, 25, 330–331.
[77]
Sibley, J.L.; Eakes, D.J.; Gilliam, C.H.; Keever, G.J.; Dozier, W.A.; Himelrick, D.G. Foliar SPAD-502 meter values, nitrogen levels, and extractable chlorophyll for red maple selections. Hortscience 1996, 31, 468–470.
[78]
Gitelson, A.; Vina, A.; Ciganda, V.; Rundquist, D.; Arkebauer, T. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett 2005, 32, 4.
