Articles | Volume 16, issue 10
https://doi.org/10.5194/essd-16-4573-2024
© 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/essd-16-4573-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Oct 2024
Data description paper |
| 10 Oct 2024
| 10 Oct 2024
VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring
Ruxandra-Maria Zotta
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Leander Moesinger
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Robin van der Schalie
Planet Labs, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands
Mariette Vreugdenhil
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Wolfgang Preimesberger
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Thomas Frederikse
Planet Labs, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands
Richard de Jeu
Planet Labs, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands
Wouter Dorigo
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
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Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Luca Ciabatta, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner
Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, https://doi.org/10.5194/essd-10-267-2018, 2018
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In this study, rainfall is estimated starting from satellite soil moisture observation on a global scale, using the ESA CCI soil moisture datasets. The new obtained rainfall product has proven to correctly identify rainfall events, showing performance sometimes higher than those obtained by using classical rainfall estimation approaches.
Matthias Forkel, Wouter Dorigo, Gitta Lasslop, Irene Teubner, Emilio Chuvieco, and Kirsten Thonicke
Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, https://doi.org/10.5194/gmd-10-4443-2017, 2017
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Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. We introduce here a new flexible data-driven fire modelling approach that allows us to explore sensitivities of burned areas to satellite and climate datasets. Our results suggest combining observations with data-driven and process-oriented fire models to better understand the role of fires in the Earth system.
Clément Albergel, Simon Munier, Delphine Jennifer Leroux, Hélène Dewaele, David Fairbairn, Alina Lavinia Barbu, Emiliano Gelati, Wouter Dorigo, Stéphanie Faroux, Catherine Meurey, Patrick Le Moigne, Bertrand Decharme, Jean-Francois Mahfouf, and Jean-Christophe Calvet
Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, https://doi.org/10.5194/gmd-10-3889-2017, 2017
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LDAS-Monde, a global land data assimilation system, is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. It is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the ISBA land surface model coupled with the CTRIP continental hydrological system. Assimilation of SSM and LAI leads to a better representation of evapotranspiration and gross primary production.
Marko Scholze, Michael Buchwitz, Wouter Dorigo, Luis Guanter, and Shaun Quegan
Biogeosciences, 14, 3401–3429, https://doi.org/10.5194/bg-14-3401-2017, https://doi.org/10.5194/bg-14-3401-2017, 2017
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This paper briefly reviews data assimilation techniques in carbon cycle data assimilation and the requirements of data assimilation systems on observations. We provide a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation, focussing on relevant space-based observations.
Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de la Torre, Gianpaolo Balsamo, Albert van Dijk, Frederiek Sperna Weiland, Marie Minvielle, Jean-Christophe Calvet, Bertrand Decharme, Stephanie Eisner, Gabriel Fink, Martina Flörke, Stefanie Peßenteiner, Rens van Beek, Jan Polcher, Hylke Beck, René Orth, Ben Calton, Sophia Burke, Wouter Dorigo, and Graham P. Weedon
Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, https://doi.org/10.5194/essd-9-389-2017, 2017
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The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.
Brecht Martens, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest
Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, https://doi.org/10.5194/gmd-10-1903-2017, 2017
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Terrestrial evaporation is a key component of the hydrological cycle and reliable data sets of this variable are of major importance. The Global Land Evaporation Amsterdam Model (GLEAM, www.GLEAM.eu) is a set of algorithms which estimates evaporation based on satellite observations. The third version of GLEAM, presented in this study, includes an improved parameterization of different model components. As a result, the accuracy of the GLEAM data sets has been improved upon previous versions.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
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Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Markus Enenkel, Christoph Reimer, Wouter Dorigo, Wolfgang Wagner, Isabella Pfeil, Robert Parinussa, and Richard De Jeu
Hydrol. Earth Syst. Sci., 20, 4191–4208, https://doi.org/10.5194/hess-20-4191-2016, https://doi.org/10.5194/hess-20-4191-2016, 2016
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Soil moisture is a crucial variable for a variety of applications, ranging from weather forecasting and agricultural production to the monitoring of floods and droughts. Satellite observations are particularly important in regions where no in situ measurements are available. Our study presents a method to integrate global near-real-time satellite observations from different sensors into one harmonized, daily data set. A first validation shows good results on a global scale.
C. Szczypta, J.-C. Calvet, F. Maignan, W. Dorigo, F. Baret, and P. Ciais
Geosci. Model Dev., 7, 931–946, https://doi.org/10.5194/gmd-7-931-2014, https://doi.org/10.5194/gmd-7-931-2014, 2014
A. Loew, T. Stacke, W. Dorigo, R. de Jeu, and S. Hagemann
Hydrol. Earth Syst. Sci., 17, 3523–3542, https://doi.org/10.5194/hess-17-3523-2013, https://doi.org/10.5194/hess-17-3523-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Biogeosciences and biodiversity
A spectral–structural characterization of European temperate, hemiboreal, and boreal forests
Crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of the United States: reconciliation of top-down and bottom-up data sources
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI
A synthesized field survey database of vegetation and active-layer properties for the Alaskan tundra (1972–2020)
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
TCSIF: a temporally consistent global Global Ozone Monitoring Experiment-2A (GOME-2A) solar-induced chlorophyll fluorescence dataset with the correction of sensor degradation
National forest carbon harvesting and allocation dataset for the period 2003 to 2018
Spatial mapping of key plant functional traits in terrestrial ecosystems across China
HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022
EUPollMap: the European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach
Reference maps of soil phosphorus for the pan-Amazon region
Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022
Investigating limnological processes and modern sedimentation at Lake Żabińskie, northeast Poland: a decade-long multi-variable dataset, 2012–2021
Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Thirty-meter map of young forest age in China
GRiMeDB: the Global River Methane Database of concentrations and fluxes
A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
A remote-sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of the Sierra Nevada (southeastern Spain)
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
A global database on holdover time of lightning-ignited wildfires
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake
Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
Global climate-related predictors at kilometer resolution for the past and future
A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
Rates and timing of chlorophyll-a increases and related environmental variables in global temperate and cold-temperate lakes
Harmonized gap-filled datasets from 20 urban flux tower sites
Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains
The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
LegacyPollen 1.0: a taxonomically harmonized global late Quaternary pollen dataset of 2831 records with standardized chronologies
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, and Lucie Homolová
Earth Syst. Sci. Data, 16, 5069–5087, https://doi.org/10.5194/essd-16-5069-2024, https://doi.org/10.5194/essd-16-5069-2024, 2024
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Radiative transfer models play a key role in monitoring vegetation using remote sensing data such as satellite or airborne images. The development of these models has been hindered by a lack of comprehensive ground reference data on structural and spectral characteristics of forests. Here, we reported datasets on the structural and spectral properties of temperate, hemiboreal, and boreal European forest stands. We anticipate that these data will have wide use in remote sensing applications.
Peiyu Cao, Bo Yi, Franco Bilotto, Carlos Gonzalez Fischer, Mario Herrero, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 4557–4572, https://doi.org/10.5194/essd-16-4557-2024, https://doi.org/10.5194/essd-16-4557-2024, 2024
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This article presents a spatially explicit time series dataset reconstructing crop-specific phosphorus fertilizer application rates, timing, and methods at a 4 km × 4 km resolution in the United States from 1850 to 2022. We comprehensively characterized the spatio-temporal dynamics of P fertilizer management over the last 170 years by considering cross-crop variations. This dataset will greatly contribute to the field of agricultural sustainability assessment and Earth system modeling.
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk
Earth Syst. Sci. Data, 16, 4389–4416, https://doi.org/10.5194/essd-16-4389-2024, https://doi.org/10.5194/essd-16-4389-2024, 2024
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Understanding vegetation response to environmental change requires accurate, long-term data on vegetation condition (VC). We evaluated existing satellite VC datasets over Australia and found them lacking, so we developed a new VC dataset for Australia, AusENDVI. It can be used for studying Australia's changing vegetation dynamics and downstream impacts on the carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
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The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
Kelly S. Aho, Kaelin Cawley, Robert Hensley, Robert O. Hall Jr., Walter Dodds, and Keli Goodman
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-330, https://doi.org/10.5194/essd-2024-330, 2024
Revised manuscript accepted for ESSD
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In streams, gas exchange is fundamental to many biogeochemical processes. Gas exchange depends on the degree of saturation and the gas transfer velocity (k). Currently, k is harder to measure than concentration. NEON conducts tracer-gas experiments at 22 streams. Here, we present our processing pipeline to estimate k from these experiments. This dataset (n = 339) represents the largest compilation of standardized k estimates available and captures substantial within- and across-site variability.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
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To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
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This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
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This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
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Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
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Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
João Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara Figueiredo Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
Earth Syst. Sci. Data, 16, 715–729, https://doi.org/10.5194/essd-16-715-2024, https://doi.org/10.5194/essd-16-715-2024, 2024
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Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it is important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge
Earth Syst. Sci. Data, 16, 277–293, https://doi.org/10.5194/essd-16-277-2024, https://doi.org/10.5194/essd-16-277-2024, 2024
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This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
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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Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Wojciech Tylmann, Alicja Bonk, Dariusz Borowiak, Paulina Głowacka, Kamil Nowiński, Joanna Piłczyńska, Agnieszka Szczerba, and Maurycy Żarczyński
Earth Syst. Sci. Data, 15, 5093–5103, https://doi.org/10.5194/essd-15-5093-2023, https://doi.org/10.5194/essd-15-5093-2023, 2023
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We present a dataset from the decade-long monitoring of Lake Żabińskie, a hardwater and eutrophic lake in northeast Poland. The lake contains varved sediments, which form a unique archive of past environmental variability. The monitoring program was designed to capture a pattern of relationships between meteorological conditions, limnological processes, and modern sedimentation and to verify if meteorological and limnological phenomena can be precisely tracked with varves.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
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The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
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Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
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A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
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Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
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Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
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We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
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Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
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The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
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This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
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This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
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Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
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This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
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The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
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The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
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We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
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Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
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This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
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Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
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We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
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Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
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Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
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Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
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Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
Andela, N., Morton, D. C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., van der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, https://doi.org/10.5194/essd-11-529-2019, 2019. a
Araza, A., Herold, M., de Bruin, S., Ciais, P., Gibbs, D. A., Harris, N., Santoro, M., Wigneron, J.-P., Yang, H., Málaga, N., Nesha, K., Rodriguez-Veiga, P., Brovkina, O., Brown, H. C., Chanev, M., Dimitrov, Z., Filchev, L., Fridman, J., García, M., Gikov, A., Govaere, L., Dimitrov, P., Moradi, F., Muelbert, A. E., Novotný, J., Pugh, T. A., Schelhaas, M.-J., Schepaschenko, D., Stereńczak, K., and Hein, L.: Past decade above-ground biomass change comparisons from four multi-temporal global maps, Int. J. Appl. Earth Obs., 118, 103274, https://doi.org/10.1016/j.jag.2023.103274, 2023. a, b, c, d, e
Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., van Dijk, A. I. J. M., Du, J., Kimball, J. S., Vergopolan, N., and Wood, E. F.: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors, Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, 2021. a
Berg, W.: GPM SSMI on F08 Common Calibrated Brightness Temperatures L1C 1.5 hours 13 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SSMI/F08/1C/07, 2021. a
Berg, W., Bilanow, S., Chen, R., Datta, S., Draper, D., Ebrahimi, H., Farrar, S., Jones, W. L., Kroodsma, R., McKague, D., Payne, V., Wang, J., Wilheit, T., and Yang, J. X.: Intercalibration of the GPM microwave radiometer constellation, J. Atmos. Ocean. Tech., 33, 2639–2654, https://doi.org/10.1175/JTECH-D-16-0100.1, 2016. a, b
Berg, W., Kroodsma, R., Kummerow, C. D., and McKague, D. S.: Fundamental climate data records of microwave brightness temperatures, Remote Sens., 10, 1306, https://doi.org/10.3390/rs12040671, 2018. a, b
Bittencourt, P., Rowland, L., Sitch, S., Poyatos, R., Miralles, D. G., and Mencuccini, M.: Bridging Scales: An Approach to Evaluate the Temporal Patterns of Global Transpiration Products Using Tree-Scale Sap Flow Data, J. Geophys. Res.-Biogeo., 128, e2022JG007308, https://doi.org/10.1029/2022JG007308, 2023. a, b
Boulton, C. A., Lenton, T. M., and Boers, N.: Pronounced loss of Amazon rainforest resilience since the early 2000s, Nat. Clim. Change, 12, 271–278, https://doi.org/10.1038/s41558-022-01287-8, 2022. a, b
Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., Prigent, C., Wagner, F. H., and Kerr, Y. H.: Influence of surface water variations on VOD and biomass estimates from passive microwave sensors, Remote Sens. Environ., 257, 112345, https://doi.org/10.1016/j.rse.2021.112345, 2021. a, b
Brandt, M., Yue, Y., Wigneron, J. P., Tong, X., Tian, F., Jepsen, M. R., Xiao, X., Verger, A., Mialon, A., Al-Yaari, A., Wang, K., and Fenshold, R.: Satellite-observed major greening and biomass increase in south China karst during recent decade, Earth's Future, 6, 1017–1028, https://doi.org/10.1016/j.rse.2021.112345, 2018. a
Brown, M. E., Pinzón, J. E., Didan, K., Morisette, J. T., and Tucker, C. J.: Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors, IEEE T. Geosci. Remote, 44, 1787–1793, https://doi.org/10.1109/TGRS.2005.860205, 2006. a
Büechi, P. E., Fischer, M., Grlj, A., Crocetti, L., Trnka, M., and Dorigo, W. A.: Improving predictions of crop yield loss in years of severe droughts by integrating Earth observation and climate data in a machine learning framework. A case study for the Pannonian basin, Conference presentation, 2022. a
Cammalleri, C., McCormick, N., and Toreti, A.: Analysis of the relationship between yield in cereals and remotely sensed fAPAR in the framework of monitoring drought impacts in Europe, Nat. Hazards Earth Syst. Sci., 22, 3737–3750, https://doi.org/10.5194/nhess-22-3737-2022, 2022. a
Chaparro, D., Piles, M., Vall-Llossera, M., Camps, A., Konings, A. G., and Entekhabi, D.: L-band vegetation optical depth seasonal metrics for crop yield assessment, Remote Sens. Environ., 212, 249–259, https://doi.org/10.1016/j.rse.2018.04.049, 2018. a
Chaparro, D., Duveiller, G., Piles, M., Cescatti, A., Vall-Llossera, M., Camps, A., and Entekhabi, D.: Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices, Remote Sens. Environ., 232, 111303, https://doi.org/10.1016/j.rse.2019.111303, 2019. a
Chen, X., Chen, T., He, B., Liu, S., Zhou, S., and Shi, T.: The global greening continues despite increased drought stress since 2000, Global Ecol. Conserv., 49, e02791, https://doi.org/10.1016/j.gecco.2023.e02791, 2024. a
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I.: STL: A seasonal-trend decomposition, J. Off. Stat, 6, 3–73, 1990. a
Crocetti, L., Forkel, M., Fischer, M., Jurečka, F., Grlj, A., Salentinig, A., Trnka, M., Anderson, M., Ng, W.-T., Kokalj, Ž., Bucur, A., and Dorigo, W.: Earth Observation for agricultural drought monitoring in the Pannonian Basin (southeastern Europe): current state and future directions, Reg. Environ. Change, 20, 1–17, https://doi.org/10.1007/s10113-020-01710-w, 2020. a
Dannenberg, M., Wang, X., Yan, D., and Smith, W.: Phenological characteristics of global ecosystems based on optical, fluorescence, and microwave remote sensing, Remote Sens., 12, 671, https://doi.org/10.3390/rs12040671, 2020. a, b, c
de Nijs, A. H., Parinussa, R. M., de Jeu, R. A., Schellekens, J., and Holmes, T. R.: A methodology to determine radio-frequency interference in AMSR2 observations, IEEE T. Geosci. Remote, 53, 5148–5159, https://doi.org/10.1109/TGRS.2015.2417653, 2015. a, b, c
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P., Hirschi, M., de Jeu, R., Kidd, R., Liu, Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017. a, b, c
Dorigo, W., Mösinger, L., van der Schalie, R., Zotta, R.-M., Scanlon, T. M., and De Jeu, R.: Long-term monitoring of vegetation state through passive microwave satellites [in State of the Climate in 2020], B. Am. Meteorol. Soc., 102, S110–S112, https://doi.org/10.1175/2021BAMSStateoftheClimate.1, 2021. a, b
Dorigo, W., Zotta, R., van der Schalie, R., Preimesberger, W., Mösinger, L., and De Jeu, R.: Vegetation Optical Depth [in State of the Climate in 2021], B. Am. Meteorol. Soc., 103, S108–S109, https://doi.org/10.1175/BAMS-D-22-0092.1, 2022. a, b
Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., and Baghdadi, N.: ESA Climate Change Initiative Plus – Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1), Zenodo https://doi.org/10.5281/zenodo.8320869, 2023. a, b, c
Dostálová, A., Wagner, W., Milenković, M., and Hollaus, M.: Annual seasonality in Sentinel-1 signal for forest mapping and forest type classification, Int. J. Remote Sens., 39, 7738–7760, https://doi.org/10.1080/01431161.2018.1479788, 2018. a
Dou, Y., Tian, F., Wigneron, J.-P., Tagesson, T., Du, J., Brandt, M., Liu, Y., Zou, L., Kimball, J. S., and Fensholt, R.: Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics, Remote Sens. Environ., 285, 113390, https://doi.org/10.1016/j.rse.2022.113390, 2023. a, b, c
Draper, D. W., Newell, D. A., Wentz, F. J., Krimchansky, S., and Skofronick-Jackson, G. M.: The global precipitation measurement (GPM) microwave imager (GMI): Instrument overview and early on-orbit performance, IEEE J. Sel. Top. Appl. Earth Obs., 8, 3452–3462, https://doi.org/10.1109/JSTARS.2015.2403303, 2015. a
Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., Mcdonald, K., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl, J.: The soil moisture active passive (SMAP) mission, P. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010. a
Evans, S. G., Small, E., and Larson, K.: Comparison of vegetation phenology in the western USA determined from reflected GPS microwave signals and NDVI, Int. J. Remote Sens., 35, 2996–3017, https://doi.org/10.1080/01431161.2014.894660, 2014. a
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., Saatchi, S. S., Bastos, A., Al-Yaari, A., Hufkens, K., Qin, Y., Xiao, X., Chen, C., Myneni, R. B., Fernandez-Moren, R., Mialon, A., Rodriguez-Fernandez, N. J., Kerr, Y., Tian, F., and Penuelas, J.: Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951, https://doi.org/10.1038/s41477-019-0478-9, 2019. a
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Sitch, S., Yue, C., Bastos, A., Li, X., Qin, Y., Yuan, Q., Schepaschenko, D., Mukhortova, L., Li, X., Liu, X., Mengjia, W., Frappart, F., Xiao, X., Chen, J., Ma, M., Wen, J., Chen, X., Yang, H., Van Wees, D., and Fensholt, R.: Siberian carbon sink reduced by forest disturbances, Nat. Geosci., 16, 56–62, https://doi.org/10.1038/s41561-022-01087-x, 2023. a
Feng, Y., Zeng, Z., Searchinger, T. D., Ziegler, A. D., Wu, J., Wang, D., He, X., Elsen, P. R., Ciais, P., Xu, R., Guo, Z., Peng, L., Tao, Y, Spracklen, D. V., Holden, J., Liu, X., Zheng, Y., Xu, P., Chen, J., Jiang, X., Song, X.-P., Lakshmi, V., Wood, E. F., and Zheng, C.: Doubling of annual forest carbon loss over the tropics during the early twenty-first century, Nat. Sustain., 5, 444–451, https://doi.org/10.1038/s41893-022-00854-3, 2022. a
Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., and Thonicke, K.: A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1), Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, 2017. a
Forkel, M., Dorigo, W., Lasslop, G., Chuvieco, E., Hantson, S., Heil, A., Teubner, I., Thonicke, K., and Harrison, S. P.: Recent global and regional trends in burned area and their compensating environmental controls, Environ. Res. Commun., 1, 051005, https://doi.org/10.1088/2515-7620/ab25d2, 2019. a
Forkel, M., Schmidt, L., Zotta, R.-M., Dorigo, W., and Yebra, M.: Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth, Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, 2023. a
Frappart, F., Wigneron, J.-P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang, M., Moisy, C., Le Masson, E., Aoulad Lafkih, Z., Valle, C., Ygorra, B., and Baghdadi, N.: Global monitoring of the vegetation dynamics from the Vegetation Optical Depth (VOD): A review, Remote Sens., 12, 2915, https://doi.org/10.3390/rs12182915, 2020. a
Gaiser, P.W., St Germain, K. M., Twarog, E. M., Poe, G. A., Purdy, W., Richardson, D., Grossman, W., Jones, W. L., Spencer, D., Golba, G., Cleveland, J., Choy, L., Bevilacqua, R. M., and Chang, P. S.: The WindSat spaceborne polarimetric microwave radiometer: Sensor description and early orbit performance, IEEE T. Geosci. Remote, 42, 2347–2361, https://doi.org/10.1109/TGRS.2004.836867, 2004. a
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple collocation-based merging of satellite soil moisture retrievals, IEEE T. Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070, 2017. a, b
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019. a
Harris, B. L., Taylor, C. M., Weedon, G. P., Talib, J., Dorigo, W., and Van Der Schalie, R.: Satellite-observed vegetation responses to intraseasonal precipitation variability, Geophys. Res. Lett., 49, e2022GL099635, https://doi.org/10.1029/2022GL099635, 2022. a
Harris, N. L., Gibbs, D. A., Baccini, A., Birdsey, R. A., de Bruin, S., Farina, M., Fatoyinbo, L., Hansen, M. C., Herold, M., Houghton, R. A., Potapov, P. V., Requena Suarez, D., Roman-Cuesta, R. M., Saatchi, S. S., Slay, C. M., Turubanova, S. A., and Tyukavina, A.: Global maps of twenty-first century forest carbon fluxes, Nat. Clim. Change, 11, 234–240, https://doi.org/10.1038/s41558-020-00976-6, 2021. a
Holmes, T., De Jeu, R., Owe, M., and Dolman, A.: Land surface temperature from Ka band (37 GHz) passive microwave observations, J. Geophys. Res.-Atmos., 114, D04113, https://doi.org/10.1029/2008JD010257, 2009. a
Jackson, T. J.: III. Measuring surface soil moisture using passive microwave remote sensing, Hydrol. Process., 7, 139–152, 1993. a
Jones, M. O., Jones, L. A., Kimball, J. S., and McDonald, K. C.: Satellite passive microwave remote sensing for monitoring global land surface phenology, Remote Sens. Environ., 115, 1102–1114, 2011. a
Jones, M. O., Kimball, J. S., Jones, L. A., and McDonald, K. C.: Satellite passive microwave detection of North America start of season, Remote Sens. Environ., 123, 324–333, 2012. a
Kawanishi, T., Sezai, T., Ito, Y., Imaoka, K., Takeshima, T., Ishido, Y., Shibata, A., Miura, M., Inahata, H., and Spencer, R. W.: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies, IEEE T. Geosci. Remote, 41, 184–194, 2003. a
Kerr, Y. H. and Njoku, E. G.: A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space, IEEE T. Geosci. Remote, 28, 384–393, 1990. a
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S., Drinkwater, M., Hahne, A., Martin-Neira, M., and Macklenburg, S.: The SMOS mission: New tool for monitoring key elements ofthe global water cycle, P. IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010. a
Kim, S., Pham, H. T., Liu, Y. Y., Marshall, L., and Sharma, A.: Improving the combination of satellite soil moisture data sets by considering error cross correlation: A comparison between triple collocation (TC) and extended double instrumental variable (EIVD) alternatives, IEEE T. Geosci. Remote, 59, 7285–7295, 2020. a, b
Knowles, K., Savoie, M., Armstrong, R., and Brodzik, M. J.: AMSR-E/Aqua Daily EASE-Grid Brightness Temperatures, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/XIMNXRTQVMOX, 2006. a
Konings, A. G. and Gentine, P.: Global variations in ecosystem-scale isohydricity, Glob. Change Biol., 23, 891–905, 2017. a
Konings, A. G., Rao, K., and Steele-Dunne, S. C.: Macro to micro: microwave remote sensing of plant water content for physiology and ecology, New Phytol., 223, 1166–1172, 2019. a
Kumar, S. V., Holmes, T., Andela, N., Dharssi, I., Vinodkumar, Hain, C., Peters-Lidard, C., Mahanama, S. P., Arsenault, K. R., Nie, W., and Getirana, A.: The 2019–2020 Australian drought and bushfires altered the partitioning of hydrological fluxes, Geophys. Res. Lett., 48, e2020GL091411, https://doi.org/10.1029/2020GL091411, 2021. a
Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The tropical rainfall measuring mission (TRMM) sensor package, J. Atmos. Ocean. Tech., 15, 809–817, 1998. a
Larson, K. M. and Small, E. E.: Normalized microwave reflection index: A vegetation measurement derived from GPS networks, IEEE J. Sel. Top. Appl. Earth Obs., 7, 1501–1511, 2014. a
Li, X., Wigneron, J.-P., Frappart, F., Fan, L., Wang, M., Liu, X., Al-Yaari, A., and Moisy, C.: Development and validation of the SMOS-IC version 2 (V2) soil moisture product, in: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 4434–4437, https://doi.org/10.1109/IGARSS39084.2020.9323324, 2020. a, b
Li, X., Wigneron, J.-P., Frappart, F., Fan, L., Ciais, P., Fensholt, R., Entekhabi, D., Brandt, M., Konings, A. G., Liu, X., Wang, M., Al-Yaari, A. , and Moisy, C.: Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products, Remote Sens. Environ., 253, 112208, https://doi.org/10.1016/j.rse.2020.112208, 2021. a, b, c, d, e, f, g, h
Li, X., Wigneron, J.-P., Frappart, F., De Lannoy, G., Fan, L., Zhao, T., Gao, L., Tao, S., Ma, H., Peng, Z., Liu, X., Wang, H., Wang, M., Moisy, C., and Ciais, P.: The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations, Remote Sens. Environ., 282, 113272, https://doi.org/10.1016/j.rse.2022.113272, 2022. a
Liu, X., Wigneron, J.-P., Wagner, W., Frappart, F., Fan, L., Vreugdenhil, M., Baghdadi, N., Zribi, M., Jagdhuber, T., Tao, S., Li, X., Wang, H., Wang, M., Bai, X., Mousa, B. G., and Ciais, P.: A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison, Remote Sens. Environ., 299, 113850, https://doi.org/10.1016/j.rse.2023.113850, 2023. a
Liu, Y. Y., De Jeu, R. A., McCabe, M. F., Evans, J. P., and Van Dijk, A. I.: Global long-term passive microwave satellite-based retrievals of vegetation optical depth, Geophys. Res. Lett., 38, L18402, https://doi.org/10.1029/2011GL048684, 2011. a, b, c
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a, b, c
Mason, P., Zillman, J., Simmons, A., Lindstrom, E., Harrison, D., Dolman, H., Bojinski, S., Fischer, A., Latham, J., and Rasmussen, J.: Implementation plan for the global observing system for climate in support of the UNFCCC (2010 Update), http://www.ecopuerto.com/bicentenario/informes/PlanClima-Cop15.pdf (last access: 12 January 2024), 2010. a
Mateo-Sanchis, A., Piles, M., Muñoz-Marí, J., Adsuara, J. E., Pérez-Suay, A., and Camps-Valls, G.: Synergistic integration of optical and microwave satellite data for crop yield estimation, Remote Sens. Environ., 234, 111460, https://doi.org/10.1016/j.rse.2019.111460, 2019. a
Markus, T., Comiso, J. C., and Meier, W. N.: AMSR-E/AMSR2 Unified L3 Daily 25 km Brightness Temperatures & Sea Ice Concentration Polar Grids, Version 1 Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/TRUIAL3WPAUP, 2018. a
Melzer, T.: Vegetation modelling in WARP 6.0, Proceedings of the EUMETSAT Meteorological Satellite Conference, Vienna, Austria, https://www-cdn.eumetsat.int/files/2020-04/pdf_conf_p_s1_07_melzer_v.pdf (last access: 3 december 2022), 2013. a
Mialon, A., Rodríguez-Fernández, N. J., Santoro, M., Saatchi, S., Mermoz, S., Bousquet, E., and Kerr, Y. H.: Evaluation of the sensitivity of SMOS L-VOD to forest above-ground biomass at global scale, Remote Sens., 12, 1450, https://doi.org/10.3390/rs12091450, 2020. a, b, c
Mladenova, I., Jackson, T., Njoku, E., Bindlish, R., Chan, S., Cosh, M., Holmes, T., De Jeu, R., Jones, L., Kimball, J., Paloscia, S., and Santi, E.: Remote monitoring of soil moisture using passive microwave-based techniques—Theoretical basis and overview of selected algorithms for AMSR-E, Remote Sens. Environ., 144, 197–213, https://doi.org/10.1016/j.rse.2014.01.013, 2014. a
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Moesinger, L., Zotta, R.-M., van der Schalie, R., Scanlon, T., de Jeu, R., and Dorigo, W.: Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI), Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, 2022. a, b, c, d, e, f
Momen, M., Wood, J. D., Novick, K. A., Pangle, R., Pockman, W. T., McDowell, N. G., and Konings, A. G.: Interacting effects of leaf water potential and biomass on vegetation optical depth, J. Geophys. Res.-Biogeo., 122, 3031–3046, 2017. a
Mukunga, T., Forkel, M., Forrest, M., Zotta, R.-M., Pande, N., Schlaffer, S., and Dorigo, W.: Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence, Fire, 6, 197, https://doi.org/10.3390/fire6050197, 2023. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b
Myneni, R. and Park, T.: MODIS/Terra+ Aqua Leaf Area Index/FPAR 4-Day L4 Global 500 m SIN Grid V061, The Land Processes Distributed Active Archive Center (LP DAAC): Sioux Falls, SD, USA, https://doi.org/10.5067/MODIS/MCD15A3H.061, 2021. a
Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G., Lotsch A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R., and Running, S. W.: Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data, Remote Sens. Environ., 83, 214–231, https://doi.org/10.1016/S0034-4257(02)00074-3, 2002. a
Myneni, R. B., Yang, W., Nemani, R. R., Huete, A. R., Dickinson, R. E., Knyazikhin, Y., Didan, K., Fu, R., Negrón Juárez, R. I., Saatchi, S. S., Hashimoto, H., Ichii, K., Shabanov, N. V., Tan, B., Ratana, P., Privette, J. L., Morisette, J. T., Vermote, E. F., Roy, D. P., Wolfe, R. E., Friedl, M. A., Running, S. W., Votava, P., El-Saleous, N., Devadiga, S., Su, Y., and Salomonson, V. V.: Large seasonal swings in leaf area of Amazon rainforests, P. Natl. Acad. Sci. USA, 104, 4820–4823, https://doi.org/10.1073/pnas.0611338104, 2007. a
Oliva, R., Daganzo, E., Kerr, Y. H., Mecklenburg, S., Nieto, S., Richaume, P., and Gruhier, C.: SMOS radio frequency interference scenario: Status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band, IEEE T. Geosci. Remote, 50, 1427–1439, 2012. a
Olivares-Cabello, C., Chaparro, D., Vall-llossera, M., and Camps, A.: Monitoring Forest Above-Ground Biomass from Multifrequency Vegetation Optical Depth: A Preliminary Study, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6012–6015, https://doi.org/10.1109/IGARSS47720.2021.9553770, 2021. a, b
Osakabe, Y., Osakabe, K., Shinozaki, K., and Tran, L.-S. P.: Response of plants to water stress, Front. Plant Sci., 5, https://doi.org/10.3389/fpls.2014.00086, 2014. a
Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index, IEEE T. Geosci. Remote, 39, 1643–1654, 2001. a
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of satellite-derived global land surface moisture, J. Geophys. Res.-Earth, 113, F01002, https://doi.org/10.1029/2007JF000769, 2008. a, b, c, d
Pearson, R. K.: Outliers in process modeling and identification, IEEE T. Control Syst. Tech., 10, 55–63, 2002. a
Petchiappan, A., Steele-Dunne, S. C., Vreugdenhil, M., Hahn, S., Wagner, W., and Oliveira, R.: The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon, Hydrol. Earth Syst. Sci., 26, 2997–3019, https://doi.org/10.5194/hess-26-2997-2022, 2022. a, b
Pfeil, I., Wagner, W., Forkel, M., Dorigo, W., and Vreugdenhil, M.: Does ASCAT observe the spring reactivation in temperate deciduous broadleaf forests?, Remote Sens. Environ., 250, 112042, https://doi.org/10.1016/j.rse.2020.112042, 2020. a
Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W., Chen, A., Ciais, P., Tømmervik, H., Nemani, R. R., and and Myneni, R. B.: Characteristics, drivers and feedbacks of global greening, Nat. Rev. Earth Environ., 1, 14–27, https://doi.org/10.1038/s43017-019-0001-x, 2020. a
Poyatos, R., Granda, V., Flo, V., Adams, M. A., Adorján, B., Aguadé, D., Aidar, M. P. M., Allen, S., Alvarado-Barrientos, M. S., Anderson-Teixeira, K. J., Aparecido, L. M., Arain, M. A., 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, M., 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., Isarangkool Na Ayutthaya, S., Jarvis, P. G., 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, Á., 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., Oren, R., Ourcival, J.-M., Paljakka, T., Perez-Priego, O., Peri, P. L., Peters, R. L., Pfautsch, S., Pockman, W. T., Preisler, Y., Rascher, K., Robinson, G., Rocha, H., Rocheteau, A., Röll, A., Rosado, B. H. P., Rowland, L., Rubtsov, A. V., Sabaté, S., Salmon, Y., Salomón, R. L., Sánchez-Costa, E., Schäfer, K. V. R., Schuldt, B., Shashkin, A., Stahl, C., Stojanović, M., Suárez, J. C., Sun, G., Szatniewska, J., Tatarinov, F., Tesař, 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., Werner, W., Wieser, G., Wingate, L., Wullschleger, S., Yi, K., Zweifel, R., Steppe, K., Mencuccini, M., and Martínez-Vilalta, J.: Global transpiration data from sap flow measurements: the SAPFLUXNET database, Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, 2021. a
Qin, Y., Xiao, X., Wigneron, J.-P., Ciais, P., Brandt, M., Fan, L., Li, X., Crowell, S., Wu, X., Doughty, R., Zhang, Y., Liu, F., Sitch, S., and Moore III, B.: Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon, Nat. Clim. Change, 11, 442–448, https://doi.org/10.1038/s41558-021-01026-5, 2021. a, b
Rodríguez-Fernández, N. J., Mialon, A., Mermoz, S., Bouvet, A., Richaume, P., Al Bitar, A., Al-Yaari, A., Brandt, M., Kaminski, T., Le Toan, T., Kerr, Y. H., and Wigneron, J.-P.: An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa, Biogeosciences, 15, 4627–4645, https://doi.org/10.5194/bg-15-4627-2018, 2018. a, b, c, d
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Nat. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011. a
Santoro, M. and Cartus, O.: ESA biomass climate change initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2, Cent. Environ. Data Anal, https://doi.org/10.5285/af60720c1e404a9e9d2c145d2b2ead4, 2021. a
Sawada, Y., Tsutsui, H., Koike, T., Rasmy, M., Seto, R., and Fujii, H.: A field verification of an algorithm for retrieving vegetation water content from passive microwave observations, IEEE T. Geosci. Remote, 54, 2082–2095, 2015. a
Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, 2023. a, b, c, d, e
Small, E. E., Roesler, C. J., and Larson, K. M.: Vegetation response to the 2012–2014 California drought from GPS and optical measurements, Remote Sens., 10, 630, https://doi.org/10.3390/rs10040630, 2018. a
Smith, T., Zotta, R.-M., Boulton, C. A., Lenton, T. M., Dorigo, W., and Boers, N.: Reliability of resilience estimation based on multi-instrument time series, Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, 2023. a, b, c
Tagesson, T., Tian, F., Schurgers, G., Horion, S., Scholes, R., Ahlström, A., Ardö, J., Moreno, A., Madani, N., Olin, S., and Fensholt, R.: A physiology-based Earth observation model indicates stagnation in the global gross primary production during recent decades, Glob. Change Biol., 27, 836–854, https://doi.org/10.1111/gcb.15424, 2021. a
Teubner, I. E., Forkel, M., Jung, M., Liu, Y. Y., Miralles, D. G., Parinussa, R., Van der Schalie, R., Vreugdenhil, M., Schwalm, C. R., Tramontana, G., Camps-Valls, G., and Dorigo, W. A.: Assessing the relationship between microwave vegetation optical depth and gross primary production, Int. J. Appl. Earth Obs., 65, 79–91, https://doi.org/10.1016/j.jag.2017.10.006, 2018. a, b
Teubner, I. E., Forkel, M., Camps-Valls, G., Jung, M., Miralles, D. G., Tramontana, G., Van der Schalie, R., Vreugdenhil, M., Mösinger, L., and Dorigo, W. A.: A carbon sink-driven approach to estimate gross primary production from microwave satellite observations, Remote Sens. Environ., 229, 100–113, 2019. a
Teubner, I. E., Forkel, M., Wild, B., Mösinger, L., and Dorigo, W.: Impact of temperature and water availability on microwave-derived gross primary production, Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, 2021. a
Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., and Wang, Y.: Evaluating temporal consistency of long-term global NDVI datasets for trend analysis, Remote Sens. Environ., 163, 326–340, 2015. a
Tian, F., Wigneron, J.-P., Ciais, P., Chave, J., Ogée, J., Peñuelas, J., Ræbild, A., Domec, J.-C., Tong, X., Brandt, M., Mialon, A., Rodriguez-Fernandez, N., Tagesson, T., Al-Yaari, A., Kerr, Y., Chen, C., Myneni, R. B., Zhang, W., Ardö, J., and Fensholt, R.: Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite, Nat. Ecol. Evol., 2, 1428–1435, https://doi.org/10.1038/s41559-018-0630-3, 2018. a
Van der Schalie, R., de Jeu, R. A., Kerr, Y. H., Wigneron, J.-P., Rodríguez-Fernández, N. J., Al-Yaari, A., Parinussa, R. M., Mecklenburg, S., and Drusch, M.: The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E, Remote Sens. Environ., 189, 180–193, 2017. a, b, c, d
van der Schalie, R., van der Vliet, M., Rodríguez-Fernández, N., Dorigo, W. A., Scanlon, T., Preimesberger, W., Madelon, R., and de Jeu, R. A.: L-band soil moisture retrievals using microwave based temperature and filtering. Towards model-independent climate data records, Remote Sens., 13, 2480, https://doi.org/10.3390/rs13132480, 2021. a
van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., and Dorigo, W.: Reconciling flagging strategies for multi-sensor satellite soil moisture climate data records, Remote Sens., 12, 3439, https://doi.org/10.3390/rs12203439, 2020. a, b, c
Van Dijk, A. I., Beck, H. E., Crosbie, R. S., De Jeu, R. A., Liu, Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, 1040–1057, 2013. a
Vandegehuchte, M. W. and Steppe, K.: Corrigendum to: Sap-flux density measurement methods: working principles and applicability, Funct. Plant Biol., 40, 1088–1088, 2013. a
Vreugdenhil, M., Navacchi, C., Bauer-Marschallinger, B., Hahn, S., Steele-Dunne, S., Pfeil, I., Dorigo, W., and Wagner, W.: Sentinel-1 cross ratio and vegetation optical depth: A comparison over Europe, Remote Sens., 12, 3404, https://doi.org/10.3390/rs12203404, 2020. a, b, c
Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo, W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., and Wagner, W.: Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges, Front. Water, 4, 1045451, https://doi.org/10.3389/frwa.2022.1045451, 2022. a, b
Wagner, W., Lindorfer, R., Melzer, T., Hahn, S., Bauer-Marschallinger, B., Morrison, K., Calvet, J.-C., Hobbs, S., Quast, R., Greimeister-Pfeil, I., and Vreugdenhil, M.: Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering, Remote Sens. Environ., 276, 113025, https://doi.org/10.1016/j.rse.2022.113025, 2022. a
Wentz, F. J.: A well-calibrated ocean algorithm for special sensor microwave/imager, J. Geophys. Res.-Oceans, 102, 8703–8718, 1997. a
Wigneron, J.-P., Kerr, Y.,Waldteufel, P., Saleh, K., Escorihuela, M.-J., Richaume, P., Ferrazzoli, P., De Rosnay, P., Gurney, R., Calvet, J.-C., Grant, J. P., Guglielmetti, M., Hornbuckle, B., Mätzler, C., Pellarin, T., and Schwank, M.: L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields, Remote Sens. Environ., 107, 639–655, 2007. a
Wigneron, J.-P., Jackson, T. J., O'Neill, P., De Lannoy, G., de Rosnay, P., Walker, J. P., Ferrazzoli, P., Mironov, V., Bircher, S., Grant, J. P., Kurum, M., Schwank, M., Munoz-Sabater, J., Das, N., Royer, A., Al-Yaari, A., Al Bitar, A., Fernandez-Moran, R., Lawrence, H., Mialon, A., and Kerr, Y.: Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms, Remote Sens. Environ., 192, 238–262, https://doi.org/10.1016/j.rse.2017.01.024, 2017. a
Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G., Liu, X., Wang, M., Le Masson, E., and Moisy, C.: SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives, Remote Sens. Environ., 254, 112238, https://doi.org/10.1016/j.rse.2020.112238, 2021. a, b, c, d
Wild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der Schalie, R., Sitch, S., and Dorigo, W.: VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing, Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, 2022. a, b, c
World Meteorological Organization: WMO guidelines on the calculation of climate normals, 18, WMO-No. 1203, 2017. a
Xu, L., Saatchi, S. S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A. A., Bowman, K., Worden, J., Liu, J., Yin, Y., Domke, G., McRoberts, R. E., Woodall, C., Nabuurs, G.-J., de-Miguel, S., Keller, M., Harris, N., Maxwell, S., and Schime, D.: Changes in global terrestrial live biomass over the 21st century, Sci. Adv., 7, eabe9829, https://doi.org/10.1126/sciadv.abe9829, 2021. a
Yang, H., Ciais, P., Wigneron, J.-P., Chave, J., Cartus, O., Chen, X., Fan, L., Green, J. K., Huang, Y., Joetzjer, E., Kay, H., Makowski, D., Maignan, F., Santoro, M., Tao, S., Liu, L., and Yao, Y.: Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño, P. Natl. Acad. Sci. USA, 119, e2101388119, https://doi.org/10.1073/pnas.2101388119, 2022. a
Yang, H., Ciais, P., Frappart, F., Li, X., Brandt, M., Fensholt, R., Fan, L., Saatchi, S., Besnard, S., Deng, Z., Bowring, S., and Wigneron, J. P.: Global increase in biomass carbon stock dominated by growth of northern young forests over past decade, Nat. Geosci., 16, 886–892, https://doi.org/10.1038/s41561-023-01274-4, 2023. a
Zhang, Y., Song, C., Band, L. E., Sun, G., and Li, J.: Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?, Remote Sens. Environ., 191, 145–155, 2017. a
Zotta, R.-M., Moesinger, L., van der Schalie, R., Preimesberger, W., Frederikse, T., De Jeu, R., and Dorigo, W.: VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring (1.0.0), TU Wien [data set], https://doi.org/10.48436/7sjqa-fyw66, 2024. a, b
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA...
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