Abstract
The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (K s) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O 2-A band (F 760), gross primary production (GPP), and thermal radiance (L t) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of K s coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (C ab) during the dry season. The use of SPM-specific K s improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R 2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F 760 (~30%), and GPP (~5%). Both models failed to represent L t; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.
| Original language | English |
|---|---|
| Article number | 112352 |
| Pages (from-to) | 1-18 |
| Number of pages | 18 |
| Journal | Remote sensing of environment |
| Volume | 257 |
| Early online date | 23 Feb 2021 |
| DOIs | |
| Publication status | Published - May 2021 |
Keywords
- Chlorophyll
- GPP
- Grassland
- Hyperspectral
- Photosynthesis
- Plant functional traits
- Radiative transfer
- SCOPE
- Senesced leaves
- Sun-induced fluorescence
- senSCOPE
- ITC-ISI-JOURNAL-ARTICLE
Access to Document
Other files and links
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver
}
In: Remote sensing of environment, Vol. 257, 112352, 05.2021, p. 1-18.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - senSCOPE
T2 - Modeling mixed canopies combining green and brown senesced leaves. Evaluation in a Mediterranean Grassland
AU - Pacheco-Labrador, J.
AU - El-Madany, T.S.
AU - van der Tol, C.
AU - Martin, M.P.
AU - Gonzalez-Cascon, R.
AU - Perez-Priego, O.
AU - Guan, J.
AU - Moreno, G.
AU - Carrara, A.
AU - Reichstein, M.
AU - Migliavacca, M.
N1 - Export Date: 26 May 2021 CODEN: RSEEA Correspondence Address: Pacheco-Labrador, J.; Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Germany; email: jpacheco@bgc-jena.mpg.de Funding details: Alexander von Humboldt-Stiftung, CGL2015-69095-R Funding details: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, INIA Funding details: Deutsches Zentrum für Luft- und Raumfahrt, DLR Funding details: Università degli Studi di Milano-Bicocca Funding details: Ministerio de Economía y Competitividad, MINECO Funding details: European Regional Development Fund, FEDER, CGL2012-34383 Funding details: Universidad de Extremadura, UEx Funding text 1: JPL, MM and MR acknowledge the EnMAP project MoReDEHESHyReS ?Modelling Responses of Dehesas with Hyperspectral Remote Sensing? (Contract No. 50EE1621, German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy). Authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein; the project SynerTGE ?Landsat-8+Sentinel-2: exploring sensor synergies for monitoring and modeling key vegetation biophysical variables in tree-grass ecosystems? (CGL2015-69095-R, MINECO/FEDER,UE); and the project FLU?PEC ?Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean ?dehesa? ecosystem? (CGL2012-34383, Spanish Ministry of Economy and Competitiveness). The authors are very thankful to the MPI-BGC Freiland Group and especially Olaf Kolle, Martin Hertel, and Ram?n L?pez-Jim?nez (CEAM) for technical assistance. We are grateful to all the colleagues from MPI-BGC, University of Extremadura, University of Milano-Bicocca, SpecLab-CSIC, INIA, and CEAM, which have collaborated in field and laboratory works. We thank Prof. St?phane Jacquemoud for helping to reconstruct the history of brown pigments. We are very grateful to Dr. Cameron Proctor for making available the optical parameters calibrated from Canadian monocots decomposing material. We thank Dr. Javier Martinez-Vega and Mar?a Pilar Echavarria Daspet (SpecLab-CSIC) for providing the SPM samples. We acknowledge the Majadas de Ti?tar city council for its support. The authors are very thankful to this manuscript's reviewers for their valuable suggestions that have improved the results and their presentation. Funding text 2: JPL, MM and MR acknowledge the EnMAP project MoReDEHESHyReS “Modelling Responses of Dehesas with Hyperspectral Remote Sensing” (Contract No. 50EE1621 , German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy ). Authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein ; the project SynerTGE “Landsat-8+Sentinel-2: exploring sensor synergies for monitoring and modeling key vegetation biophysical variables in tree-grass ecosystems” ( CGL2015-69095-R , MINECO/FEDER,UE ); and the project FLUχPEC “Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean ‘dehesa’ ecosystem” ( CGL2012-34383 , Spanish Ministry of Economy and Competitiveness ). The authors are very thankful to the MPI-BGC Freiland Group and especially Olaf Kolle, Martin Hertel, and Ramón López-Jiménez (CEAM) for technical assistance. We are grateful to all the colleagues from MPI-BGC, University of Extremadura, University of Milano-Bicocca, SpecLab-CSIC, INIA, and CEAM , which have collaborated in field and laboratory works. We thank Prof. Stéphane Jacquemoud for helping to reconstruct the history of brown pigments. We are very grateful to Dr. Cameron Proctor for making available the optical parameters calibrated from Canadian monocots decomposing material. We thank Dr. Javier Martinez-Vega and María Pilar Echavarria Daspet (SpecLab-CSIC) for providing the SPM samples. We acknowledge the Majadas de Tiétar city council for its support. The authors are very thankful to this manuscript's reviewers for their valuable suggestions that have improved the results and their presentation. References: Aber, J.D., Wessman, C.A., Peterson, D.L., Melillo, J.M., Fownes, J.H., Remote sensing of litter and soil organic matter decomposition in Forest ecosystems (1990) Remote Sensing of Biosphere Functioning, pp. 87-103. , R.J. Hobbs H.A. Mooney Springer New York New York, NY; Asner, G.P., Biophysical and biochemical sources of variability in canopy reflectance (1998) Remote Sens. Environ., 64, pp. 234-253; Asner, G.P., Wessman, C.A., Archer, S., Scale dependence of absorption of photosynthetically active radiation in terrestrial ecosystems (1998) Ecol. Appl., 8, pp. 1003-1021; Asner, G.P., Wessman, C.A., Schimel, D.S., Archer, S., Variability in leaf and litter optical properties: implications for BRDF model inversions using AVHRR, MODIS, and MISR (1998) Remote Sens. Environ., 63, pp. 243-257; Bach, H., Verhoef, W., Sensitivity studies on the effect of surface soil moisture on canopy reflectance using the radiative transfer model GeoSAIL (2003) IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), pp. 1679-1681; Bach, H., Schneider, K., Verhoef, W., Stolz, R., Mauser, W., Van Leeuwen, H., Schouten, L., Borgeaud, M., Retrieval of Geo-and Biophysical Information from Remote Sensing through Advanced Combination of a Land Surface Process Model with Inversion Techniques in the Optical and Microwave Spectral Range (2001); Bach, H., Verhoef, W., Schneider, K., Coupling remote sensing observation models and a growth model for improved retrieval of (geo)biophysical information from optical remote sensing data (2001) Europto Remote Sensing, p. 11. , SPIE; Bacour, C., Jacquemoud, S., Leroy, M., Hautecœur, O., Weiss, M., Prévot, L., Bruguier, N., Chauki, H., Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data (2002) Agronomie, 22, pp. 555-565; Badgley, G., Field, C.B., Berry, J.A., Canopy near-infrared reflectance and terrestrial photosynthesis (2017) Sci. Adv., 3; Barillot, R., Louarn, G., Escobar-Gutiérrez, A.J., Huynh, P., Combes, D., How good is the turbid medium-based approach for accounting for light partitioning in contrasted grass–legume intercropping systems? (2011) Ann. Bot., 108, pp. 1013-1024. , https://academic.oup.com/aob/article/108/6/1013/211760; Bayat, B., van der Tol, C., Verhoef, W., Integrating satellite optical and thermal infrared observations for improving daily ecosystem functioning estimations during a drought episode (2018) Remote Sens. Environ., 209, pp. 375-394; Bayat, B., van der Tol, C., Yang, P., Verhoef, W., Extending the SCOPE model to combine optical reflectance and soil moisture observations for remote sensing of ecosystem functioning under water stress conditions (2019) Remote Sens. Environ., 221, pp. 286-301; Berwaers, S., De Boeck, H.J., Nijs, I., End-of-season senescence in grassland species can be traced to leaf temperature during preceding summer drought (2019) Perspect. Plant Ecol. Evolut. System., 38, pp. 31-38; Biswal, B., Carotenoid catabolism during leaf senescence and its control by light (1995) J. Photochem. Photobiol. B Biol., 30, pp. 3-13; Bousquet, L., Lachérade, S., Jacquemoud, S., Moya, I., Leaf BRDF measurements and model for specular and diffuse components differentiation (2005) Remote Sens. Environ., 98, pp. 201-211; Braswell, B.H., Schimel, D.S., Privette, J.L., Moore Iii, B., Emery, W.J., Sulzman, E.W., Hudak, A.T., Extracting ecological and biophysical information from AVHRR optical data: an integrated algorithm based on inverse modeling (1996) J. Geophys. Res.-Atmos., 101, pp. 23335-23348; Camino, C., Gonzalez-Dugo, V., Hernandez, P., Zarco-Tejada, P.J., Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials (2019) Remote Sens. Environ., p. 111186; Catorci, A., Carotenuto, L., Gatti, R., Flowering patterns in sub-Mediterranean grasslands: a functional approach (2012) Plant Ecol. Evol., 145, pp. 165-175; Ciganda, V., Gitelson, A., Schepers, J., Vertical profile and temporal variation of chlorophyll in maize canopy: quantitative “crop vigor” Indicator by means of reflectance-based techniques (2008) Agron. J., 100, pp. 1409-1417; Cleland, E.E., Chiariello, N.R., Loarie, S.R., Mooney, H.A., Field, C.B., Diverse responses of phenology to global changes in a grassland ecosystem (2006) Proc. Natl. Acad. Sci., 103, p. 13740; Cleveland, C.C., Townsend, A.R., Taylor, P., Alvarez-Clare, S., Bustamante, M.M.C., Chuyong, G., Dobrowski, S.Z., Wieder, W.R., Relationships among net primary productivity, nutrients and climate in tropical rain forest: a pan-tropical analysis (2011) Ecol. Lett., 14, pp. 939-947; Coûteaux, M.-M., Bottner, P., Berg, B., Litter decomposition, climate and liter quality (1995) Trends Ecol. Evol., 10, pp. 63-66; Croft, H., Chen, J.M., Luo, X., Bartlett, P., Chen, B., Staebler, R.M., Leaf chlorophyll content as a proxy for leaf photosynthetic capacity (2017) Glob. Chang. Biol., 23, pp. 3513-3524; Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland (2008) Remote Sens. Environ., 112, pp. 2592-2604; Daughtry, C.S.T., Serbin, G., Reeves, J.B., Doraiswamy, P.C., Hunt, E.R., Spectral reflectance of wheat residue during decomposition and remotely sensed estimates of residue cover (2010) Remote Sens., p. 2; Drusch, M., Moreno, J., Bello, U.D., Franco, R., Goulas, Y., Huth, A., Kraft, S., Verhoef, W., The FLuorescence EXplorer Mission concept-ESA's earth Explorer 8 (2017) IEEE Trans. Geosci. Remote Sens., 55, pp. 1273-1284; Dubeux, J.C.B., Sollenberger, L.E., Chapter 4 - Nutrient cycling in grazed pastures (2020) Management Strategies for Sustainable Cattle Production in Southern Pastures, pp. 59-75. , M. Rouquette G.E. Aiken Academic Press; Dutta, D., Schimel, D.S., Sun, Y., van der Tol, C., Frankenberg, C., Optimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxes (2019) Biogeosciences, 16, pp. 77-103; El-Madany, T.S., Reichstein, M., Perez-Priego, O., Carrara, A., Moreno, G., Pilar Martín, M., Pacheco-Labrador, J., Migliavacca, M., Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem (2018) Agric. For. Meteorol., 262, pp. 258-278; Feng, X., Dietze, M., Scale dependence in the effects of leaf ecophysiological traits on photosynthesis: Bayesian parameterization of photosynthesis models (2013) New Phytol., 200, pp. 1132-1144; Féret, J.B., Apport de la modélisation pour l'estimation de la teneur en pigments foliaires par télédétection (2009), p. 214. , Université Pierre et Marie Curie; Féret, J.B., Gitelson, A.A., Noble, S.D., Jacquemoud, S., PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle (2017) Remote Sens. Environ., 193, pp. 204-215; Féret, J.B., le Maire, G., Jay, S., Berveiller, D., Bendoula, R., Hmimina, G., Cheraiet, A., Lefèvre-Fonollosa, M.J., Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning (2019) Remote Sens. Environ., 231, p. 110959; Feret, J.-B., François, C., Asner, G.P., Gitelson, A.A., Martin, R.E., Bidel, L.P.R., Ustin, S.L., Jacquemoud, S., PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments (2008) Remote Sens. Environ., 112, pp. 3030-3043; Fernández Ales, R., Laffarga, J.M., Ortega, F., Strategies in Mediterranean grassland annuals in relation to stress and disturbance (1993) J. Veg. Sci., 4, pp. 313-322; Gamon, J.A., Peñuelas, J., Field, C.B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency (1992) Remote Sens. Environ., 41, pp. 35-44; Gartner, T.B., Cardon, Z.G., Decomposition dynamics in mixed-species leaf litter (2004) Oikos, 104, pp. 230-246. , https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0030-1299.2004.12738.x; Gastellu-Etchegorry, J.P., Martin, E., Gascon, F., DART: a 3D model for simulating satellite images and studying surface radiation budget (2004) Int. J. Remote Sens., 25, pp. 73-96; Golub, G.H., Loan, C.F.V., An analysis of the Total least squares problem (1980) SIAM J. Numer. Anal., 17, pp. 883-893; Gregersen, P.L., Culetic, A., Boschian, L., Krupinska, K., Plant senescence and crop productivity (2013) Plant Mol. Biol., 82, pp. 603-622; Grimm, V., Railsback, S.F., Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology (2012) Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci., 367, pp. 298-310; Guyot, S., Vercauteren, J., Cheynier, V., Structural determination of colourless and yellow dimers resulting from (+)-catechin coupling catalysed by grape polyphenoloxidase (1996) Phytochemistry, 42, pp. 1279-1288; He, Y., Mui, A., Scaling up semi-arid grassland biochemical content from the leaf to the canopy level: challenges and opportunities (2010) Sensors, 10, pp. 11072-11087; Homolová, L., Schaepman, M.E., Lamarque, P., Clevers, J.G.P.W., de Bello, F., Thuiller, W., Lavorel, S., Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands (2014) Ecosphere, 5; Hörtensteiner, S., Chlorophyll degradation during senescence (2006) Annu. Rev. Plant Biol., 57, pp. 55-77; Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, G., Schmuck, G., Leaf Optical Properties Experiment 93 (LOPEX93). In E.C.J.R. Centre (Ed.). Ispra (Italy) (1994), http://opticleaf.ipgp.fr/databases/LDB_lopex1993.xls, Available online: last accessed 11-Nov-2020; Houborg, R., Anderson, M., Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at regional scales (2009), p. 29. , SPIE; Houborg, R., Boegh, E., Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data (2008) Remote Sens. Environ., 112, pp. 186-202; Houborg, R., McCabe, M.F., Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems (2016) Remote Sens. Environ., 186, pp. 105-120; Houborg, R., Anderson, M., Daughtry, C., Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale (2009) Remote Sens. Environ., 113, pp. 259-274; Houborg, R., McCabe, M., Cescatti, A., Gao, F., Schull, M., Gitelson, A., Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC) (2015) Remote Sens. Environ., 159, pp. 203-221; Jacquemoud, S., Modélisation des propriétés optiques des feuilles (1988), USTL/ENSA Montpellier/INRA; Jacquemoud, S., Baret, F., PROSPECT: a model of leaf optical properties spectra (1990) Remote Sens. Environ., 34, pp. 75-91; Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G., Hosgood, B., Estimating leaf biochemistry using the PROSPECT leaf optical properties model (1996) Remote Sens. Environ., 56, pp. 194-202; Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L., PROSPECT+SAIL models: a review of use for vegetation characterization (2009) Remote Sens. Environ., 113, pp. S56-S66; Junker, L.V., Ensminger, I., Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves (2016) Tree Physiol., 36, pp. 694-711; Keeley, J.E., Fotheringham, C.J., Plot shape effects on plant species diversity measurements (2005) J. Veg. Sci., 16, pp. 249-256; Kidnie, S., Cruz, M.G., Gould, J., Nichols, D., Anderson, W., Bessell, R., Effects of curing on grassfires: I. Fuel dynamics in a senescing grassland (2015) Int. J. Wildland Fire, 24, pp. 828-837; Koukoura, Z., Mamolos, A.P., Kalburtji, K.L., Decomposition of dominant plant species litter in a semi-arid grassland (2003) Appl. Soil Ecol., 23, pp. 13-23; Letts, B., Lamb, E.G., Mischkolz, J.M., Romo, J.T., Litter accumulation drives grassland plant community composition and functional diversity via leaf traits (2015) Plant Ecol., 216, pp. 357-370; Luo, Y., El-Madany, T., Filippa, G., Ma, X., Ahrens, B., Carrara, A., Gonzalez-Cascon, R., Migliavacca, M., Using near-infrared-enabled digital repeat photography to track structural and physiological phenology in Mediterranean tree–grass ecosystems (2018) Remote Sens., 10, p. 1293; Luo, Y.Q., Randerson, J.T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Zhou, X.H., A framework for benchmarking land models (2012) Biogeosciences, 9, pp. 3857-3874; Mamolos, A.P., Veresoglou, D.S., Noitsakis, V., Gerakis, A., Differential drought tolerance of five coexisting plant species in Mediterranean lowland grasslands (2001) J. Arid Environ., 49, pp. 329-341; Martín, M.P., Pacheco-Labrador, J., González-Cascón, R., Moreno, G., Migliavacca, M., García, M., Yebra, M., Riaño, D., Estimation of essential vegetation variables in a dehesa ecosystem using reflectance factors simulated at different phenological stages (2020) Revista de Teledetección, No 55 (2020); Martini, D., Pacheco-Labrador, J., Perez-Priego, O., van der Tol, C., Madany, T.S.E., Julitta, T., Rossini, M., Migliavacca, M., Nitrogen and phosphorus effect on sun-induced fluorescence and gross primary productivity in mediterranean Grassland (2019) Remote Sens., p. 11; Matile, P., Biochemistry of Indian summer: physiology of autumnal leaf coloration (2000) Exp. Gerontol., 35, pp. 145-158; Mattila, H., Valev, D., Havurinne, V., Khorobrykh, S., Virtanen, O., Antinluoma, M., Mishra, K.B., Tyystjärvi, E., Degradation of chlorophyll and synthesis of flavonols during autumn senescence—the story told by individual leaves (2018) AoB PLANTS, 10; McKay, M.D., Beckman, R.J., Conover, W.J., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code (1979) Technometrics, 21, pp. 239-245; Melendo-Vega, J., Martín, M., Pacheco-Labrador, J., González-Cascón, R., Moreno, G., Pérez, F., Migliavacca, M., Riaño, D., Improving the performance of 3-D Radiative transfer model FLIGHT to simulate optical properties of a tree-grass ecosystem (2018) Remote Sens., 10, p. 2061; Merzlyak, M.N., Gitelson, A., Why and what for the leaves are yellow in autumn? On the interpretation of optical spectra of senescing leaves (Acerplatanoides L.) (1995) J. Plant Physiol., 145, pp. 315-320; Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., Rakitin, V.Y.U., Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening (1999) Physiol. Plant., 106, pp. 135-141; Metcalfe, D.B., Fisher, R.A., Wardle, D.A., Plant communities as drivers of soil respiration: pathways, mechanisms, and significance for global change (2011) Biogeosciences, 8, pp. 2047-2061; Migliavacca, M., Perez-Priego, O., Rossini, M., El-Madany, T.S., Moreno, G., van der Tol, C., Rascher, U., Reichstein, M., Plant functional traits and canopy structure control the relationship between photosynthetic CO2 uptake and far-red sun-induced fluorescence in a Mediterranean grassland under different nutrient availability (2017) New Phytol., 214 (3), pp. 1078-1091. , https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.14437, n/a-n/a; Nagler, P.L., Daughtry, C.S.T., Goward, S.N., Plant litter and soil reflectance (2000) Remote Sens. Environ., 71, pp. 207-215; Neumann, M., Ukonmaanaho, L., Johnson, J., Benham, S., Vesterdal, L., Novotný, R., Verstraeten, A., Hasenauer, H., Quantifying Carbon and nutrient input from Litterfall in European forests using Field observations and modeling (2018) Glob. Biogeochem. Cycles, 32, pp. 784-798; North, P.R.J., Three-dimensional forest light interaction model using a Monte Carlo method (1996) IEEE Trans. Geosci. Remote Sens., 34, pp. 946-956; Omlin, M., Reichert, P., A comparison of techniques for the estimation of model prediction uncertainty (1999) Ecol. Model., 115, pp. 45-59; Pacheco-Labrador, J., Perez-Priego, O., El-Madany, T.S., Julitta, T., Rossini, M., Guan, J., Moreno, G., Migliavacca, M., Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits (2019) Remote Sens. Environ., 234, p. 111362; Pei, G., Liu, J., Peng, B., Gao, D., Wang, C., Dai, W., Jiang, P., Bai, E., Nitrogen, lignin, C/N as important regulators of gross nitrogen release and immobilization during litter decomposition in a temperate forest ecosystem (2019) For. Ecol. Manag., 440, pp. 61-69; Perez-Priego, O., Guan, J., Rossini, M., Fava, F., Wutzler, T., Moreno, G., Carvalhais, N., Migliavacca, M., Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem (2015) Biogeosciences, 12, pp. 6351-6367; Perez-Priego, O., El-Madany, T.S., Migliavacca, M., Kowalski, A.S., Jung, M., Carrara, A., Kolle, O., Reichstein, M., Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem (2017) Agric. For. Meteorol., 236, pp. 87-99; Peters, R.D., Noble, S.D., Sensitivity and correlation analysis of PROSPECT-D and ABM-B leaf models (2020) IEEE Trans. Geosci. Remote Sens., pp. 1-10; Pourcel, L., Routaboul, J.-M., Cheynier, V., Lepiniec, L., Debeaujon, I., Flavonoid oxidation in plants: from biochemical properties to physiological functions (2007) Trends Plant Sci., 12, pp. 29-36; Price, J.N., Gazol, A., Tamme, R., Hiiesalu, I., Pärtel, M., The functional assembly of experimental grasslands in relation to fertility and resource heterogeneity (2014) Func. Ecol., 28, pp. 509-519. , https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2435.12186; Proctor, C., Lu, B., He, Y., Determining the absorption coefficients of decay pigments in decomposing monocots (2017) Remote Sens. Environ., 199, pp. 137-153; Ren, H., Zhang, B., Spatiotemporal variations in litter mass and their relationships with climate in temperate grassland: a case study from Xilingol grassland, Inner Mongolia (China) (2018) Adv. Space Res., 61, pp. 1055-1065; Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Monitoring vegetation systems in the Great Plains with ERTS (1974) Third Earth Resources Technology Satellite- 1 Symposium, pp. 301-317. , S.C. Freden E.P. Mercanti M.A. Becker NASA Greenbelt, MD, USA; Sabetta, L., Zaccarelli, N., Mancinelli, G., Mandrone, S., Salvatori, R., Costantini, M.L., Zurlini, G., Rossi, L., Mapping litter decomposition by remote-detected indicators (2006), 2006, p. 49; Schimel, D., Schneider, F.D., Carbon, J., Participants, E., Flux towers in the sky: global ecology from space (2019) New Phytol., 224, pp. 570-584; Serbin, S.P., Singh, A., Desai, A.R., Dubois, S.G., Jablonski, A.D., Kingdon, C.C., Kruger, E.L., Townsend, P.A., Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy (2015) Remote Sens. Environ., 167, pp. 78-87; Silva-Perez, V., Molero, G., Serbin, S.P., Condon, A.G., Reynolds, M.P., Furbank, R.T., Evans, J.R., Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat (2018) J. Exp. Bot., 69, pp. 483-496; 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 (2002) Remote Sens. Environ., 81, pp. 337-354; Stoler, A.B., Relyea, R.A., Reviewing the role of plant litter inputs to forested wetland ecosystems: leafing through the literature (2020) Ecol. Monogr., 90; Stuckens, J., Verstraeten, W.W., Delalieux, S., Swennen, R., Coppin, P., A dorsiventral leaf radiative transfer model: development, validation and improved model inversion techniques (2009) Remote Sens. Environ., 113, pp. 2560-2573; Taranto, F., Pasqualone, A., Mangini, G., Tripodi, P., Miazzi, M.M., Pavan, S., Montemurro, C., Polyphenol oxidases in crops: biochemical, physiological and genetic aspects (2017) Int. J. Mol. Sci., 18, p. 377; Thomas, H., Stoddart, J.L., Leaf senescence (1980) Annu. Rev. Plant Physiol., 31, pp. 83-111; Throop, H.L., Belnap, J., Connectivity Dynamics in Dryland Litter Cycles: Moving Decomposition beyond Spatial Stasis (2019) BioScience, 69, pp. 602-614. , https://academic.oup.com/bioscience/article/69/8/602/5522598; Valentinuz, O.R., Tollenaar, M., Vertical profile of leaf senescence during the grain-filling period in older and newer maize hybrids (2004) Crop Sci., 44, pp. 827-834; van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., Su, Z., An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance (2009) Biogeosciences, 6, pp. 3109-3129; van der Tol, C., Berry, J.A., Campbell, P.K.E., Rascher, U., Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence (2014) J. Geophys. Res. Biogeosci., 119, pp. 2312-2327; van der Tol, C., Rossini, M., Cogliati, S., Verhoef, W., Colombo, R., Rascher, U., Mohammed, G., A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops (2016) Remote Sens. Environ., 186, pp. 663-677; van Leeuwen, W.J.D., Huete, A.R., Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices (1996) Remote Sens. Environ., 55, pp. 123-138; Verhoef, W., Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model (1984) Remote Sens. Environ., 16, pp. 125-141; Verhoef, W., Bach, H., Remote sensing data assimilation using coupled radiative transfer models (2003) Phys. Chem. Earth Parts A/B/C, 28, pp. 3-13; Vilfan, N., van der Tol, C., Muller, O., Rascher, U., Verhoef, W., Fluspect-B: a model for leaf fluorescence, reflectance and transmittance spectra (2016) Remote Sens. Environ., 186, pp. 596-615; Vilfan, N., Van der Tol, C., Yang, P., Wyber, R., Malenovský, Z., Robinson, S.A., Verhoef, W., Extending Fluspect to simulate xanthophyll driven leaf reflectance dynamics (2018) Remote Sens. Environ., 211, pp. 345-356; Wallis, C.I.B., Homeier, J., Peña, J., Brandl, R., Farwig, N., Bendix, J., Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data (2019) Remote Sens. Environ., 225, pp. 77-92; Wang, Q., Tenhunen, J., Dinh, N.Q., Reichstein, M., Otieno, D., Granier, A., Pilegarrd, K., Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites (2005) Remote Sens. Environ., 96, pp. 475-484; Wenhan, Q., Modeling bidirectional reflectance of multicomponent vegetation canopies (1993) Remote Sens. Environ., 46, pp. 235-245; Whitfield, D.M., Rowan, K.S., Changes in the chlorophylls and carotenoids of leaves of Nicotiana tabacum during senescence (1974) Phytochemistry, 13, pp. 77-83; Wohlfahrt, G., Bahn, M., Tappeiner, U., Cernusca, A., A multi-component, multi-species model of vegetation–atmosphere CO2 and energy exchange for mountain grasslands (2001) Agric. For. Meteorol., 106, pp. 261-287; Yang, P., Verhoef, W., van der Tol, C., The mSCOPE model: a simple adaptation to the SCOPE model to describe reflectance, fluorescence and photosynthesis of vertically heterogeneous canopies (2017) Remote Sens. Environ., 201, pp. 1-11; Yang, X., Guo, X., Quantifying responses of spectral vegetation indices to dead materials in mixed grasslands (2014) Remote Sens., 6, pp. 4289-4304; Zhang, D., Hui, D., Luo, Y., Zhou, G., Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors (2008) J. Plant Ecol., 1, pp. 85-93; Zhang, Q., Middleton, E.M., Cheng, Y.-B., Huemmrich, K.F., Cook, B.D., Corp, L.A., Kustas, W.P., Yao, T., Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production (2016) Remote Sens. Environ., 186, pp. 311-321; Zhang, Y., Guanter, L., Berry Joseph, A., Joiner, J., Tol, C., Huete, A., Gitelson, A., Köhler, P., Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models (2014) Glob. Chang. Biol., 20, pp. 3727-3742; Zhang, Y., Guanter, L., Joiner, J., Song, L., Guan, K., Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data (2018) Remote Sens. Environ., 210, pp. 362-374; Zhou, S., Yu, B., Zhang, Y., Huang, Y., Wang, G., Partitioning evapotranspiration based on the concept of underlying water use efficiency (2016) Water Resour. Res., 52, pp. 1160-1175; Zurita-Milla, R., Laurent, V.C.E., van Gijsel, J.A.E., Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: a case study for Sentinel-2 (2015) Int. J. Appl. Earth Obs. Geoinf., 43, pp. 7-18
PY - 2021/5
Y1 - 2021/5
N2 - The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (K s) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O 2-A band (F 760), gross primary production (GPP), and thermal radiance (L t) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of K s coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (C ab) during the dry season. The use of SPM-specific K s improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R 2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F 760 (~30%), and GPP (~5%). Both models failed to represent L t; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.
AB - The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (K s) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O 2-A band (F 760), gross primary production (GPP), and thermal radiance (L t) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of K s coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (C ab) during the dry season. The use of SPM-specific K s improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R 2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F 760 (~30%), and GPP (~5%). Both models failed to represent L t; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.
KW - Chlorophyll
KW - GPP
KW - Grassland
KW - Hyperspectral
KW - Photosynthesis
KW - Plant functional traits
KW - Radiative transfer
KW - SCOPE
KW - Senesced leaves
KW - Sun-induced fluorescence
KW - senSCOPE
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1016/j.rse.2021.112352
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/vandertol_sen.pdf
U2 - 10.1016/j.rse.2021.112352
DO - 10.1016/j.rse.2021.112352
M3 - Article
SN - 0034-4257
VL - 257
SP - 1
EP - 18
JO - Remote sensing of environment
JF - Remote sensing of environment
M1 - 112352
ER -