Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-473-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-473-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reviews and syntheses
| 
25 Jan 2024
Reviews and syntheses |
| 25 Jan 2024
| 25 Jan 2024
Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Lammert Kooistra
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Katja Berger
CORRESPONDING AUTHOR
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
Benjamin Brede
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Lukas Valentin Graf
Earth Observation of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
Department of Environmental Systems Sciences, Institute of Agricultural Science, Crop Science, ETH Zürich, Zurich, Switzerland
Helge Aasen
Earth Observation of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
Department of Environmental Systems Sciences, Institute of Agricultural Science, Crop Science, ETH Zürich, Zurich, Switzerland
Jean-Louis Roujean
CESBIO, CNES, CNRS, INRAE, IRD, UT3, 18 avenue Edouard Belin, BPI 2801, TOULOUSE Cedex 9, 31401, France
Miriam Machwitz
Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, 4422 Belvaux, Luxembourg
Martin Schlerf
Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, 4422 Belvaux, Luxembourg
Clement Atzberger
Mantle Labs Ltd 29 Farm Street, London W1J 5RL, UK
Egor Prikaziuk
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, the Netherlands
Dessislava Ganeva
Space Research and Technology Institute – Bulgarian Academy of Sciences, Georgi Bonchev bl. 1, 1113 Sofia, Bulgaria
Enrico Tomelleri
Faculty of Agricultural, Environmental and Food Sciences; Free University of Bozen/Bolzano, Bolzano, Italy
Holly Croft
School of Biosciences, University of Sheffield, Sheffield, S10 2TN, UK
Institute for Sustainable Food, University of Sheffield, Sheffield, S10 2TN, UK
Pablo Reyes Muñoz
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
Virginia Garcia Millan
European Topic Centre, University of Malaga, Arquitecto Francisco Peñalosa, 18, 29010 Málaga, Spain
Roshanak Darvishzadeh
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, the Netherlands
Gerbrand Koren
Copernicus Institute of Sustainable Development, Utrecht University, 3584 CB Utrecht, the Netherlands
Ittai Herrmann
The Plant Sensing Laboratory, The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
Offer Rozenstein
Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization – Volcani Institute, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
Santiago Belda
UAVAC, Applied Mathematics Department, University of Alicante, 03080 Alicante, Spain
Miina Rautiainen
School of Engineering, Department of Built Environment, Aalto University, 02150 Espoo, Finland
Stein Rune Karlsen
NORCE Norwegian Research Centre AS, P.O. Box 6434, 9294 Tromsø, Norway
Cláudio Figueira Silva
Forest Research Centre (CEF) and Associated Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Sofia Cerasoli
Forest Research Centre (CEF) and Associated Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
Jon Pierre
Mantle Labs Ltd 29 Farm Street, London W1J 5RL, UK
Emine Tanır Kayıkçı
Karadeniz Technical University, Engineering Faculty, Department of Geomatics Engineering, Trabzon, Turkey
Andrej Halabuk
Institute of Landscape Ecology, Slovak Academy of Sciences, 814 99 Bratislava, Slovakia
Esra Tunc Gormus
Karadeniz Technical University, Engineering Faculty, Department of Geomatics Engineering, Trabzon, Turkey
Frank Fluit
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Zhanzhang Cai
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62 Lund, Sweden
Marlena Kycko
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmieście 26/28, 00-927, Warsaw, Poland
Thomas Udelhoven
EOCP – Earth Observation and Climate Processes, Environmental Remote Sensing & Geoinformatics, Trier University, 54296 Trier, Germany
Jochem Verrelst
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain
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This article is included in the Encyclopedia of Geosciences
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This article is included in the Encyclopedia of Geosciences
Hanna Sjulgård, Lukas Valentin Graf, Tino Colombi, Juliane Hirte, Thomas Keller, and Helge Aasen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1872, https://doi.org/10.5194/egusphere-2024-1872, 2024
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This article is included in the Encyclopedia of Geosciences
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This article is included in the Encyclopedia of Geosciences
Juliëtte C. S. Anema, Klaas Folkert Boersma, Piet Stammes, Gerbrand Koren, William Woodgate, Philipp Köhler, Christian Frankenberg, and Jacqui Stol
Biogeosciences, 21, 2297–2311, https://doi.org/10.5194/bg-21-2297-2024, https://doi.org/10.5194/bg-21-2297-2024, 2024
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This article is included in the Encyclopedia of Geosciences
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 15–34, https://doi.org/10.5194/essd-16-15-2024, https://doi.org/10.5194/essd-16-15-2024, 2024
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This article is included in the Encyclopedia of Geosciences
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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This article is included in the Encyclopedia of Geosciences
Raphael Zürcher, Jiayan Zhao, Alvaro Lau Sarmiento, Benjamin Brede, and Alexander Klippel
AGILE GIScience Ser., 4, 15, https://doi.org/10.5194/agile-giss-4-15-2023, https://doi.org/10.5194/agile-giss-4-15-2023, 2023
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Earth Syst. Sci. Data, 15, 579–605, https://doi.org/10.5194/essd-15-579-2023, https://doi.org/10.5194/essd-15-579-2023, 2023
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This article is included in the Encyclopedia of Geosciences
S. Hamzeh, M. Hajeb, S. K. Alavipanah, and J. Verrelst
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 271–277, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-271-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-271-2023, 2023
Stijn Naus, Lucas G. Domingues, Maarten Krol, Ingrid T. Luijkx, Luciana V. Gatti, John B. Miller, Emanuel Gloor, Sourish Basu, Caio Correia, Gerbrand Koren, Helen M. Worden, Johannes Flemming, Gabrielle Pétron, and Wouter Peters
Atmos. Chem. Phys., 22, 14735–14750, https://doi.org/10.5194/acp-22-14735-2022, https://doi.org/10.5194/acp-22-14735-2022, 2022
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We assimilate MOPITT CO satellite data in the TM5-4D-Var inverse modelling framework to estimate Amazon fire CO emissions for 2003–2018. We show that fire emissions have decreased over the analysis period, coincident with a decrease in deforestation rates. However, interannual variations in fire emissions are large, and they correlate strongly with soil moisture. Our results reveal an important role for robust, top-down fire CO emissions in quantifying and attributing Amazon fire intensity.
This article is included in the Encyclopedia of Geosciences
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
This article is included in the Encyclopedia of Geosciences
Anne Schucknecht, Bumsuk Seo, Alexander Krämer, Sarah Asam, Clement Atzberger, and Ralf Kiese
Biogeosciences, 19, 2699–2727, https://doi.org/10.5194/bg-19-2699-2022, https://doi.org/10.5194/bg-19-2699-2022, 2022
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Actual maps of grassland traits could improve local farm management and support environmental assessments. We developed, assessed, and applied models to estimate dry biomass and plant nitrogen (N) concentration in pre-Alpine grasslands with drone-based multispectral data and canopy height information. Our results indicate that machine learning algorithms are able to estimate both parameters but reach a better level of performance for biomass.
This article is included in the Encyclopedia of Geosciences
Malte Ortner, Michael Seidel, Sebastian Semella, Thomas Udelhoven, Michael Vohland, and Sören Thiele-Bruhn
SOIL, 8, 113–131, https://doi.org/10.5194/soil-8-113-2022, https://doi.org/10.5194/soil-8-113-2022, 2022
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Soil organic carbon (SOC) and its labile fractions are influenced by soil use and mineral properties. These parameters interact with each other and affect SOC differently depending on local conditions. To investigate the latter, the dependence of SOC content on parameters that vary on a local scale depending on parent material, soil texture, and land use as well as parameter combinations was statistically assessed. Relevance and superiority of local models compared to total models were shown.
This article is included in the Encyclopedia of Geosciences
Thomas Luke Smallman, David Thomas Milodowski, Eráclito Sousa Neto, Gerbrand Koren, Jean Ometto, and Mathew Williams
Earth Syst. Dynam., 12, 1191–1237, https://doi.org/10.5194/esd-12-1191-2021, https://doi.org/10.5194/esd-12-1191-2021, 2021
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Our study provides a novel assessment of model parameter, structure and climate change scenario uncertainty contribution to future predictions of the Brazilian terrestrial carbon stocks to 2100. We calibrated (2001–2017) five models of the terrestrial C cycle of varied structure. The calibrated models were then projected to 2100 under multiple climate change scenarios. Parameter uncertainty dominates overall uncertainty, being ~ 40 times that of either model structure or climate change scenario.
This article is included in the Encyclopedia of Geosciences
Peiqi Yang, Egor Prikaziuk, Wout Verhoef, and Christiaan van der Tol
Geosci. Model Dev., 14, 4697–4712, https://doi.org/10.5194/gmd-14-4697-2021, https://doi.org/10.5194/gmd-14-4697-2021, 2021
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Since the first publication 12 years ago, the SCOPE model 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.
This article is included in the Encyclopedia of Geosciences
Anteneh Getachew Mengistu, Gizaw Mengistu Tsidu, Gerbrand Koren, Maurits L. Kooreman, K. Folkert Boersma, Torbern Tagesson, Jonas Ardö, Yann Nouvellon, and Wouter Peters
Biogeosciences, 18, 2843–2857, https://doi.org/10.5194/bg-18-2843-2021, https://doi.org/10.5194/bg-18-2843-2021, 2021
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In this study, we assess the usefulness of Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval (SIFTER) data from the GOME-2A instrument and near-infrared reflectance of vegetation (NIRv) from MODIS to capture the seasonality and magnitudes of gross primary production (GPP) derived from six eddy-covariance flux towers in Africa in the overlap years between 2007–2014. We also test the robustness of sun-induced fluoresence and NIRv to compare the seasonality of GPP for the major biomes.
This article is included in the Encyclopedia of Geosciences
Joost Buitink, Anne M. Swank, Martine van der Ploeg, Naomi E. Smith, Harm-Jan F. Benninga, Frank van der Bolt, Coleen D. U. Carranza, Gerbrand Koren, Rogier van der Velde, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 6021–6031, https://doi.org/10.5194/hess-24-6021-2020, https://doi.org/10.5194/hess-24-6021-2020, 2020
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The amount of water stored in the soil is critical for the productivity of plants. Plant productivity is either limited by the available water or by the available energy. In this study, we infer this transition point by comparing local observations of water stored in the soil with satellite observations of vegetation productivity. We show that the transition point is not constant with soil depth, indicating that plants use water from deeper layers when the soil gets drier.
This article is included in the Encyclopedia of Geosciences
Anne J. Hoek van Dijke, Kaniska Mallick, Martin Schlerf, Miriam Machwitz, Martin Herold, and Adriaan J. Teuling
Biogeosciences, 17, 4443–4457, https://doi.org/10.5194/bg-17-4443-2020, https://doi.org/10.5194/bg-17-4443-2020, 2020
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We investigated the link between the vegetation leaf area index (LAI) and the land–atmosphere exchange of water, energy, and carbon fluxes. We show that the correlation between the LAI and water and energy fluxes depends on the vegetation type and aridity. For carbon fluxes, however, the correlation with the LAI was strong and independent of vegetation and aridity. This study provides insight into when the vegetation LAI can be used to model or extrapolate land–atmosphere fluxes.
This article is included in the Encyclopedia of Geosciences
S. Chauhan, R. Darvishzadeh, M. Boschetti, and A. Nelson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 267–274, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-267-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-267-2020, 2020
H. Haggrén, P. Ståhle, M. Vaaja, P. Rönnholm, P. Sarkola, M. Rautiainen, M. Nordman, and J. Nikander
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 17–22, https://doi.org/10.5194/isprs-annals-V-5-2020-17-2020, https://doi.org/10.5194/isprs-annals-V-5-2020-17-2020, 2020
Getachew Agmuas Adnew, Thijs L. Pons, Gerbrand Koren, Wouter Peters, and Thomas Röckmann
Biogeosciences, 17, 3903–3922, https://doi.org/10.5194/bg-17-3903-2020, https://doi.org/10.5194/bg-17-3903-2020, 2020
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We measured the effect of photosynthesis, the largest flux in the carbon cycle, on the triple oxygen isotope composition of atmospheric CO2 at the leaf level during gas exchange using three plant species. The main factors that limit the impact of land vegetation on the triple oxygen isotope composition of atmospheric CO2 are identified, characterized and discussed. The effect of photosynthesis on the isotopic composition of CO2 is commonly quantified as discrimination (ΔA).
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Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
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The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
This article is included in the Encyclopedia of Geosciences
Xiaojin Qian, Liangyun Liu, Holly Croft, and Jingming Chen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-228, https://doi.org/10.5194/bg-2019-228, 2019
Preprint withdrawn
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The leaf maximum carboxylation rate (Vcmax) is a key photosynthesis parameter. We attempt to investigate whether a universal and stable relationship exists between leaf Vcmax25 and chlorophyll content across different C3 plant types from a plant physiological perspective and verify it using field experiments. The results confirm that leaf chlorophyll can be a reliable proxy for estimating Vcmax25, providing an operational approach for the global mapping of Vcmax25 across different plant types.
This article is included in the Encyclopedia of Geosciences
G. T. Alckmin, L. Kooistra, A. Lucieer, and R. Rawnsley
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1827–1831, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1827-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1827-2019, 2019
S. Chauhan, R. Darvishzadeh, Y. Lu, D. Stroppiana, M. Boschetti, M. Pepe, and A. Nelson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 235–240, https://doi.org/10.5194/isprs-archives-XLII-2-W13-235-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-235-2019, 2019
A. Tubau Comas, J. Valente, and L. Kooistra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 631–635, https://doi.org/10.5194/isprs-archives-XLII-2-W13-631-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-631-2019, 2019
C. Zhang, J. Valente, L. Kooistra, L. Guo, and W. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 673–680, https://doi.org/10.5194/isprs-archives-XLII-2-W13-673-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-673-2019, 2019
J. Valente, M. Doldersum, C. Roers, and L. Kooistra
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 179–185, https://doi.org/10.5194/isprs-annals-IV-2-W5-179-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-179-2019, 2019
Anne J. Hoek van Dijke, Kaniska Mallick, Adriaan J. Teuling, Martin Schlerf, Miriam Machwitz, Sibylle K. Hassler, Theresa Blume, and Martin Herold
Hydrol. Earth Syst. Sci., 23, 2077–2091, https://doi.org/10.5194/hess-23-2077-2019, https://doi.org/10.5194/hess-23-2077-2019, 2019
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Satellite images are often used to estimate land water fluxes over a larger area. In this study, we investigate the link between a well-known vegetation index derived from satellite data and sap velocity, in a temperate forest in Luxembourg. We show that the link between the vegetation index and transpiration is not constant. Therefore we suggest that the use of vegetation indices to predict transpiration should be limited to ecosystems and scales where the link has been confirmed.
This article is included in the Encyclopedia of Geosciences
Sofia Cerasoli, Manuel Campagnolo, Joana Faria, Carla Nogueira, and Maria da Conceição Caldeira
Biogeosciences, 15, 5455–5471, https://doi.org/10.5194/bg-15-5455-2018, https://doi.org/10.5194/bg-15-5455-2018, 2018
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We compared the ability of in situ spectral and satellite sensors to estimate the productivity of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify the best set of spectral predictors. In situ CO gas exchange and vegetation reflectance measurements were used for this purpose. Our results show the potential of Sentinel 2 and Landsat 8 satellites to monitor grasslands in support of a sustainable agriculture management.
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M. H. D. Franceschini, H. Bartholomeus, D. van Apeldoorn, J. Suomalainen, and L. Kooistra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 109–112, https://doi.org/10.5194/isprs-archives-XLII-2-W6-109-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-109-2017, 2017
Bob van der Meij, Lammert Kooistra, Juha Suomalainen, Janna M. Barel, and Gerlinde B. De Deyn
Biogeosciences, 14, 733–749, https://doi.org/10.5194/bg-14-733-2017, https://doi.org/10.5194/bg-14-733-2017, 2017
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Plant–soil feedback (PSF) is an important mechanism to explain plant performance in natural and agricultural systems but is hard to quantify in field experiments. We used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way at high resolution using UAV-based optical sensors.
This article is included in the Encyclopedia of Geosciences
Aarne Hovi, Jingjing Liang, Lauri Korhonen, Hideki Kobayashi, and Miina Rautiainen
Biogeosciences, 13, 6015–6030, https://doi.org/10.5194/bg-13-6015-2016, https://doi.org/10.5194/bg-13-6015-2016, 2016
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We investigated forest albedo and FAPAR in Alaska and Finland in the boreal zone, using a radiative transfer model parameterized with forest inventory data. Albedo and canopy FAPAR were tightly connected in coniferous forests, indicating that managing forests to increase albedo may compromise productivity. Alaskan and Finnish forests differed in their albedo and FAPAR values, and solar elevation was an important factor controlling the relationships between forest structure, albedo, and FAPAR.
This article is included in the Encyclopedia of Geosciences
Kaniska Mallick, Ivonne Trebs, Eva Boegh, Laura Giustarini, Martin Schlerf, Darren T. Drewry, Lucien Hoffmann, Celso von Randow, Bart Kruijt, Alessandro Araùjo, Scott Saleska, James R. Ehleringer, Tomas F. Domingues, Jean Pierre H. B. Ometto, Antonio D. Nobre, Osvaldo Luiz Leal de Moraes, Matthew Hayek, J. William Munger, and Steven C. Wofsy
Hydrol. Earth Syst. Sci., 20, 4237–4264, https://doi.org/10.5194/hess-20-4237-2016, https://doi.org/10.5194/hess-20-4237-2016, 2016
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While quantifying vegetation water use over multiple plant function types in the Amazon Basin, we found substantial biophysical control during drought as well as a water-stress period and dominant climatic control during a water surplus period. This work has direct implication in understanding the resilience of the Amazon forest in the spectre of frequent drought menace as well as the role of drought-induced plant biophysical functioning in modulating the water-carbon coupling in this ecosystem.
This article is included in the Encyclopedia of Geosciences
Elnaz Neinavaz, Andrew K. Skidmore, Roshanak Darvishzadeh, and Thomas A. Groen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 99–105, https://doi.org/10.5194/isprs-archives-XLI-B7-99-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-99-2016, 2016
A. Porcar-Castell, A. Mac Arthur, M. Rossini, L. Eklundh, J. Pacheco-Labrador, K. Anderson, M. Balzarolo, M. P. Martín, H. Jin, E. Tomelleri, S. Cerasoli, K. Sakowska, A. Hueni, T. Julitta, C. J. Nichol, and L. Vescovo
Biogeosciences, 12, 6103–6124, https://doi.org/10.5194/bg-12-6103-2015, https://doi.org/10.5194/bg-12-6103-2015, 2015
S. Carter, M. Herold, M. C. Rufino, K. Neumann, L. Kooistra, and L. Verchot
Biogeosciences, 12, 4809–4825, https://doi.org/10.5194/bg-12-4809-2015, https://doi.org/10.5194/bg-12-4809-2015, 2015
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Emission from agriculture-driven deforestation can be mitigated by reducing the expansion of agriculture into forests through intensification and utilizing non-forested land for agriculture. Climate-smart agriculture can reduce emissions from existing agricultural land. Tropical countries which are priorities for action can be identified by assessing the mitigation potential of these interventions, by assessing capacity for implementation and the risks associated with these approaches.
This article is included in the Encyclopedia of Geosciences
Related subject area
Remote Sensing: Terrestrial
Remote sensing reveals fire-driven enhancement of a C4 invasive alien grass on a small Mediterranean volcanic island
Divergent biophysical responses of western United States forests to wildfire driven by eco-climatic gradients
Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models
Data-based investigation of the effects of canopy structure and shadows on chlorophyll fluorescence in a deciduous oak forest
Evaluation of five models for constructing forest NPP–age relationships in China based on 3121 field survey samples
Geographically divergent trends in snow disappearance timing and fire ignitions across boreal North America
Dune belt restoration effectiveness assessed by UAV topographic surveys (northern Adriatic coast, Italy)
High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France
Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields
Riccardo Guarino, Daniele Cerra, Renzo Zaia, Alessandro Chiarucci, Pietro Lo Cascio, Duccio Rocchini, Piero Zannini, and Salvatore Pasta
Biogeosciences, 21, 2717–2730, https://doi.org/10.5194/bg-21-2717-2024, https://doi.org/10.5194/bg-21-2717-2024, 2024
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The severity and the extent of a large fire event that occurred on the small volcanic island of Stromboli (Aeolian archipelago, Italy) on 25–26 May 2022 were evaluated through remotely sensed data to assess the short-term effect of fire on local plant communities. For the first time, we documented the outstanding after-fire resilience of an invasive alien species, Saccharum biflorum, which is a rhizomatous C4 perennial grass introduced on the island in the nineteenth century.
This article is included in the Encyclopedia of Geosciences
Surendra Shrestha, Christopher A. Williams, Brendan M. Rogers, John Rogan, and Dominik Kulakowski
Biogeosciences, 21, 2207–2226, https://doi.org/10.5194/bg-21-2207-2024, https://doi.org/10.5194/bg-21-2207-2024, 2024
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Here, we generated chronosequences of leaf area index (LAI) and surface albedo as a function of time since fire to demonstrate the differences in the characteristic trajectories of post-fire biophysical changes among seven forest types and 21 level III ecoregions of the western United States (US) using satellite data from different sources. We also demonstrated how climate played the dominant role in the recovery of LAI and albedo 10 and 20 years after wildfire events in the western US.
This article is included in the Encyclopedia of Geosciences
Ricardo Martínez Prentice, Miguel Villoslada, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp
Biogeosciences, 21, 1411–1431, https://doi.org/10.5194/bg-21-1411-2024, https://doi.org/10.5194/bg-21-1411-2024, 2024
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Despite hosting a wide range of ecosystem services, coastal wetlands face threats from global changes. This study models the plant fractional cover of plant communities in Estonian coastal meadows with a synergistic use of drone, satellite imagery and digital elevation models. This approach highlights the significant contribution of digital elevation models to multispectral data, enabling the modelling of heterogeneous plant community distributions in such wetlands.
This article is included in the Encyclopedia of Geosciences
Hamadou Balde, Gabriel Hmimina, Yves Goulas, Gwendal Latouche, Abderrahmane Ounis, and Kamel Soudani
Biogeosciences, 21, 1259–1276, https://doi.org/10.5194/bg-21-1259-2024, https://doi.org/10.5194/bg-21-1259-2024, 2024
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We show that FyieldLIF was not correlated with SIFy at the diurnal timescale, and the diurnal patterns in SIF and PAR did not match under clear-sky conditions due to canopy structure. Φk was sensitive to canopy structure. RF models show that Φk can be predicted using reflectance in different bands. RF models also show that FyieldLIF was more sensitive to reflectance and radiation than SIF and SIFy, indicating that the combined effect of reflectance bands could hide the SIF physiological trait.
This article is included in the Encyclopedia of Geosciences
Peng Li, Rong Shang, Jing M. Chen, Mingzhu Xu, Xudong Lin, Guirui Yu, Nianpeng He, and Li Xu
Biogeosciences, 21, 625–639, https://doi.org/10.5194/bg-21-625-2024, https://doi.org/10.5194/bg-21-625-2024, 2024
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The amount of carbon that forests gain from the atmosphere, called net primary productivity (NPP), changes a lot with age. These forest NPP–age relationships could be modeled from field survey data, but we are not sure which model works best. Here we tested five different models using 3121 field survey samples in China, and the semi-empirical mathematical (SEM) function was determined as the optimal. The relationships built by SEM can improve China's forest carbon modeling and prediction.
This article is included in the Encyclopedia of Geosciences
Thomas D. Hessilt, Brendan M. Rogers, Rebecca C. Scholten, Stefano Potter, Thomas A. J. Janssen, and Sander Veraverbeke
Biogeosciences, 21, 109–129, https://doi.org/10.5194/bg-21-109-2024, https://doi.org/10.5194/bg-21-109-2024, 2024
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In boreal North America, snow and frozen ground prevail in winter, while fires occur in summer. Over the last 20 years, the northwestern parts have experienced earlier snow disappearance and more ignitions. This is opposite to the southeastern parts. However, earlier ignitions following earlier snow disappearance timing led to larger fires across the region. Snow disappearance timing may be a good proxy for ignition timing and may also influence important atmospheric conditions related to fires.
This article is included in the Encyclopedia of Geosciences
Regine Anne Faelga, Luigi Cantelli, Sonia Silvestri, and Beatrice Maria Sole Giambastiani
Biogeosciences, 20, 4841–4855, https://doi.org/10.5194/bg-20-4841-2023, https://doi.org/10.5194/bg-20-4841-2023, 2023
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A dune restoration project on the northern Adriatic coast (Ravenna, Italy) was assessed using UAV monitoring. Structure-from-motion photogrammetry, elevation differencing, and statistical analysis were used to quantify dune development in terms of sand volume and vegetation cover change. Results show that the installed fence has been effective as there was significant sand accumulation, embryo dune development, and a decrease in blowout features due to increased vegetation colonization.
This article is included in the Encyclopedia of Geosciences
Lilian Vallet, Martin Schwartz, Philippe Ciais, Dave van Wees, Aurelien de Truchis, and Florent Mouillot
Biogeosciences, 20, 3803–3825, https://doi.org/10.5194/bg-20-3803-2023, https://doi.org/10.5194/bg-20-3803-2023, 2023
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This study analyzes the ecological impact of the 2022 summer fire season in France by using high-resolution satellite data. The total biomass loss was 2.553 Mt, equivalent to a 17 % increase of the average natural mortality of all French forests. While Mediterranean forests had a lower biomass loss, there was a drastic increase in burned area and biomass loss over the Atlantic pine forests and temperate forests. This result revisits the distinctiveness of the 2022 fire season.
This article is included in the Encyclopedia of Geosciences
Arthur Bayle, Bradley Z. Carlson, Anaïs Zimmer, Sophie Vallée, Antoine Rabatel, Edoardo Cremonese, Gianluca Filippa, Cédric Dentant, Christophe Randin, Andrea Mainetti, Erwan Roussel, Simon Gascoin, Dov Corenblit, and Philippe Choler
Biogeosciences, 20, 1649–1669, https://doi.org/10.5194/bg-20-1649-2023, https://doi.org/10.5194/bg-20-1649-2023, 2023
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Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. We used remote sensing approaches to study early succession dynamics as it allows to analyze the deglaciation, colonization, and vegetation growth within a single framework. We found that the heterogeneity of early succession dynamics is deterministic and can be explained well by local environmental context. This work has been done by an international consortium.
This article is included in the Encyclopedia of Geosciences
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Abbas, S., Nichol, J. E., and Wong, M. S.: Trends in vegetation productivity related to climate change in China's Pearl River Delta, PLOS ONE, 16, e0245467, https://doi.org/10.1371/journal.pone.0245467, 2021. a
Abdi, A. M., Carrié, R., Sidemo-Holm, W., Cai, Z., Boke-Olén, N., Smith, H. G., Eklundh, L., and Ekroos, J.: Biodiversity decline with increasing crop productivity in agricultural fields revealed by satellite remote sensing, Ecol. Indic., 130, 108098, https://doi.org/10.1016/j.ecolind.2021.108098, 2021. a
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Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation...
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