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Your search keyword '"Martha M. Farella"' showing total 6 results
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6 results on '"Martha M. Farella"'

1. Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Author

Mallory L. Barnes, Martha M. Farella, Russell L. Scott, David J. P. Moore, Guillermo E. Ponce-Campos, Joel A. Biederman, Natasha MacBean, Marcy E. Litvak, and David D. Breshears

Subjects
Geology, QE1-996.5, Environmental sciences, GE1-350
Abstract

Upscaling in situ carbon flux measurements using remotely sensed and meteorological observations in a machine learning algorithm leads to improved estimates of average uptake, and interannual variability in global drylands.

Published
2021
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2. Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

Author

Martha M. Farella, Mallory L. Barnes, David D. Breshears, Jessica Mitchell, Willem J. D. van Leeuwen, and Rachel E. Gallery

Subjects
foliar nitrogen, hyperspectral, imaging spectroscopy, partial least squares regression, plant traits, remote sensing, Ecology, QH540-549.5
Abstract

Abstract The nitrogen content in plant foliar tissues (foliar N) regulates photosynthetic capacity and has a major impact on global biogeochemical cycles. Despite its importance, a robust, time, and cost‐effective methodology to estimate variation in foliar N concentration across globally represented terrestrial systems does not exist. Although advances in remote sensing data have enabled landscape‐scale foliar N predictions, improved accuracy is needed to effectively capture variation in foliar N across ecosystems. Airborne remote sensing imagery was analyzed in conjunction with ground‐sampled foliar chemistry data (n = 692), provided by the NEON, to predict foliar N at sites across the United States covering a variety of plant communities and climate types. We developed indices from novel two‐band combinations that predicted foliar N more accurately than existing indices (≈8% improvement across all sites and a 45% improvement in arid sites). Compared with two‐band indices, we increased accuracy and decreased bias of foliar N predictions by using full‐spectrum reflectance information and partial least squares regression (PLSR) models (R2 = 0.638; root mean square error = 0.440). Significant wavelengths included red edge (720–765 nm), near infrared (NIR) reflectance at 1125 nm, and shortwave infrared (SWIR) reflectance at 2050 and 2095 nm, which are regions indicative of foliar traits such as growth type (e.g., leaf area index with NIR) and photosynthetic parameters (e.g., chlorophyll and Rubisco with red and SWIR reflectance, respectively). With the confluence of rapid increases in computing power, several forthcoming or recently launched hyperspectral missions, and the development of large‐scale environmental research observatories worldwide, we have an exciting opportunity to estimate foliar N across larger spatial areas covering more diverse biomes than ever before. We anticipate that these predictions will prove to be invaluable in helping to constrain biogeochemical model uncertainties across a global range of terrestrial ecosystems.

Published
2022
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5. The three major axes of terrestrial ecosystem function

Author

Georg Wohlfahrt, Sebastian Wolf, David Martini, Alexander Knohl, Micol Rossini, Dennis D. Baldocchi, Nuno Carvalhais, Talie Musavi, Tarek S. El-Madany, Ulisse Gomarasca, Ankur R. Desai, Matthias Forkel, Dario Papale, Marcos Fernández-Martínez, Jens Kattge, Jacob A. Nelson, Michael J. Liddell, Peter B. Reich, Guido Kraemer, Christopher M. Gough, Miguel D. Mahecha, Mathias Göckede, Hiroki Ikawa, Elise Pendall, Ivan A. Janssens, Rune Christiansen, Jiquan Chen, Craig Macfarlane, Alessandro Cescatti, Andreas Ibrom, Leonardo Montagnani, Marta Galvagno, T. Andrew Black, Michael Bahn, Sönke Zaehle, Giorgio Matteucci, Dan Yakir, Russell L. Scott, Silvia Caldararu, Jürgen Knauer, Markus Reichstein, Ulrich Weber, Richard P. Phillips, Cinzia Panigada, Mirco Migliavacca, Ian J. Wright, Jonas Peters, Beverly E. Law, Josep Peñuelas, Martha M. Farella, Arnaud Carrara, Eyal Rotenberg, Ivan Mammarella, Gianluca Filippa, Xuanlong Ma, Hideki Kobayashi, Jamie Cleverly, Oscar Perez-Priego, Edoardo Cremonese, Peter D. Blanken, Karen Anderson, Martin Jung, Clément Stahl, Daniel E. Pabon-Moreno, Damien Bonal, Nina Buchmann, Trevor F. Keenan, Institute for Atmospheric and Earth System Research (INAR), Micrometeorology and biogeochemical cycles, Migliavacca, M, Musavi, T, Mahecha, M, Nelson, J, Knauer, J, Baldocchi, D, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, T, Blanken, P, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, A, El-Madany, T, Farella, M, Fernandez-Martinez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, C, Gockede, M, Ibrom, A, Ikawa, H, Janssens, I, Jung, M, Kattge, J, Keenan, T, Knohl, A, Kobayashi, H, Kraemer, G, Law, B, Liddell, M, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, D, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, R, Reich, P, Rossini, M, Rotenberg, E, Scott, R, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, I, Yakir, D, Zaehle, S, and Reichstein, M

Subjects
010504 meteorology & atmospheric sciences, Range (biology), General Science & Technology, Climate, Ecosystem ecology, Biome, Datasets as Topic, 01 natural sciences, 114 Physical sciences, Article, Carbon Cycle, 03 medical and health sciences, Water Cycle, SDG 13 - Climate Action, Ecosystem, Biology, 030304 developmental biology, 0105 earth and related environmental sciences, SDG 15 - Life on Land, 0303 health sciences, Principal Component Analysis, Multidisciplinary, Ecology, Humidity, ecosystem ecology, Vegetation, 15. Life on land, Carbon Dioxide, Plants, Arid, Productivity (ecology), Biogeography, 13. Climate action, Ecosystem function, terrestrial biomes, productivity, vegetation structure, water-use efficiency, carbon-use efficiency, Environmental science, Terrestrial ecosystem, Engineering sciences. Technology
Abstract

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8., Three key axes of variation of ecosystem functional changes and their underlying causes are identified from a dataset of surface gas exchange measurements across major terrestrial biomes and climate zones.

Published
2021
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