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230 results on '"Van Der Tol, C"'

1. Spatio-Temporal Convergence of Maximum Daily Light-Use Efficiency Based on Radiation Absorption by Canopy Chlorophyll

Author

Zhang, Y, Xiao, X, Wolf, S, Wu, J, Wu, X, Gioli, B, Wohlfahrt, G, Cescatti, A, van der Tol, C, Zhou, S, Gough, CM, Gentine, P, Steinbrecher, R, and Ardö, J

Subjects
MD Multidisciplinary, Meteorology & Atmospheric Sciences
Abstract

Light-use efficiency (LUE), which quantifies the plants' efficiency in utilizing solar radiation for photosynthetic carbon fixation, is an important factor for gross primary production estimation. Here we use satellite-based solar-induced chlorophyll fluorescence as a proxy for photosynthetically active radiation absorbed by chlorophyll (APARchl) and derive an estimation of the fraction of APARchl (fPARchl) from four remotely sensed vegetation indicators. By comparing maximum LUE estimated at different scales from 127 eddy flux sites, we found that the maximum daily LUE based on PAR absorption by canopy chlorophyll (εchlmax), unlike other expressions of LUE, tends to converge across biome types. The photosynthetic seasonality in tropical forests can also be tracked by the change of fPARchl, suggesting the corresponding (εchlmax) to have less seasonal variation. This spatio-temporal convergence of LUE derived from fPARchl can be used to build simple but robust gross primary production models and to better constrain process-based models.

Published
2018

5. Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production

Author

Martini, D, Sakowska, K, Wohlfahrt, G, Pacheco-Labrador, J, van der Tol, C, Porcar-Castell, A, Magney, T, Carrara, A, Colombo, R, El-Madany, T, Gonzalez-Cascon, R, Martin, M, Julitta, T, Moreno, G, Rascher, U, Reichstein, M, Rossini, M, Migliavacca, M, Martini D., Sakowska K., Wohlfahrt G., Pacheco-Labrador J., van der Tol C., Porcar-Castell A., Magney T. S., Carrara A., Colombo R., El-Madany T. S., Gonzalez-Cascon R., Martin M. P., Julitta T., Moreno G., Rascher U., Reichstein M., Rossini M., Migliavacca M., Martini, D, Sakowska, K, Wohlfahrt, G, Pacheco-Labrador, J, van der Tol, C, Porcar-Castell, A, Magney, T, Carrara, A, Colombo, R, El-Madany, T, Gonzalez-Cascon, R, Martin, M, Julitta, T, Moreno, G, Rascher, U, Reichstein, M, Rossini, M, Migliavacca, M, Martini D., Sakowska K., Wohlfahrt G., Pacheco-Labrador J., van der Tol C., Porcar-Castell A., Magney T. S., Carrara A., Colombo R., El-Madany T. S., Gonzalez-Cascon R., Martin M. P., Julitta T., Moreno G., Rascher U., Reichstein M., Rossini M., and Migliavacca M.

Abstract

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP–SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis–fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ–SIF–GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.

Published
2022

6. Land Cover Control on the Drivers of Evaporation and Sensible Heat Fluxes: An Observation-Based Synthesis for the Netherlands

Author

Jansen, F. A. Jongen, H. J. Jacobs, C. M. J. Bosveld, F. C. Buzacott, A. J. V. Heusinkveld, B. G. Kruijt, B. van der Molen, M. Moors, E. Steeneveld, G. J. van der Tol, C. van der Velde, Y. Voortman, B. Uijlenhoet, R. Teuling, A. J. and Jansen, F. A. Jongen, H. J. Jacobs, C. M. J. Bosveld, F. C. Buzacott, A. J. V. Heusinkveld, B. G. Kruijt, B. van der Molen, M. Moors, E. Steeneveld, G. J. van der Tol, C. van der Velde, Y. Voortman, B. Uijlenhoet, R. Teuling, A. J.

Abstract

Land cover controls the land-atmosphere exchange of water and energy through the partitioning of solar energy into latent and sensible heat. Observations over all land cover types at the regional scale are required to study these turbulent flux dynamics over a landscape. Here, we aim to study how the control of daily and midday latent and sensible heat fluxes over different land cover types is distributed along three axes: energy availability, water availability and exchange efficiency. To this end, observations from 19 eddy covariance flux tower sites in the Netherlands, covering six different land cover types located within the same climatic zone, were used in a regression analysis to explain the observed dynamics and find the principle drivers. The resulting relative position of these sites along the three axes suggests that land cover partly explains the variance of daily and midday turbulent fluxes. We found that evaporation dynamics from grassland, peatland swamp and cropland sites could mostly be explained by energy availability. Forest evaporation can mainly be explained by water availability, urban evaporation by water availability and exchange efficiency, and open water evaporation can almost entirely be explained by exchange efficiency. We found that the sensible heat flux is less sensitive to land cover type. This demonstrates that the land-atmosphere interface plays an active role in the shedding of sensible heat. Our results contribute to a better understanding of the dynamics of evaporation over different land cover types and may help to optimize, and potentially simplify, models to predict evaporation.

Published
2023
Full Text
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11. Global transpiration data from sap flow measurements: The SAPFLUXNET database

Author

Ministerio de Ciencia e Innovación (España), Agència de Gestió d'Ajuts Universitaris i de Recerca, Poyatos, R [0000-0003-0521-2523], Granda, V [0000-0002-0469-1991], Flo, V [0000-0003-1908-4577], Adams, MA [0000-0002-8154-0097], Adorjan, B [0000-0002-5482-2515], Aidar, MPM [0000-0001-5728-6749], Alvarado-Barrientos, MS [0000-0003-0098-0806], Anderson-Teixeira, KJ [0000-0001-7585-712X], Arain, MA [0000-0002-1433-5173], Aranda, I [0000-0001-9086-7940], Asbjornsen, H [000-0001-8126-3328], Oishi, AC [0000-0001-5064-4080], Oliveira RS [0000-0002-6392-2526], Oren, R [0000-0002-5654-1733], Ourcival,JM [0000-0002-3557-3496], Paljakka, T [0000-0002-3674-4904], Perez-Priego, O [0000-0002-3138-3177], Peri, PL [0000-0002-5398-4408], Peters, RL [0000-0002-7441-1297], Pfautsch, S [0000-0002-4390-4195], Pockman, WT [0000-0002-3286-0457], Baxter, R [0000-0002-7504-6797], Preisler, Y [0000-0001-5861-8362], Rocha, H [0000-0002-5981-4469], Röll, A [0000-0001-9457-4459], Rosado, BHP [0000-0002-8924-8672], Rowland, L [0000-0002-0774-3216], Rubtsov, A [0000-0002-9663-4344], Sabaté, S [0000-0003-1854-0761], Salmon, Y [0000-0003-4433-4021], Salomón, RL [0000-0003-2674-1731], Sánchez-Costa, E [0000-0001-6799-9611], Beamesderfer, E [0000-0002-7513-7349], Schäfer, K [0000-0001-9452-3619], Schuldt, B [0000-0003-4738-5289], Shashkin, A [0000-0001-9925-5019], Stahl, C [0000-0001-5411-1169], Stojanović, M [0000-0003-4918-8668], Sun, G [0000-0002-0159-1370], Szatniewska, J [0000-0003-3027-9965], Tatarinov, F [0000-0002-8338-6070], Tesar, M [0000-0002-5647-8498], Thomas, FM [0000-0001-6377-719X], Berveiller, D [0000-0001-7461-6420], Tor-ngern, P [0000-0001-7363-4926], Urban, J [0000-0003-1730-947X], Van der Tol, C [0000-0002-2484-8191], Van Meerveld [0000-0002-7547-3270], Varlagin, A [0000-0002-2549-5236], Werner, C [0000-0002-7676-9057], Wieser, G [0000-0002-7575-5657], Wingate, W [0000-0003-1921-1556], Wullschleger, S [0000-0002-9869-0446], Koong, Y [0000-0002-8630-3031], Blakely, B [0000-0001-9923-0794], Zweifel, R [0000-0001-9438-0582], Steppe, K [0000-0001-6252-0704], Mencuccini, M [0000-0003-0840-1477], Martínez-Vilalta, J [0000-0002-2332-7298], Boggs, J [0000-0003-4826-1843], Bohrer, G [0000-0002-9209-9540], Bolstad, P [0000-0002-1829-8859], Bonal, D [0000-0001-9602-8603], Bracho, R [0000-0002-8015-9796], Brodeur, J [0000-0001-7118-8360], Casanoves, F [0000-0001-8765-9382], Chave, J [0000-0002-7766-1347], Chen, H [0000-0001-7619-3425], Cisneros, C [0000-0001-9661-4581], Clark, K [0000-0003-0534-9677], Cremonese, E [0000-0002-6708-8532], Dang, H [0000-0003-4428-3834], David, J [0000-0001-6994-1085], David, T [0000-0003-1473-899X], Delpierre, D [0000-0003-0906-9402], Desai, AR [0000-0002-5226-6041], Do, FC [0000-0002-2852-627X], Dohnal, M [0000-0003-1769-4750], Domec, JC [0000-0003-0478-2559], Dzikiti, S [0000-0002-1892-4143], Edgar, C [0000-0002-7026-8358], Eichstaedt, R [0000-0002-4905-7994], El-Madany, T [0000-0002-0726-7141], Elbers, J [0000-0002-0631-3505], Eller, CB [0000-0002-7795-2574], Euskirchen, E [0000-0002-0848-4295], Ewers, B [0000-0001-6647-7475], Fonti, P [0000-0002-7070-3292], Forner, A [0000-0002-7123-6403], Forrester, D [0000-0003-2732-5692], Freitas, HC [0000-0002-4861-1164], Galvagno, M [0000-0002-0827-487X], Garcia-Tejera, O [0000-0001-7726-8118], Ghimire, CP [0000-0002-3715-6311], Gimeno, TE [0000-0002-1707-9291], Granier, A [0000-0002-4174-2487], Griebel, A [0000-0002-4476-8279], Guangyu, Y [0000-0003-3242-5348], Gush, MB [0000-0003-1328-9862], Hanson, PJ [0000-0001-7293-3561], Hasselquist, N [0000-0003-2777-0163], Heinrich, I [0000-0001-5800-6999], Hernandez-Santana, V [0000-0001-9018-8622], Herrmann, V [0000-0002-4519-481X], Hölttä, T [0000-0001-7677-7156], Holwerda, F [0000-0003-4125-1765], Ayutthaya, SIN [0000-0002-5354-1527], Jochheim,H [0000-0001-8047-4553], Joly, CA [0000-0002-7945-2805], Kim, HS [0000-0002-3440-6071], Klemedtsson, L [0000-0002-1122-0717], Kropp, H [0000-0002-4258-3393], Lagergren, F [0000-0002-0061-733X], Lane, P [0000-0001-6121-8386], Lapenas, A [0000-0002-2135-3636], Lechuga, V [0000-0003-3745-587X], Lee,M [0000-0002-9601-3863], Leuschner, C [0000-0002-5689-7932], Limousin, JM [0000-0002-2734-2495], Linares, JC [0000-0001-8375-6353], Linderson, ML [0000-0001-6578-6671], Lindroth, A [0000-0002-7669-784X], Llorens, P [0000-0003-4591-5303], López-Bernal, A [0000-0002-1034-4718], Loranty, MM[0000-0001-8851-7386], Macinnis-Ng, C [0000-0003-3935-9814], Marechaux, I [0000-0002-5401-0197], Martin, TA [0000-0002-7872-4194], Matheny, A [0000-0002-9532-7131], McDowell, N [0000-0002-2178-2254], Meir, P [0000-0002-2362-0398], Mészáros, I [0000-0001-8841-730X], Migliavacca, M [0000-0003-3546-8407], Mölder, M [0000-0001-6767-3195], Montagnani, L [0000-0003-2957-9071], Moore, GW [0000-0001-5190-5983], Nakada, R [0000-0002-3704-1784], Niu, F [0000-0003-3445-4011], Nolan, R [0000-0001-9277-5142], Norby,R [0000-0002-0238-9828], Novick, K [0000-0002-8431-0879], Oberhuber, W [0000-0002-5197-7044], Obojes, N [0000-0002-6718-2756], Poyatos, R, Granda, V, Flo, V, Adams, MA, Adorján, B, Aguadé, D, Aidar, MPM, Allen, S, Alvarado-Barrientos, MS, Anderson-Teixeira, KJ, Aparecido, LM, Joly, CA, Kaplick, J, Kim, HS, Klemedtsson, L, Kropp, H, Lagergren, F, Lane, P, Lang, P, Lapenas, A, Lechuga, V, Migliavacca, M, Lee, M, Leuschner, C, Limousin, JM, Linares, JC, Linderson, ML, Lindroth, A, Llorens, P, López-Bernal, A, Loranty, MM, Lüttschwager, D, Mitchell, P, MacInnis-Ng, C, Maréchaux, I, Martin, TA, Matheny, A, McDowell, N, McMahon, S, Meir, P, Mészáros, I, Molder, M, Mölder, M, Montagnani, L, Moore, GW, Nakada, R, Niu, F, Nolan, R, Norby,R, Novick, K, Oberhuber, W, Obojes, N, Oishi, AC, Oliveira RS, Oren, R, Ourcival,JM, Paljakka, T, Perez-Priego, O, Peri, PL, Peters, RL, Pfautsch, S, Pockman, WT, Preisler, Y, Rascher, K, Robinson, G, Rocha, H, Rocheteau, A, Röll, A, Rosado, BHP, Rowland, L [, Rubtsov, A, Sabaté, S, Salmon, Y, Salomón, RL, Sánchez-Costa, E, Schäfer, K, Schuldt, B, Shashkin, A, Stahl, C, Stojanovic, M, Suárez, JC, Sun, G, Niu, FR, Szatniewska, J, Tatarinov, F, Tesar, M, Thomas, FM, Tor-ngern, P, Urban, J, Valladares, F, Van der Tol, C, Van Meerveld, Varlagin, A, Norby, R, Voigt, H, Warren, J, Werner, C, Werner, W, Wieser, G, Wingate, W, Wullschleger, S, Koong, Y, Zweifel, R, Arain, MA, Oliveira, RS, Ourcival, JM, Aranda, I, Roll, A, Asbjornsen, H, Baxter, R, Beamesderfer, E, Berry, ZC, Berveiller, D, Blakely, B, Boggs, J, Bohrer, G, Bolstad, PV, Bonal, D, Bracho, R, Brito, P, Brodeur, J, Casanoves, F, Chave, J, Chen, H, Cisneros, C, Clark, K, Cremonese, E, Dang, HZ, David, JS, David, TS, Delpierre, N, Desai, AR, Do, Frederic C., Dohnal, M, Domec, JC, Dzikiti, S, Edgar, C, Eichstaedt, R, El-Madany, TS, Elbers, J, Eller, CB, Euskirchen, ES, Ewers, B, Fonti, P, Forner, A, Forrester, DI, Freitas, HC, Galvagno, M, Garcia-Tejera, O, Ghimire, CP, Gimeno, TE, Grace, J, Granier, A, Griebel, A, Guangyu, Y, Gush, MB, Hanson, PJ, Hasselquist, NJ, Heinrich, I, Hernandez-Santana, V, Herrmann, V, Hölttä, T, Holwerda, F, Irvine, J, Ayutthaya, SIN, Jarvis, PG, Jochheim, H, Ministerio de Ciencia e Innovación (España), Agència de Gestió d'Ajuts Universitaris i de Recerca, Poyatos, R [0000-0003-0521-2523], Granda, V [0000-0002-0469-1991], Flo, V [0000-0003-1908-4577], Adams, MA [0000-0002-8154-0097], Adorjan, B [0000-0002-5482-2515], Aidar, MPM [0000-0001-5728-6749], Alvarado-Barrientos, MS [0000-0003-0098-0806], Anderson-Teixeira, KJ [0000-0001-7585-712X], Arain, MA [0000-0002-1433-5173], Aranda, I [0000-0001-9086-7940], Asbjornsen, H [000-0001-8126-3328], Oishi, AC [0000-0001-5064-4080], Oliveira RS [0000-0002-6392-2526], Oren, R [0000-0002-5654-1733], Ourcival,JM [0000-0002-3557-3496], Paljakka, T [0000-0002-3674-4904], Perez-Priego, O [0000-0002-3138-3177], Peri, PL [0000-0002-5398-4408], Peters, RL [0000-0002-7441-1297], Pfautsch, S [0000-0002-4390-4195], Pockman, WT [0000-0002-3286-0457], Baxter, R [0000-0002-7504-6797], Preisler, Y [0000-0001-5861-8362], Rocha, H [0000-0002-5981-4469], Röll, A [0000-0001-9457-4459], Rosado, BHP [0000-0002-8924-8672], Rowland, L [0000-0002-0774-3216], Rubtsov, A [0000-0002-9663-4344], Sabaté, S [0000-0003-1854-0761], Salmon, Y [0000-0003-4433-4021], Salomón, RL [0000-0003-2674-1731], Sánchez-Costa, E [0000-0001-6799-9611], Beamesderfer, E [0000-0002-7513-7349], Schäfer, K [0000-0001-9452-3619], Schuldt, B [0000-0003-4738-5289], Shashkin, A [0000-0001-9925-5019], Stahl, C [0000-0001-5411-1169], Stojanović, M [0000-0003-4918-8668], Sun, G [0000-0002-0159-1370], Szatniewska, J [0000-0003-3027-9965], Tatarinov, F [0000-0002-8338-6070], Tesar, M [0000-0002-5647-8498], Thomas, FM [0000-0001-6377-719X], Berveiller, D [0000-0001-7461-6420], Tor-ngern, P [0000-0001-7363-4926], Urban, J [0000-0003-1730-947X], Van der Tol, C [0000-0002-2484-8191], Van Meerveld [0000-0002-7547-3270], Varlagin, A [0000-0002-2549-5236], Werner, C [0000-0002-7676-9057], Wieser, G [0000-0002-7575-5657], Wingate, W [0000-0003-1921-1556], Wullschleger, S [0000-0002-9869-0446], Koong, Y [0000-0002-8630-3031], Blakely, B [0000-0001-9923-0794], Zweifel, R [0000-0001-9438-0582], Steppe, K [0000-0001-6252-0704], Mencuccini, M [0000-0003-0840-1477], Martínez-Vilalta, J [0000-0002-2332-7298], Boggs, J [0000-0003-4826-1843], Bohrer, G [0000-0002-9209-9540], Bolstad, P [0000-0002-1829-8859], Bonal, D [0000-0001-9602-8603], Bracho, R [0000-0002-8015-9796], Brodeur, J [0000-0001-7118-8360], Casanoves, F [0000-0001-8765-9382], Chave, J [0000-0002-7766-1347], Chen, H [0000-0001-7619-3425], Cisneros, C [0000-0001-9661-4581], Clark, K [0000-0003-0534-9677], Cremonese, E [0000-0002-6708-8532], Dang, H [0000-0003-4428-3834], David, J [0000-0001-6994-1085], David, T [0000-0003-1473-899X], Delpierre, D [0000-0003-0906-9402], Desai, AR [0000-0002-5226-6041], Do, FC [0000-0002-2852-627X], Dohnal, M [0000-0003-1769-4750], Domec, JC [0000-0003-0478-2559], Dzikiti, S [0000-0002-1892-4143], Edgar, C [0000-0002-7026-8358], Eichstaedt, R [0000-0002-4905-7994], El-Madany, T [0000-0002-0726-7141], Elbers, J [0000-0002-0631-3505], Eller, CB [0000-0002-7795-2574], Euskirchen, E [0000-0002-0848-4295], Ewers, B [0000-0001-6647-7475], Fonti, P [0000-0002-7070-3292], Forner, A [0000-0002-7123-6403], Forrester, D [0000-0003-2732-5692], Freitas, HC [0000-0002-4861-1164], Galvagno, M [0000-0002-0827-487X], Garcia-Tejera, O [0000-0001-7726-8118], Ghimire, CP [0000-0002-3715-6311], Gimeno, TE [0000-0002-1707-9291], Granier, A [0000-0002-4174-2487], Griebel, A [0000-0002-4476-8279], Guangyu, Y [0000-0003-3242-5348], Gush, MB [0000-0003-1328-9862], Hanson, PJ [0000-0001-7293-3561], Hasselquist, N [0000-0003-2777-0163], Heinrich, I [0000-0001-5800-6999], Hernandez-Santana, V [0000-0001-9018-8622], Herrmann, V [0000-0002-4519-481X], Hölttä, T [0000-0001-7677-7156], Holwerda, F [0000-0003-4125-1765], Ayutthaya, SIN [0000-0002-5354-1527], Jochheim,H [0000-0001-8047-4553], Joly, CA [0000-0002-7945-2805], Kim, HS [0000-0002-3440-6071], Klemedtsson, L [0000-0002-1122-0717], Kropp, H [0000-0002-4258-3393], Lagergren, F [0000-0002-0061-733X], Lane, P [0000-0001-6121-8386], Lapenas, A [0000-0002-2135-3636], Lechuga, V [0000-0003-3745-587X], Lee,M [0000-0002-9601-3863], Leuschner, C [0000-0002-5689-7932], Limousin, JM [0000-0002-2734-2495], Linares, JC [0000-0001-8375-6353], Linderson, ML [0000-0001-6578-6671], Lindroth, A [0000-0002-7669-784X], Llorens, P [0000-0003-4591-5303], López-Bernal, A [0000-0002-1034-4718], Loranty, MM[0000-0001-8851-7386], Macinnis-Ng, C [0000-0003-3935-9814], Marechaux, I [0000-0002-5401-0197], Martin, TA [0000-0002-7872-4194], Matheny, A [0000-0002-9532-7131], McDowell, N [0000-0002-2178-2254], Meir, P [0000-0002-2362-0398], Mészáros, I [0000-0001-8841-730X], Migliavacca, M [0000-0003-3546-8407], Mölder, M [0000-0001-6767-3195], Montagnani, L [0000-0003-2957-9071], Moore, GW [0000-0001-5190-5983], Nakada, R [0000-0002-3704-1784], Niu, F [0000-0003-3445-4011], Nolan, R [0000-0001-9277-5142], Norby,R [0000-0002-0238-9828], Novick, K [0000-0002-8431-0879], Oberhuber, W [0000-0002-5197-7044], Obojes, N [0000-0002-6718-2756], Poyatos, R, Granda, V, Flo, V, Adams, MA, Adorján, B, Aguadé, D, Aidar, MPM, Allen, S, Alvarado-Barrientos, MS, Anderson-Teixeira, KJ, Aparecido, LM, Joly, CA, Kaplick, J, Kim, HS, Klemedtsson, L, Kropp, H, Lagergren, F, Lane, P, Lang, P, Lapenas, A, Lechuga, V, Migliavacca, M, Lee, M, Leuschner, C, Limousin, JM, Linares, JC, Linderson, ML, Lindroth, A, Llorens, P, López-Bernal, A, Loranty, MM, Lüttschwager, D, Mitchell, P, MacInnis-Ng, C, Maréchaux, I, Martin, TA, Matheny, A, McDowell, N, McMahon, S, Meir, P, Mészáros, I, Molder, M, Mölder, M, Montagnani, L, Moore, GW, Nakada, R, Niu, F, Nolan, R, Norby,R, Novick, K, Oberhuber, W, Obojes, N, Oishi, AC, Oliveira RS, Oren, R, Ourcival,JM, Paljakka, T, Perez-Priego, O, Peri, PL, Peters, RL, Pfautsch, S, Pockman, WT, Preisler, Y, Rascher, K, Robinson, G, Rocha, H, Rocheteau, A, Röll, A, Rosado, BHP, Rowland, L [, Rubtsov, A, Sabaté, S, Salmon, Y, Salomón, RL, Sánchez-Costa, E, Schäfer, K, Schuldt, B, Shashkin, A, Stahl, C, Stojanovic, M, Suárez, JC, Sun, G, Niu, FR, Szatniewska, J, Tatarinov, F, Tesar, M, Thomas, FM, Tor-ngern, P, Urban, J, Valladares, F, Van der Tol, C, Van Meerveld, Varlagin, A, Norby, R, Voigt, H, Warren, J, Werner, C, Werner, W, Wieser, G, Wingate, W, Wullschleger, S, Koong, Y, Zweifel, R, Arain, MA, Oliveira, RS, Ourcival, JM, Aranda, I, Roll, A, Asbjornsen, H, Baxter, R, Beamesderfer, E, Berry, ZC, Berveiller, D, Blakely, B, Boggs, J, Bohrer, G, Bolstad, PV, Bonal, D, Bracho, R, Brito, P, Brodeur, J, Casanoves, F, Chave, J, Chen, H, Cisneros, C, Clark, K, Cremonese, E, Dang, HZ, David, JS, David, TS, Delpierre, N, Desai, AR, Do, Frederic C., Dohnal, M, Domec, JC, Dzikiti, S, Edgar, C, Eichstaedt, R, El-Madany, TS, Elbers, J, Eller, CB, Euskirchen, ES, Ewers, B, Fonti, P, Forner, A, Forrester, DI, Freitas, HC, Galvagno, M, Garcia-Tejera, O, Ghimire, CP, Gimeno, TE, Grace, J, Granier, A, Griebel, A, Guangyu, Y, Gush, MB, Hanson, PJ, Hasselquist, NJ, Heinrich, I, Hernandez-Santana, V, Herrmann, V, Hölttä, T, Holwerda, F, Irvine, J, Ayutthaya, SIN, Jarvis, PG, and Jochheim, H

Abstract

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely a

Published
2021

15. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Author

Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin

Abstract

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

Published
2022

17. Global transpiration data from sap flow measurements: The SAPFLUXNET database

Author

Poyatos, R, Granda, V, Flo, V, Adams, MA, Adorján, B, Aguadé, D, Aidar, MPM, Allen, S, Alvarado-Barrientos, MS, Anderson-Teixeira, KJ, Aparecido, LM, Joly, CA, Kaplick, J, Kim, HS, Klemedtsson, L, Kropp, H, Lagergren, F, Lane, P, Lang, P, Lapenas, A, Lechuga, V, Migliavacca, M, Lee, M, Leuschner, C, Limousin, JM, Linares, JC, Linderson, ML, Lindroth, A, Llorens, P, López-Bernal, A, Loranty, MM, Lüttschwager, D, Mitchell, P, MacInnis-Ng, C, Maréchaux, I, Martin, TA, Matheny, A, McDowell, N, McMahon, S, Meir, P, Mészáros, I, Molder, M, Mölder, M, Montagnani, L, Moore, GW, Nakada, R, Niu, F, Nolan, R, Norby,R, Novick, K, Oberhuber, W, Obojes, N, Oishi, AC, Oliveira RS, Oren, R, Ourcival,JM, Paljakka, T, Perez-Priego, O, Peri, PL, Peters, RL, Pfautsch, S, Pockman, WT, Preisler, Y, Rascher, K, Robinson, G, Rocha, H, Rocheteau, A, Röll, A, Rosado, BHP, Rowland, L [, Rubtsov, A, Sabaté, S, Salmon, Y, Salomón, RL, Sánchez-Costa, E, Schäfer, K, Schuldt, B, Shashkin, A, Stahl, C, Stojanovic, M, Suárez, JC, Sun, G, Niu, FR, Szatniewska, J, Tatarinov, F, Tesar, M, Thomas, FM, Tor-ngern, P, Urban, J, Valladares, F, Van der Tol, C, Van Meerveld, Varlagin, A, Norby, R, Voigt, H, Warren, J, Werner, C, Werner, W, Wieser, G, Wingate, W, Wullschleger, S, Koong, Y, Zweifel, R, Arain, MA, Oliveira, RS, Ourcival, JM, Aranda, I, Roll, A, Asbjornsen, H, Baxter, R, Beamesderfer, E, Berry, ZC, Berveiller, D, Blakely, B, Boggs, J, Bohrer, G, Bolstad, PV, Bonal, D, Bracho, R, Brito, P, Brodeur, J, Casanoves, F, Chave, J, Chen, H, Cisneros, C, Clark, K, Cremonese, E, Dang, HZ, David, JS, David, TS, Delpierre, N, Desai, AR, Do, Frederic C., Dohnal, M, Domec, JC, Dzikiti, S, Edgar, C, Eichstaedt, R, El-Madany, TS, Elbers, J, Eller, CB, Euskirchen, ES, Ewers, B, Fonti, P, Forner, A, Forrester, DI, Freitas, HC, Galvagno, M, Garcia-Tejera, O, Ghimire, CP, Gimeno, TE, Grace, J, Granier, A, Griebel, A, Guangyu, Y, Gush, MB, Hanson, PJ, Hasselquist, NJ, Heinrich, I, Hernandez-Santana, V, Herrmann, V, Hölttä, T, Holwerda, F, Irvine, J, Ayutthaya, SIN, Jarvis, PG, Jochheim, H, Ministerio de Ciencia e Innovación (España), Agència de Gestió d'Ajuts Universitaris i de Recerca, Poyatos, R [0000-0003-0521-2523], Granda, V [0000-0002-0469-1991], Flo, V [0000-0003-1908-4577], Adams, MA [0000-0002-8154-0097], Adorjan, B [0000-0002-5482-2515], Aidar, MPM [0000-0001-5728-6749], Alvarado-Barrientos, MS [0000-0003-0098-0806], Anderson-Teixeira, KJ [0000-0001-7585-712X], Arain, MA [0000-0002-1433-5173], Aranda, I [0000-0001-9086-7940], Asbjornsen, H [000-0001-8126-3328], Oishi, AC [0000-0001-5064-4080], Oliveira RS [0000-0002-6392-2526], Oren, R [0000-0002-5654-1733], Ourcival,JM [0000-0002-3557-3496], Paljakka, T [0000-0002-3674-4904], Perez-Priego, O [0000-0002-3138-3177], Peri, PL [0000-0002-5398-4408], Peters, RL [0000-0002-7441-1297], Pfautsch, S [0000-0002-4390-4195], Pockman, WT [0000-0002-3286-0457], Baxter, R [0000-0002-7504-6797], Preisler, Y [0000-0001-5861-8362], Rocha, H [0000-0002-5981-4469], Röll, A [0000-0001-9457-4459], Rosado, BHP [0000-0002-8924-8672], Rowland, L [0000-0002-0774-3216], Rubtsov, A [0000-0002-9663-4344], Sabaté, S [0000-0003-1854-0761], Salmon, Y [0000-0003-4433-4021], Salomón, RL [0000-0003-2674-1731], Sánchez-Costa, E [0000-0001-6799-9611], Beamesderfer, E [0000-0002-7513-7349], Schäfer, K [0000-0001-9452-3619], Schuldt, B [0000-0003-4738-5289], Shashkin, A [0000-0001-9925-5019], Stahl, C [0000-0001-5411-1169], Stojanović, M [0000-0003-4918-8668], Sun, G [0000-0002-0159-1370], Szatniewska, J [0000-0003-3027-9965], Tatarinov, F [0000-0002-8338-6070], Tesar, M [0000-0002-5647-8498], Thomas, FM [0000-0001-6377-719X], Berveiller, D [0000-0001-7461-6420], Tor-ngern, P [0000-0001-7363-4926], Urban, J [0000-0003-1730-947X], Van der Tol, C [0000-0002-2484-8191], Van Meerveld [0000-0002-7547-3270], Varlagin, A [0000-0002-2549-5236], Werner, C [0000-0002-7676-9057], Wieser, G [0000-0002-7575-5657], Wingate, W [0000-0003-1921-1556], Wullschleger, S [0000-0002-9869-0446], Koong, Y [0000-0002-8630-3031], Blakely, B [0000-0001-9923-0794], Zweifel, R [0000-0001-9438-0582], Steppe, K [0000-0001-6252-0704], Mencuccini, M [0000-0003-0840-1477], Martínez-Vilalta, J [0000-0002-2332-7298], Boggs, J [0000-0003-4826-1843], Bohrer, G [0000-0002-9209-9540], Bolstad, P [0000-0002-1829-8859], Bonal, D [0000-0001-9602-8603], Bracho, R [0000-0002-8015-9796], Brodeur, J [0000-0001-7118-8360], Casanoves, F [0000-0001-8765-9382], Chave, J [0000-0002-7766-1347], Chen, H [0000-0001-7619-3425], Cisneros, C [0000-0001-9661-4581], Clark, K [0000-0003-0534-9677], Cremonese, E [0000-0002-6708-8532], Dang, H [0000-0003-4428-3834], David, J [0000-0001-6994-1085], David, T [0000-0003-1473-899X], Delpierre, D [0000-0003-0906-9402], Desai, AR [0000-0002-5226-6041], Do, FC [0000-0002-2852-627X], Dohnal, M [0000-0003-1769-4750], Domec, JC [0000-0003-0478-2559], Dzikiti, S [0000-0002-1892-4143], Edgar, C [0000-0002-7026-8358], Eichstaedt, R [0000-0002-4905-7994], El-Madany, T [0000-0002-0726-7141], Elbers, J [0000-0002-0631-3505], Eller, CB [0000-0002-7795-2574], Euskirchen, E [0000-0002-0848-4295], Ewers, B [0000-0001-6647-7475], Fonti, P [0000-0002-7070-3292], Forner, A [0000-0002-7123-6403], Forrester, D [0000-0003-2732-5692], Freitas, HC [0000-0002-4861-1164], Galvagno, M [0000-0002-0827-487X], Garcia-Tejera, O [0000-0001-7726-8118], Ghimire, CP [0000-0002-3715-6311], Gimeno, TE [0000-0002-1707-9291], Granier, A [0000-0002-4174-2487], Griebel, A [0000-0002-4476-8279], Guangyu, Y [0000-0003-3242-5348], Gush, MB [0000-0003-1328-9862], Hanson, PJ [0000-0001-7293-3561], Hasselquist, N [0000-0003-2777-0163], Heinrich, I [0000-0001-5800-6999], Hernandez-Santana, V [0000-0001-9018-8622], Herrmann, V [0000-0002-4519-481X], Hölttä, T [0000-0001-7677-7156], Holwerda, F [0000-0003-4125-1765], Ayutthaya, SIN [0000-0002-5354-1527], Jochheim,H [0000-0001-8047-4553], Joly, CA [0000-0002-7945-2805], Kim, HS [0000-0002-3440-6071], Klemedtsson, L [0000-0002-1122-0717], Kropp, H [0000-0002-4258-3393], Lagergren, F [0000-0002-0061-733X], Lane, P [0000-0001-6121-8386], Lapenas, A [0000-0002-2135-3636], Lechuga, V [0000-0003-3745-587X], Lee,M [0000-0002-9601-3863], Leuschner, C [0000-0002-5689-7932], Limousin, JM [0000-0002-2734-2495], Linares, JC [0000-0001-8375-6353], Linderson, ML [0000-0001-6578-6671], Lindroth, A [0000-0002-7669-784X], Llorens, P [0000-0003-4591-5303], López-Bernal, A [0000-0002-1034-4718], Loranty, MM[0000-0001-8851-7386], Macinnis-Ng, C [0000-0003-3935-9814], Marechaux, I [0000-0002-5401-0197], Martin, TA [0000-0002-7872-4194], Matheny, A [0000-0002-9532-7131], McDowell, N [0000-0002-2178-2254], Meir, P [0000-0002-2362-0398], Mészáros, I [0000-0001-8841-730X], Migliavacca, M [0000-0003-3546-8407], Mölder, M [0000-0001-6767-3195], Montagnani, L [0000-0003-2957-9071], Moore, GW [0000-0001-5190-5983], Nakada, R [0000-0002-3704-1784], Niu, F [0000-0003-3445-4011], Nolan, R [0000-0001-9277-5142], Norby,R [0000-0002-0238-9828], Novick, K [0000-0002-8431-0879], Oberhuber, W [0000-0002-5197-7044], and Obojes, N [0000-0002-6718-2756]

Subjects
Stomatal condua, Sapflow measurements, Heat-pulse method, Vapor- pressure deficit, Radial patterns, Water use strategies, Eddy covariance, Fluw density, Thermal dissipation
Abstract

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr"R package-designed to access, visualize, and process SAPFLUXNET data-is available from CRAN. This research was supported by the Ministerio de Economía y Competitividad (grant no. CGL2014-55883-JIN), the Ministerio de Ciencia e Innovación (grant no. RTI2018-095297-J-I00), the Ministerio de Ciencia e Innovación (grant no. CAS16/00207), the Agència de Gestió d'Ajuts Universitaris i de Recerca (grant no. SGR1001), the Alexander von Humboldt-Stiftung (Humboldt Research Fellowship for Experienced Researchers (RP)), and the Institució Catalana de Recerca i Estudis Avançats (Academia Award (JMV)). Víctor Flo was supported by the doctoral fellowship FPU15/03939 (MECD, Spain).

Published
2021

20. Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil-plant-atmosphere continuum (STEMMUS-SCOPE v1.0.0)

Author

Wang, Y, Zeng, Y, Yu, L, Yang, P, Van der Tol, C, Yu, Q, Lu, X, Cai, H, and Su, Z

Subjects
04 Earth Sciences
Abstract

Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.

Published
2021

21. Nitrogen and phosphorus effect on sun-induced fluorescence and gross primary productivity in mediterranean grassland

Author

Martini, D, Pacheco-Labrador, J, Perez-Priego, O, van der Tol, C, El-Madany, T, Julitta, T, Rossini, M, Reichstein, M, Christiansen, R, Rascher, U, Moreno, G, Pilar Martin, M, Yang, P, Carrara, A, Guan, J, Gonzalez-Cascon, R, Migliavacca, M, Martini D., Pacheco-Labrador J., Perez-Priego O., van der Tol C., El-Madany T. S., Julitta T., Rossini M., Reichstein M., Christiansen R., Rascher U., Moreno G., Pilar Martin M., Yang P., Carrara A., Guan J., Gonzalez-Cascon R., Migliavacca M., Martini, D, Pacheco-Labrador, J, Perez-Priego, O, van der Tol, C, El-Madany, T, Julitta, T, Rossini, M, Reichstein, M, Christiansen, R, Rascher, U, Moreno, G, Pilar Martin, M, Yang, P, Carrara, A, Guan, J, Gonzalez-Cascon, R, Migliavacca, M, Martini D., Pacheco-Labrador J., Perez-Priego O., van der Tol C., El-Madany T. S., Julitta T., Rossini M., Reichstein M., Christiansen R., Rascher U., Moreno G., Pilar Martin M., Yang P., Carrara A., Guan J., Gonzalez-Cascon R., and Migliavacca M.

Abstract

Sun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F760 in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F760 and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F760. Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F760 by controlling the escape probability of F760 (Fesc). In particular, we found a change in GPP-F760 relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUEf), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F760 at leaf scale (F760leaf,fw), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP

Published
2019

22. Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits

Author

Pacheco-Labrador, J, Perez-Priego, O, El-Madany, T, Julitta, T, Rossini, M, Guan, J, Moreno, G, Carvalhais, N, Martin, M, Gonzalez-Cascon, R, Kolle, O, Reischtein, M, van der Tol, C, Carrara, A, Martini, D, Hammer, T, Moossen, H, Migliavacca, M, Pacheco-Labrador J., Perez-Priego O., El-Madany T. S., Julitta T., Rossini M., Guan J., Moreno G., Carvalhais N., Martin M. P., Gonzalez-Cascon R., Kolle O., Reischtein M., van der Tol C., Carrara A., Martini D., Hammer T. W., Moossen H., Migliavacca M., Pacheco-Labrador, J, Perez-Priego, O, El-Madany, T, Julitta, T, Rossini, M, Guan, J, Moreno, G, Carvalhais, N, Martin, M, Gonzalez-Cascon, R, Kolle, O, Reischtein, M, van der Tol, C, Carrara, A, Martini, D, Hammer, T, Moossen, H, Migliavacca, M, Pacheco-Labrador J., Perez-Priego O., El-Madany T. S., Julitta T., Rossini M., Guan J., Moreno G., Carvalhais N., Martin M. P., Gonzalez-Cascon R., Kolle O., Reischtein M., van der Tol C., Carrara A., Martini D., Hammer T. W., Moossen H., and Migliavacca M.

Abstract

The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, such as the Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation. The multiple-constraint inversion approach proposed together with the combination of optical RF and diel GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of senescent leaves in the grassland. The pr

Published
2019

23. Energy budget and radiative transfer modelling in the soil, vegetation, atmosphere continuum

Author

van der Tol, C., Yang, Peiqi, Prikaziuk, E., Verhoef, W., Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-WCC

Abstract

Radiative transfer modelling is often used to retrieve optical properties of the soil, vegetation, water and air from satellite data. The aim of the model 'Soil-Canopy-Observation of Photosynthesis and Energy fluxes' (SCOPE) is to further simulate the micro-climate in the vegetation, and the fluxes of energy and matter (carbon and water). We demonstrate, with examples of the latest version SCOPE2.0, how vegetation structure affects within-canopy heterogeneity of micro-climate and foliage temperatures. SCOPE links satellite observations in the visible, infrared and thermal domains with land surface processes in a physically based manner. It simulates radiative transfer in the soil, leaves and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 11 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP) and directional thermal signals. Here we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) It enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understory or with a vertical gradient in leaf chlorophyll concentration; (2) It enables the simulation of soil reflectance; (3) It includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) The computation speed has been reduced by 90\% compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in the computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the interoperability for the numerical retrieval of land surface products from satellite or airborne data.

Published
2020

24. Global transpiration data from sap flow measurements: The SAPFLUXNET database

Author

Poyatos, R., Granda, V, Flo, V., Adorján, B., Adams, M. A., Aguadé, D., Aidar, M. P. M., Allen, S., Alvarado-Barrientos, M. S., Anderson-Teixeira, K. J., Aparecido, L. M., Altaf Arain, M., Aranda, I., Asbjornsen, H., Baxter, R., Beamesderfer, E., Berry, Z. C., Berveiller, D., Blakely, B., Boggs, J., Bohrer, G., Bolstad, P. V., Bonal, D., Bracho, R., Brito, P., Brodeur, J., Casanoves, F., Chave, J., Chen, H., Cisneros, C., Clark, K., Cremonese, E., Dang, H., David, J. S., David, T. S., Delpierre, N., Desai, A. R., Do, F. C., Dohnal, N., Domec, J. C., Dzikiti, S., Edgar, C., Eichstaedt, R., El-Madany, T. S., Elbers, J., Eller, C. B., Euskirchen, E. S., Ewers, B., Fonti, P., Forner, A., Forrester, D. I., Freitas, H. C., Galvagno, M., Garcia-Tejera, O., Ghimire, C. P., Gimeno, T. E., Grace, J., Granier, A., Griebel, A., Guangyu, Y., Gush, M. B., Hanson, P. J., Hasselquist, N. J., Heinrich, I., Hernandez-Santana, V., Herrmann, V, Hölttä, T., Holwerda, F., Irvine, J., Na Ayutthaya, S. Y., Jarvis, P. J., Jochheim, H., Joly, C. A., Kaplick, J., Kim, H. S., Klemedtsson, L., Kropp, H., Lagergren, F., Lane, P., Lang, P., Lapenas, A., Lechuga, V., Lee, M., Leuschner, C., Limousin, J. M., Linares, J. C., Linderson, M. L., Lindroth, A., Llorens, P., López-Bernal, A., Loranty, M. M., Lüttschwager, D., MacInnis-Ng, C., Maréchaux, I., Martin, T. A., Matheny, A., McDowell, N., McMahon, S., Meir, P., Mészáros, I., Migliavacca, M., Mitchell, P., Mölder, M., Montagnani, L., Moore, G. W., Nakada, R., Niu, F., Nolan, R.H., Norby, R., Novick, K., Oberhuber, W., Obojes, N., Oishi, A. C., Oliveira, R. S., Olen, R., Ourcival, J. M., Paljakka, T., Perez Priego, O., Peri, P. L., Peters, R. L., Pfautsch, S., Pockman, W. T., Preysler, I., Rascher, K., Robinson, G., Rocheteau, A., Rocha, H., Röll, A., Rosado, B. H. P., Rowland, L., Rubtsov, A. V., Sabaté, S., Salmón, Y., Salomon, R. L., Schäfer, K. V. R., Sánchez-Costa, E., Schuld, B., Shashkin, A., Stahl, C., Stojanovic, M., Suárez, J. C., Szatniewska, J, Sun, G., Tatarinov, F., TesaÅ, M, Thomas, F. M., Tor-Ngern, P., Urban, J., Valladares, F., Van Der Tol, C., Van Meerveld, I., Varlagin, A., Voigt, H., Warren, J., Werner, C., Poyatos, R., Granda, V, Flo, V., Adorján, B., Adams, M. A., Aguadé, D., Aidar, M. P. M., Allen, S., Alvarado-Barrientos, M. S., Anderson-Teixeira, K. J., Aparecido, L. M., Altaf Arain, M., Aranda, I., Asbjornsen, H., Baxter, R., Beamesderfer, E., Berry, Z. C., Berveiller, D., Blakely, B., Boggs, J., Bohrer, G., Bolstad, P. V., Bonal, D., Bracho, R., Brito, P., Brodeur, J., Casanoves, F., Chave, J., Chen, H., Cisneros, C., Clark, K., Cremonese, E., Dang, H., David, J. S., David, T. S., Delpierre, N., Desai, A. R., Do, F. C., Dohnal, N., Domec, J. C., Dzikiti, S., Edgar, C., Eichstaedt, R., El-Madany, T. S., Elbers, J., Eller, C. B., Euskirchen, E. S., Ewers, B., Fonti, P., Forner, A., Forrester, D. I., Freitas, H. C., Galvagno, M., Garcia-Tejera, O., Ghimire, C. P., Gimeno, T. E., Grace, J., Granier, A., Griebel, A., Guangyu, Y., Gush, M. B., Hanson, P. J., Hasselquist, N. J., Heinrich, I., Hernandez-Santana, V., Herrmann, V, Hölttä, T., Holwerda, F., Irvine, J., Na Ayutthaya, S. Y., Jarvis, P. J., Jochheim, H., Joly, C. A., Kaplick, J., Kim, H. S., Klemedtsson, L., Kropp, H., Lagergren, F., Lane, P., Lang, P., Lapenas, A., Lechuga, V., Lee, M., Leuschner, C., Limousin, J. M., Linares, J. C., Linderson, M. L., Lindroth, A., Llorens, P., López-Bernal, A., Loranty, M. M., Lüttschwager, D., MacInnis-Ng, C., Maréchaux, I., Martin, T. A., Matheny, A., McDowell, N., McMahon, S., Meir, P., Mészáros, I., Migliavacca, M., Mitchell, P., Mölder, M., Montagnani, L., Moore, G. W., Nakada, R., Niu, F., Nolan, R.H., Norby, R., Novick, K., Oberhuber, W., Obojes, N., Oishi, A. C., Oliveira, R. S., Olen, R., Ourcival, J. M., Paljakka, T., Perez Priego, O., Peri, P. L., Peters, R. L., Pfautsch, S., Pockman, W. T., Preysler, I., Rascher, K., Robinson, G., Rocheteau, A., Rocha, H., Röll, A., Rosado, B. H. P., Rowland, L., Rubtsov, A. V., Sabaté, S., Salmón, Y., Salomon, R. L., Schäfer, K. V. R., Sánchez-Costa, E., Schuld, B., Shashkin, A., Stahl, C., Stojanovic, M., Suárez, J. C., Szatniewska, J, Sun, G., Tatarinov, F., TesaÅ, M, Thomas, F. M., Tor-Ngern, P., Urban, J., Valladares, F., Van Der Tol, C., Van Meerveld, I., Varlagin, A., Voigt, H., Warren, J., and Werner, C.

Abstract

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely a

Published
2021

25. Photosynthesis-sun induced fluorescence relationship in a Mediterranean grassland

Author

Martini, D, Pacheco-Labrador, J, Perez-Priego, O, Van Der Tol, C, El Madany, T, Julitta, T, Rossini, M, Gitelson, A, Reichstein, M, Migliavacca, M, Martini D., Pacheco-Labrador J., Perez-Priego O., Van Der Tol C., El Madany T., Julitta T., Rossini M., Gitelson A., Reichstein M., Migliavacca M., Martini, D, Pacheco-Labrador, J, Perez-Priego, O, Van Der Tol, C, El Madany, T, Julitta, T, Rossini, M, Gitelson, A, Reichstein, M, Migliavacca, M, Martini D., Pacheco-Labrador J., Perez-Priego O., Van Der Tol C., El Madany T., Julitta T., Rossini M., Gitelson A., Reichstein M., and Migliavacca M.

Abstract

Sun induced fluorescence at 760 nm (F 760 ) has shown to provide a valid approach to quantify gross primary production (GPP) at various scales, however the relationship between GPP and F 760 is influenced by the escape probability of fluorescence (Fesc), a variable which is not still fully understood. Combining radiative transfer modelling approaches, by means of the SCOPE model, and a data driven methodology based on variable selection methods we identify the predictors of Fesc, focusing on the effect of functional and structural traits. We show that Fesc is mainly predicted by structural variables such as fraction of grasses and near infrared reflectance. Building on the analysis of the predictor of Fesc, LUE p and LUE f we present a semi-empirical model formulation based only on optical data that significantly improves the GPP prediction.

Published
2018

26. Mapping plant traits in agricultural fields based on time series of hyperspectral images from Unmanned Aerial Vehicles (UAV)

Author

Ntakos, G., van der Tol, C., van der Wal, Tamme, Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
Remote Sensing, Plant traits, UAV images, Hyperspectral, Agriculture, SCOPE model, Radiative transfer modelling (RTM)
Abstract

Hyperspectral cameras, mounted on unmanned aerial vehicles provide information with high spatial, temporal and spectral resolution. Such detailed information allows us to monitor various plant traits which is essential for precision agriculture applications and changes the game of remote sensing which was, until recently, focused on multispectral imagery. In particular, the higher spectral resolution has the potential to provide details for the plant health status that were not distinguishable before. The added value of hyperspectral remote sensing may contribute to exploring the spatial and temporal development of various plant traits in the field. This can help farmers to redefine the management zones and agricultural practices throughout the cultivation period. During the summer of 2018, a time series of images from a potato field in the Netherlands was acquired, using a fixed-wing drone and a hyperspectral camera, made by the Swiss company Gamaya, able to measure reflectance in 40 bands in the range from 450nm to 900nm. The objective of the study was to create maps of key plant traits by using the lookup table (LUT) approach and a radiative transfer model (RTM). In order to narrow the range of variables for the LUT simulations, we first estimated the possible ranges of the vegetation parameters from 50 randomly selected pixels by using model inversion. By using the RTM SCOPE, we obtained the minimum and maximum of the parameters of the pixels from the study area. We thereafter generated a LUT of approximately 100,000 simulations for each image with the values of the input parameters within these minimums and maximums using a normal distribution sampling approach. Finally, we compared the reflectance measurements from the images to the LUT and found the best LUT simulation for each measurement, according to root mean square error (RMSE). In this way, the key vegetation parameters, such as Chlorophyll a+b concentration (Cab), Carotenoid content (Cca), Dry matter content (Cdm), Leaf area index (LAI), were estimated for the images. The results show that the size of the LUT is a key factor to achieve a low difference between the measured and simulated TOC reflectance. However, estimating plant trait values by assigning LUT simulations is a significantly less time-consuming approach than using numerical optimization for each individual image pixel. In our case study, we achieved an average RMSE of 0.13 with a standard deviation of 0.05 by selecting the “best” simulation for each pixel but, as a future work, it could be interesting to investigate whether selecting a bigger number of simulations to assign to the image pixels can further improve our results. Furthermore, when using RTMs, it is important to always take into consideration the potential ill-posedness of the retrieval, where different combinations of the model’s input parameters can produce the same TOC reflectance. This problem can be amplified due to measurement errors like the fast-changing lighting conditions at the time of data acquisition. * The project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721995.

Published
2019

27. Ecosystem fluxes simulation with the SCOPE model constrained by Sentinel-3 measurements: poster

Author

Prikaziuk, E., van der Tol, C., Migliavacca, Mirco, Pilar Martín, M., Paul-Limoges, Eugénie, Buchmann, Nina, Heinesch, Bernard, Vincke, Caroline, Sachs, Torsten, Bernhofer, Christian, Järvi, Leena, Kolari, Pasi, Joffre, Richard, Magliulo, Enzo, Grüning, Carsten, Cremonese, Edoardo, Galvagno, Marta, Varlagin, Andrej, Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
remote sensing, Sentinel-3, SCOPE model, Ecosystem flux
Abstract

The aim of this research is to assess the feasibility of retrieving plant functional traits from Sentinel-3 data by using combined vegetation-atmosphere inversions of radiative transfer models. Sentinel-3 is a constellation of ESA’s satellites (Sentinel-3A and 3B) that have roughly 30 bands on two sensors (Ocean and Land Colour Instrument, OLCI and Sea and Land Surface Temperature Radiometer, SLSTR) in the visible, near- and mid-infrared regions. The revisit time of about one day makes Sentinel-3 suitable for producing time series and monitoring the ecosystem state. However, the measurements provided by Sentinel-3 are strongly affected by the atmosphere. Atmospheric correction is usually carried out before surface parameter retrievals, but we attempted to retrieve properties from OLCI and SLSTR from the radiative transfer models SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) and 6S so the vegetation and atmospheric parameters are simultaneously modelled. SCOPE is a radiative transfer and micro-meteorological model that simulates reflectance spectra at leaf and canopy levels as well as photosynthesis and the components of the energy balance (net radiation, sensible and latent heat flux) for soil an canopy. We developed an algorithm based on the coupling of the optical module of SCOPE with the atmospheric radiative transfer model 6S to retrieve plant traits (chlorophyll content, leaf area index, water content) from Sentinel-3 measurements. Clouded images, based on a cloud band of SLSTR product, and images without vegetation, based on a low value of the normalized difference vegetation index (NDVI), were excluded from the analysis. The retrieved plant traits were further used as an input for the complete SCOPE model to produce ecosystem fluxes. Ground measurements from a number of European FLUXNET eddy-covariance towers were used for the validation of the simulated ecosystem fluxes. The plant functional types represented by those towers included savannahs, croplands, grasslands, evergreen broadleaf and needleleaf forests and deciduous forests. To reduce the noise in the data, we compared 3-days mean fluxes. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

Published
2019

28. Global sensitivity analysis of coupled SCOPE-6S model

Author

Prikaziuk, E., van der Tol, C., Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
remote sensing, SCOPE model, Sentinel-3, global sensitivity analysis (GSA)
Abstract

We quantified the relative importance of different Sentinel-3 bands for retrieval of plant traits and atmospheric composition parameters by coupling a land surface (SCOPE) and an atmospheric radiative transfer model (S6). For this purpose, we simulated top of atmosphere (TOA) radiance of the two instruments on-board of Sentinel-3 (Ocean and Land Colour Instrument, OLCI and Sea and Land Surface Temperature Radiometer, SLSTR), and carried out a sensitivity analysis of these radiances to vegetation, soil and atmospheric parameters. The SCOPE model parameters included in the sensitivity analysis were leaf traits (chlorophylls, Cab; carotenoids, Cca; anthocyanins, Cant; water thickness, Cw; dry matter content, Cdm; senescent material, Cs; leaf mesophyll structure parameter, N), canopy traits (leaf area index, LAI; leaf inclination distribution functions parameters, LIDFa, LIDFb) and soil traits (brightness, B; soil moisture content, SMC). Top of canopy reflectance simulated with SCOPE was propagated through the atmosphere to produce TOA radiance. The atmospheric propagation was carried out with 6S using a “Continental” aerosol distribution profile scaled to provided aerosol optical thickness values (AOT). Water and ozone values were used to scale “us standard 62” atmospheric profile. Three parameters of 6S model (AOT, water and ozone) were used in the sensitivity analysis. TOA radiance values were integrated with the sensor response functions of OLCI and SLSTR. The global sensitivity analysis was done against 15 parameters with Saltelli sampling and Sobol method. The results demonstrated that LAI and soil brightness had high impact on all the bands of both OLCI and SLSTR instruments. As expected, chlorophyll content significantly influenced bands Oa06-11 (561-709 nm) and S1-2 (554, 659 nm) and water content (both leaf and atmospheric) Oa20-21 (941, 1025 nm) and S4-6 (1375, 1613, 2256 nm). Bands beyond 750 nm were sensitive to dry matter content. The rest of the parameters showed low impact on TOA radiance with total sensitivity index close to 0.05, whereas N parameter did not demonstrate its importance at all. The next step in the analysis will be the inclusion of the SLSTR bands in the thermal domain S7-9 (3.7, 10.8, 12.0 um). The results will be used for the development of a retrieval algorithm of land surface properties from Sentinel-3. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

Published
2019

29. Analysis of short-term vegetation dynamics combining hyperspectral measurements and RTMs inversion

Author

Celesti, M, van der Tol, C, Cogliati, S, Colombo, R, Miglietta, F, Panigada, C, Pinto, F, Rascher, U, Schüttemeyer, D, Tagliabue, G, Yang, P, Rossini, M, Celesti, M, van der Tol, C, Cogliati, S, Colombo, R, Miglietta, F, Panigada, C, Pinto, F, Rascher, U, Schüttemeyer, D, Tagliabue, G, Yang, P, and Rossini, M

Subjects
Hyperspectral, Radiative Transfer Modelling, SCOPE, Sun-Induced Chlorophyll Fluorescence
Published
2018

31. Quantitative global mapping of terrestrial vegetation photosynthesis: The Fluorescence Explorer (FLEX) mission

Author

Moreno, J, Colombo, R, Damm, A, Goulas, Y, Middleton, E, Miglietta, F, Mohammed, G, Mottus, M, North, P, Rascher, U, Van Der Tol, C, Drusch, M, Moreno J., Colombo R., Damm A., Goulas Y., Middleton E., Miglietta F., Mohammed G., Mottus M., North P., Rascher U., Van Der Tol C., Drusch M., Moreno, J, Colombo, R, Damm, A, Goulas, Y, Middleton, E, Miglietta, F, Mohammed, G, Mottus, M, North, P, Rascher, U, Van Der Tol, C, Drusch, M, Moreno J., Colombo R., Damm A., Goulas Y., Middleton E., Miglietta F., Mohammed G., Mottus M., North P., Rascher U., Van Der Tol C., and Drusch M.

Abstract

Although traditional remote sensing systems based on spectral reflectance can already provide estimates of the 'potential' photosynthetic activity of terrestrial vegetation through the quantification of total canopy chlorophyll content or absorbed photosynthetic radiation, the determination of the 'actual' photosynthetic activity of terrestrial vegetation requires information about how the absorbed light is used by plants, such as vegetation fluorescence, using very high spectral resolution spectroscopy in the range 650-800 nm. The Fluorescence Explorer (FLEX) mission, selected in November 2015 as the 8th Earth Explorer by the European Space Agency (ESA), carries the FLORIS spectrometer, with a spectral resolution of 0.3 nm and a spatial resolution of 300 m, with a swath of 150 km. The FLEX mission is designed to fly in tandem with the Copernicus Sentinel-3 satellite, in order to provide all the necessary information to disentangle emitted fluorescence from the background reflected radiance, and to allow proper interpretation of the fluorescence spatial and temporal changes in relation to photosynthesis dynamics, accounting for non-photochemical energy dissipation and canopy temperature effects.

Published
2017

32. Sun-induced Fluorescence For Assessing Vegetation Functioning From Remote

Author

Rossini, M, Celesti, M, Cogliati, S, Colombo, R, Damm, A, Julitta, T, Juszczak, R, Migliavacca, M, Miglietta, F, Panigada, C, Perez Priego, O, Pinto, F, Rascher, U, Reichstein, M, Sakowska, K, Schickling, A, Schüttemeyer, D, Tagliabue, G, van der Tol, C, ROSSINI, MICOL, CELESTI, MARCO, COGLIATI, SERGIO, COLOMBO, ROBERTO, JULITTA, TOMMASO, PANIGADA, CINZIA, TAGLIABUE, GIULIA, van der Tol, C., Rossini, M, Celesti, M, Cogliati, S, Colombo, R, Damm, A, Julitta, T, Juszczak, R, Migliavacca, M, Miglietta, F, Panigada, C, Perez Priego, O, Pinto, F, Rascher, U, Reichstein, M, Sakowska, K, Schickling, A, Schüttemeyer, D, Tagliabue, G, van der Tol, C, ROSSINI, MICOL, CELESTI, MARCO, COGLIATI, SERGIO, COLOMBO, ROBERTO, JULITTA, TOMMASO, PANIGADA, CINZIA, TAGLIABUE, GIULIA, and van der Tol, C.

Published
2017

33. USING DESIS AND EO-1 HYPERION REFLECTANCE TIME SERIES FOR THE ASSESSMENT OF VEGETATION TRAITS AND GROSS PRIMARY PRODUCTION (GPP).

Author

Campbell, P. E. K., Huemmrich, K. F., Middleton, E. M., Alfieri, J., van der Tol, C., and Neigh, C. S. R.

Subjects
LEAF area index, TIME series analysis, REFLECTANCE, BIOLOGICAL interfaces, VEGETATION dynamics
Abstract

This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400–2400 nm) reflectance collection over vegetated flux sites, to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using together VNIR reflectance from EO-1 Hyperion and DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function. [ABSTRACT FROM AUTHOR]

Published
2021
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34. Detecting Nitrogen and Water status of Potato Crops with Machine Learning

Author

Ntakos, G., Prikaziuk, E., van der Tol, C., van der Wal, Tamme, Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
Remote Sensing, Machine Learning, Vegetation stress, UAV images, Agriculture
Abstract

This study investigates the potential of using a random forest approach to detect the nitrogen and water status of potato crops based on multispectral reflectance. For this purpose, images from 2 different experimental fields in the Netherlands were used. Each field had plots with 6 different water and nitrogen treatments and 3 different potato varieties, with one of them being the same in both. The images consist of 9 bands in the visible - near infrared region and one band in the Long-Wave Infrared. The results showed that the random forest algorithm was able to achieve an accuracy of 85% for detecting the 6 different treatments, and 90% for detecting water or nitrogen status after local calibration. The results, however, were different if the algorithm was trained in one field and tested in another. By applying the algorithm only to the variety that was common in both the fields, the 6 treatments were detected with accuracy of approximately 48% while it was able to detect water or nitrogen status with accuracy of 63% and 79% respectively.

Published
2019

35. Heterogeneity of a leaf for radiative transfer: poster

Author

Prikaziuk, E., van der Tol, C., Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
remote sensing, PROSPECT, Fluspect, retrieval, leaf optical properties
Abstract

Popular radiative transfer models (PROSPECT, Fluspect) represent mesophyll of leaves as a homogeneous medium of pigments and water. However, mesophyll of many leaves is heterogeneous. The aim of the work was to analyse how significant the difference in optical properties between two sides of a leaf is. The key question was: Is possible to retrieve extra plant traits by adding additional data – optical properties of the abaxial side? We surveyed three-five leaves of six species: two Monocots and four Dicots. Leaves were fixed in a FluoWAT leaf clip and optical properties (reflectance and transmittance) were recorded for both sides of each leaf with ASD spectroradiometer under solar light. The spectral data was used to invert the radiative transfer model Fluspect and retrieve leaf traits. We were able to validate accuracy of the retrieval for chlorophyll and carotenoid concentrations, as we measured them by extraction right after the spectral measurements. The results showed that the reflectance from the abaxial side was 0.05 to 0.10 higher than that of the adaxial side in dicots, while there was no substantial difference between the two sides in monocots. No significant side-related differences in transmittance was observed. Among different combinations of retrieval strategies (from reflectance, from transmittance, from both) the lowest RMSE for chlorophyll was obtained from adaxial reflectance but for carotenoids – from abaxial reflectance data. Observed difference in reflectance may be explained by different surface properties (hairs in the epidermis) or the structure of the mesophyll. We intend to study this further by including these effects in Fluspect and upscaling it to canopy level with the model SCOPE. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

Published
2018

36. Sun-induced Fluorescence For Assessing Vegetation Functioning From Remote

Author

ROSSINI, MICOL, CELESTI, MARCO, COGLIATI, SERGIO, COLOMBO, ROBERTO, JULITTA, TOMMASO, PANIGADA, CINZIA, TAGLIABUE, GIULIA, Damm, A, Juszczak, R, Migliavacca, M, Miglietta, F, Perez Priego, O, Pinto, F, Rascher, U, Reichstein, M, Sakowska, K, Schickling, A, Schüttemeyer, D, van der Tol, C., Rossini, M, Celesti, M, Cogliati, S, Colombo, R, Damm, A, Julitta, T, Juszczak, R, Migliavacca, M, Miglietta, F, Panigada, C, Perez Priego, O, Pinto, F, Rascher, U, Reichstein, M, Sakowska, K, Schickling, A, Schüttemeyer, D, Tagliabue, G, and van der Tol, C

Subjects
Sun-induced Fluorescence, Photosynthetic efficiency, vegetation, stress
Published
2017

37. Short-term dynamics of sun-induced chlorophyll fluorescence to detect variations in photosynthetic efficiency: an applied case study using ground and airborne data

Author

CELESTI, MARCO, COGLIATI, SERGIO, JULITTA, TOMMASO, PANIGADA, CINZIA, TAGLIABUE, GIULIA, COLOMBO, ROBERTO, ROSSINI, MICOL, Pinto, F, Alberti, G, Bozzi, E, Cilia, C, Damm, A, Delle Vedove, G, Guanter, L, Juszczak, R, Matveeva, M, Palombo, A, Pignatti, S, Sakowska, K, Schickling, A, Schuettemeyer, D, Stróżecki, M, Tudoroiu, M, van der Tol, C, Miglietta, F, Rascher, U, Celesti, M, Pinto, F, Alberti, G, Bozzi, E, Cilia, C, Cogliati, S, Damm, A, Delle Vedove, G, Guanter, L, Julitta, T, Juszczak, R, Matveeva, M, Palombo, A, Panigada, C, Pignatti, S, Sakowska, K, Schickling, A, Schuettemeyer, D, Stróżecki, M, Tagliabue, G, Tudoroiu, M, van der Tol, C, Colombo, R, Miglietta, F, Rascher, U, and Rossini, M

Subjects
sun-induced chlorophyll fluorescence, hyperspectral, remote sensing, photosynthetic efficiency
Published
2017

38. Summary of ESA FLuorescence EXplorer (FLEX) field campaigns aiming at linking fluorescence and photosynthesis

Author

Tagliabue, G, Celesti, M, Cogliati, S, Colombo, R, Dechant, B, Julitta, T, Meroni, M, Miglietta, F, Moreno, J, Panigada, C, Schickling, A, Schüttemeyer, D, Rascher, U, Ryu, Y, van der Tol, C, Verrelst, J, Rossini, M, Tagliabue, G, Celesti, M, Cogliati, S, Colombo, R, Dechant, B, Julitta, T, Meroni, M, Miglietta, F, Moreno, J, Panigada, C, Schickling, A, Schüttemeyer, D, Rascher, U, Ryu, Y, van der Tol, C, Verrelst, J, and Rossini, M

Subjects
Photosynthesi, Sun-induced chlorophyll fluorescence, Remote sensing, Vegetation status
Published
2017

39. Hyplant-derived Sun-Induced Fluorescence-A new opportunity to disentangle complex vegetation signals from diverse vegetation types

Author

Bandopadhyay, S, Rastogi, A, Rascher, U, Rademske, P, Schickling, A, Cogliati, S, Julitta, T, Mac Arthur, A, Hueni, A, Tomelleri, E, Celesti, M, Burkart, A, Stróżecki, M, Sakowska, K, Gąbka, M, Rosadziński, S, Sojka, M, Iordache, M, Reusen, I, Van Der Tol, C, Damm, A, Schuettemeyer, D, Juszczak, R, Iordache, MD, Bandopadhyay, S, Rastogi, A, Rascher, U, Rademske, P, Schickling, A, Cogliati, S, Julitta, T, Mac Arthur, A, Hueni, A, Tomelleri, E, Celesti, M, Burkart, A, Stróżecki, M, Sakowska, K, Gąbka, M, Rosadziński, S, Sojka, M, Iordache, M, Reusen, I, Van Der Tol, C, Damm, A, Schuettemeyer, D, Juszczak, R, and Iordache, MD

Abstract

Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF760, were strongly associated with the functional diversity of the peatland vegetation. At the peatl

Published
2019

40. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Author

Mohammed, G, Colombo, R, Middleton, E, Rascher, U, van der Tol, C, Nedbal, L, Goulas, Y, Pérez-Priego, O, Damm, A, Meroni, M, Joiner, J, Cogliati, S, Verhoef, W, Malenovský, Z, Gastellu-Etchegorry, J, Miller, J, Guanter, L, Moreno, J, Moya, I, Berry, J, Frankenberg, C, Zarco-Tejada, P, Mohammed, Gina H., Colombo, Roberto, Middleton, Elizabeth M., Rascher, Uwe, van der Tol, Christiaan, Nedbal, Ladislav, Goulas, Yves, Pérez-Priego, Oscar, Damm, Alexander, Meroni, Michele, Joiner, Joanna, Cogliati, Sergio, Verhoef, Wouter, Malenovský, Zbyněk, Gastellu-Etchegorry, Jean-Philippe, Miller, John R., Guanter, Luis, Moreno, Jose, Moya, Ismael, Berry, Joseph A., Frankenberg, Christian, Zarco-Tejada, Pablo J., Mohammed, G, Colombo, R, Middleton, E, Rascher, U, van der Tol, C, Nedbal, L, Goulas, Y, Pérez-Priego, O, Damm, A, Meroni, M, Joiner, J, Cogliati, S, Verhoef, W, Malenovský, Z, Gastellu-Etchegorry, J, Miller, J, Guanter, L, Moreno, J, Moya, I, Berry, J, Frankenberg, C, Zarco-Tejada, P, Mohammed, Gina H., Colombo, Roberto, Middleton, Elizabeth M., Rascher, Uwe, van der Tol, Christiaan, Nedbal, Ladislav, Goulas, Yves, Pérez-Priego, Oscar, Damm, Alexander, Meroni, Michele, Joiner, Joanna, Cogliati, Sergio, Verhoef, Wouter, Malenovský, Zbyněk, Gastellu-Etchegorry, Jean-Philippe, Miller, John R., Guanter, Luis, Moreno, Jose, Moya, Ismael, Berry, Joseph A., Frankenberg, Christian, and Zarco-Tejada, Pablo J.

Abstract

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in tandem with state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Published
2019

42. Spatio-temporal mapping of daily photosynthesis in drought conditions using remote sensing observations and in-situ measurements: abstract

Author

Bayat, B., van der Tol, C., Verhoef, W., Raj, R., Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
ITC-GOLD
Abstract

Reliable spatio-temporal information about the photosynthesis and its dependence on environmental factors, is crucial for vegetation productivity monitoring, water resources management, and detection of climate change effects.. In this study, we investigated the relationship between time series of Landsat (TM5 and ETM7) optical data, soil moisture measurements and canopy daily photosynthesis of annual C3 grasses at a Fluxnet site (US-Var) during a prolonged drought episode from January to August 2004. By using the ‘Soil -Canopy Observation of Photosynthesis and Energy fluxes’ (SCOPE) model, time series maps of photosynthesis were simulated via Landsat retrieved vegetation properties maps [notably Leaf Area Index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm), the leaf inclination distribution function (LIDF) and the senescent material content (Cs)], locally measured weather data and in-situ soil moisture data. The generated maps of photosynthesis simulated by the SCOPE model were validated at a Fluxnet site (US-Var). The comparison between daily photosynthesis simulations and measurements shows that considerable drought effects on photosynthesis are ‘visible’ in the Landsat optical bands. However, the most accurate photosynthesis maps are obtained when soil moisture information is added as an extra source of input data to the SCOPE simulations. The results from this study indicate that the combined use of optical remote sensing observations and in-situ measured soil moisture data has a great potential to capture the drought effects on the grass canopy photosynthesis.

Published
2018

43. SCOPE model inversion for Sentinel-3 data retrieval

Author

Prikaziuk, E., van der Tol, C., Department of Water Resources, UT-I-ITC-WCC, and Faculty of Geo-Information Science and Earth Observation

Subjects
remote sensing, Remote sensing, Ecosystem, Retrieval, Sentinel-3, SCOPE, Sentinel-3, SCOPE model, retrieval
Abstract

The SCOPE is a coupled radiative transfer and energy balance model used for simulation of vegetation optical properties and temperature at leaf and canopy level over a spectral range from 0.4 to 50 µm. Inversion of the model allows to retrieve a number of plant traits: pigments (Cab, Car, Cant), dry matter content (Cdm), water content (Cw), leaf area index (LAI) and others. Subsequent forward simulation can calculate photosynthesis, evapotranspiration (ET) and fraction of absorbed photosynthetically active radiation (fAPAR) that can be used further for integrated water use efficiency (WUE) and light use efficiency (LUE) calculations, respectively. The higher the accuracy in retrieved parameters is achieved the higher precision in calculated ecosystem functional properties will be. The aim of this work was to develop a model-based retrieval algorithm from multispectral satellite data. The initial retrieval algorithm used numerical optimisation of residuals squared sum and operated over the spectral range from 0.4 to 2.4 µm. First, the algorithm was extended to the thermal domain (up to 50 µm) and validated against open-source spectral measurement datasets (SPECCHIO). As the SCOPE model operates at both leaf and canopy levels we had to use different cost functions and constraints for each level. Having validated the hyperspectral retrieval algorithm, we tried to make a convolution to multispectral case of Sentinel-3 satellite sensors: ocean and land colour instrument (OLCI) and sea and land surface temperature radiometer (SLTR). Finally, parameter retrieved with the algorithm from Sentinel-3 images were used for forward simulation of the SCOPE model and calculation of integrated WUE and LUE at few selected FLUXNET towers. The results of the simulation were validated against data from FLUXNET eddy-covariance towers. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

Published
2018
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44. Retrieval of Spatio-temporal Evaporation by Integrating Landsat OLI Optical and Thermal Data

Author

Wandera, Loise, van der Tol, C., Mallick, Kaniska, Bayat, B., Barrios, Miguel, Verhoef, W., Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-WCC

Subjects
ITC-GOLD
Abstract

Soil-Vegetation-Atmosphere (SVAT) Transfer Models are capable of providing continuous predictions of evapotranspiration (ET). However, providing these models with reliable spatio-temporal information of vegetation and soil properties remains challenging. Thus, combining optical and thermal satellite information might assists to overcome this challenge when using SVAT models. In this study, using a radiative transfer model of solar and sky radiation (RTMo), we simulate Landsat 8 reflectance bands (2-7). We then apply a numerical optimization approach to invert the model and retrieve the corresponding canopy attributes leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm), leaf brown material (Cs), Leaf Area Index (LAI) and the leaf angle distribution function in the canopy at overpass time. The retrievals are then directly used as inputs into our SVAT model of choice, Soil Canopy Observations of Photochemistry and Energy Fluxes (SCOPE). Using a model for transfer of thermal radiation emitted by vegetation and soil (RTMt), we proceed to simulate Landsat radiance for the corresponding reflectance data using a lookup table (LUT). These variables were then used to develop a crop factor (Kc) map. A reference ET was generated and applied to the Kc map to obtain actual ET. We proceeded to interpolate the ET between the image acquisition dates to have a complete time series. The retrieval maps for the specific variables captured seasonal variability patterns for the respective variables. The generated KC map showed similar trend with the LAI maps. There was an underestimation of actual ET when the simulation was not constrained to the thermal information. The interpolation of ET between acquisition image dates reflected the seasonal trends. Key Word: SVAT, optical, thermal, remote sensing, evapotranspiration

Published
2017

45. Bayesian integration of flux tower data into process-based simulator for quantifying uncertainty in simulated output

Author

Raj, R., Hamm, N.A.S., van der Tol, C., Stein, A., Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, UT-I-ITC-WCC, Department of Earth Observation Science, and UT-I-ITC-ACQUAL

Subjects
ITC-GOLD
Abstract

Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can, however, be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator BIOME-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55 year old Douglas fir stand as an example. The uncertainties of both the BIOME-BGC parameters and the simulated GPP were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in Rubisco (FLNR), and soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash-Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated BIOME-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly, and some overestimate in spring and underestimates in summer remained after calibration. Further analysis showed that, although simulated GPP was time dependent due to carbon allocation, it still followed the variability of the meteorological forcing closely. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by allowing the parameter values to vary month-by-month. Time varying parameters substantially improved the simulated GPP as compared to GPP obtained with constant parameters. The time varying estimation also revealed a seasonal change in parameter values that determine phenology, and in parameters that determine soil water availability. It was concluded that Bayesian calibration approach can reveal features of the modelled physical processes, and identify aspects of the process simulator that are too rigid.

Published
2016
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49. Modeling forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the qilian mountains

Author

Tian, X, Yan, M, van der Tol, C, Li, Z, Su, Z, Chen, E, Li, X, Li, L, Wang, X, Pan, X, Gao, L, Han, Z, Tian, X, Yan, M, van der Tol, C, Li, Z, Su, Z, Chen, E, Li, X, Li, L, Wang, X, Pan, X, Gao, L, and Han, Z

Abstract

© 2017 The Author(s) In this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China. Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R2 = 0.70 and RMSE = 24.52 tones ha−1). We then obtained one seasonal cycle (2011) of GPP (R2 = 0.88 and RMSE = 5.02 gC m−2 8d−1) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (Biome-BioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R2 = 0.75 and RMSE = 1. 27 gC m−2 d−1 for GPP, and R2 = 0.61 and RMSE = 1.17 gC m−2 d−1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R2 = 0.73 and RMSE = 46.65 g m−2 year−1). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step

Published
2017

50. Short-term dynamics of sun-induced chlorophyll fluorescence to detect variations in photosynthetic efficiency: an applied case study using ground and airborne data

Author

Celesti, M, Pinto, F, Alberti, G, Bozzi, E, Cilia, C, Cogliati, S, Damm, A, Delle Vedove, G, Guanter, L, Julitta, T, Juszczak, R, Matveeva, M, Palombo, A, Panigada, C, Pignatti, S, Sakowska, K, Schickling, A, Schuettemeyer, D, Stróżecki, M, Tagliabue, G, Tudoroiu, M, van der Tol, C, Colombo, R, Miglietta, F, Rascher, U, Rossini, M, CELESTI, MARCO, COGLIATI, SERGIO, JULITTA, TOMMASO, PANIGADA, CINZIA, TAGLIABUE, GIULIA, COLOMBO, ROBERTO, ROSSINI, MICOL, Celesti, M, Pinto, F, Alberti, G, Bozzi, E, Cilia, C, Cogliati, S, Damm, A, Delle Vedove, G, Guanter, L, Julitta, T, Juszczak, R, Matveeva, M, Palombo, A, Panigada, C, Pignatti, S, Sakowska, K, Schickling, A, Schuettemeyer, D, Stróżecki, M, Tagliabue, G, Tudoroiu, M, van der Tol, C, Colombo, R, Miglietta, F, Rascher, U, Rossini, M, CELESTI, MARCO, COGLIATI, SERGIO, JULITTA, TOMMASO, PANIGADA, CINZIA, TAGLIABUE, GIULIA, COLOMBO, ROBERTO, and ROSSINI, MICOL

Published
2017

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