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Markus Reichstein
2010 Nature  
A biogeochemist looks at where all the emitted carbon dioxide is going.
doi:10.1038/464145e pmid:20220802 fatcat:cmxatnbdj5fkjhkrhrn2q5g6tm

Terrestrial ecosystem carbon dynamics and climate feedbacks

Martin Heimann, Markus Reichstein
2008 Nature  
) ■ Martin Heimann and Markus Reichstein are the Max Planck Institute for Biogeochemistry, Hans-Knöll-Strasse 10, D-07745 Jena, Germany.  ...  Reichstein Recent evidence suggests that, on a global scale, terrestrial ecosystems will provide a positive feedback in a warming world, albeit of uncertain magnitude. effect on respiration exceeds the  ... 
doi:10.1038/nature06591 pmid:18202646 fatcat:c7rd7h6iovdovj6utgiv432jv4

Predicting Landscapes from Environmental Conditions Using Generative Networks [article]

Christian Requena-Mesa, Markus Reichstein, Miguel Mahecha, Basil Kraft, Joachim Denzler
2019 arXiv   pre-print
Landscapes are meaningful ecological units that strongly depend on the environmental conditions. Such dependencies between landscapes and the environment have been noted since the beginning of Earth sciences and cast into conceptual models describing the interdependencies of climate, geology, vegetation and geomorphology. Here, we ask whether landscapes, as seen from space, can be statistically predicted from pertinent environmental conditions. To this end we adapted a deep learning generative
more » ... odel in order to establish the relationship between the environmental conditions and the view of landscapes from the Sentinel-2 satellite. We trained a conditional generative adversarial network to generate multispectral imagery given a set of climatic, terrain and anthropogenic predictors. The generated imagery of the landscapes share many characteristics with the real one. Results based on landscape patch metrics, indicative of landscape composition and structure, show that the proposed generative model creates landscapes that are more similar to the targets than the baseline models while overall reflectance and vegetation cover are predicted better. We demonstrate that for many purposes the generated landscapes behave as real with immediate application for global change studies. We envision the application of machine learning as a tool to forecast the effects of climate change on the spatial features of landscapes, while we assess its limitations and breaking points.
arXiv:1909.10296v1 fatcat:3ys6dhtp4ja5xmzlb3ajncfoo4

The Low Dimensionality of Development

Guido Kraemer, Markus Reichstein, Gustau Camps-Valls, Jeroen Smits, Miguel D. Mahecha
2020 Social Indicators Research  
The World Bank routinely publishes over 1500 "World Development Indicators" to track the socioeconomic development at the country level. A range of indices has been proposed to interpret this information. For instance, the "Human Development Index" was designed to specifically capture development in terms of life expectancy, education, and standard of living. However, the general question which independent dimensions are essential to capture all aspects of development still remains open. Using
more » ... nonlinear dimensionality reduction approach we aim to extract the core dimensions of development in a highly efficient way. We find that more than 90% of variance in the WDIs can be represented by solely five uncorrelated dimensions. The first dimension, explaining 74% of variance, represents the state of education, health, income, infrastructure, trade, population, and pollution. Although this dimension resembles the HDI, it explains much more variance. The second dimension (explaining 10% of variance) differentiates countries by gender ratios, labor market, and energy production patterns. Here, we differentiate societal structures when comparing e.g. countries from the Middle-East to the Post-Soviet area. Our analysis confirms that most countries show rather consistent temporal trends towards wealthier and aging societies. We can also find deviations from the long-term trajectories during warfare, environmental disasters, or fundamental political changes. The data-driven nature of the extracted dimensions complements classical indicator approaches, allowing a broader exploration of global development space. The extracted independent dimensions represent different aspects of development that need to be considered when proposing new metric indices.
doi:10.1007/s11205-020-02349-0 fatcat:etnywomiuvfgrdkvpjv3yvt3zm

Carbon–Water Flux Coupling Under Progressive Drought

Sven Boese, Martin Jung, Nuno Carvalhais, Adriaan J. Teuling, Markus Reichstein
2018 Biogeosciences Discussions  
Eddy-covariance GPP values were obtained with the flux partitioning method of Reichstein et al. (2005) .  ... 
doi:10.5194/bg-2018-474 fatcat:3feqnirsunfohlhdll4uc7mr3q

Carbon–water flux coupling under progressive drought

Sven Boese, Martin Jung, Nuno Carvalhais, Adriaan J. Teuling, Markus Reichstein
2019 Biogeosciences  
Eddy-covariance GPP values were obtained with the flux partitioning method of Reichstein et al. (2005) .  ... 
doi:10.5194/bg-16-2557-2019 fatcat:f4s5pbpvbnbfzklanukyb32foi

dimRed and coRanking - Unifying Dimensionality Reduction in R

Guido Kraemer, Markus Reichstein, Miguel,D. Mahecha
2018 The R Journal  
Dimensionality reduction" (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. DR can be realized by a plethora of methods with different properties, objectives, and, hence, (dis)advantages. The resulting low-dimensional data embeddings are often difficult to compare with objective criteria. Here, we introduce the dimRed and coRanking packages for the R language. These open source software
more » ... enable users to easily access multiple classical and advanced DR methods using a common interface. The packages also provide quality indicators for the embeddings and easy visualization of high dimensional data. The coRanking package provides the functionality for assessing DR methods in the co-ranking matrix framework. In tandem, these packages allow for uncovering complex structures high dimensional data. Currently 15 DR methods are available in the package, some of which were not previously available to R users. Here, we outline the dimRed and coRanking packages and make the implemented methods understandable to the interested reader.
doi:10.32614/rj-2018-039 fatcat:knheeiowvzdqferkus5giwhnzy

The importance of radiation for semiempirical water-use efficiency models

Sven Boese, Martin Jung, Nuno Carvalhais, Markus Reichstein
2017 Biogeosciences  
., 2006; Reichstein et al., 2005) to assure consistent quality of the observations. Eddy covariance GPP results were based on the flux partitioning method of Reichstein (2005) .  ... 
doi:10.5194/bg-14-3015-2017 fatcat:vtqilqjaobagnd2z47uc4vgtzy

Ecosystem physio-phenology revealed using circular statistics

Daniel E. Pabon-Moreno, Talie Musavi, Mirco Migliavacca, Markus Reichstein, Christine Römermann, Miguel D. Mahecha
2020 Biogeosciences  
From the dataset we use the GPP data that were derived using the nighttime partitioning method and considering the threshold of the variable u * to discriminate values of insufficient turbulence (Reichstein  ... 
doi:10.5194/bg-17-3991-2020 fatcat:42kko7v7lvcall3yl3xngsu66m

EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts [article]

Christian Requena-Mesa, Vitus Benson, Joachim Denzler, Jakob Runge, Markus Reichstein
2020 arXiv   pre-print
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth's surface at such local scales remains unresolved by current physical models. Large Earth observation datasets now enable us to create machine learning models capable
more » ... f translating coarse weather information into high-resolution Earth surface forecasts. Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts. Video prediction has been tackled with deep learning models. Developing such models requires analysis-ready datasets. We introduce EarthNet2021, a new, curated dataset containing target spatio-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables. With over 32000 samples it is suitable for training deep neural networks. Comparing multiple Earth surface forecasts is not trivial. Hence, we define the EarthNetScore, a novel ranking criterion for models forecasting Earth surface reflectance. For model intercomparison we frame EarthNet2021 as a challenge with four tracks based on different test sets. These allow evaluation of model validity and robustness as well as model applicability to extreme events and the complete annual vegetation cycle. In addition to forecasting directly observable weather impacts through satellite-derived vegetation indices, capable Earth surface models will enable downstream applications such as crop yield prediction, forest health assessments, coastline management, or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech .
arXiv:2012.06246v1 fatcat:m7a32p22zbht5mpfxjdo46t3eu

Summarizing the state of the terrestrial biosphere in few dimensions

Guido Kraemer, Gustau Camps-Valls, Markus Reichstein, Miguel D. Mahecha
2019 Biogeosciences Discussions  
., 2011; Reichstein et al., 2013) which are yet to be fully understood Sippel et al., 2018) .  ... 
doi:10.5194/bg-2019-307 fatcat:ihuqomif4redhcosftmhtq45oi

The importance of radiation for semi-empirical water-use efficiency models

Sven Boese, Martin Jung, Nuno Carvalhais, Markus Reichstein
2017 Biogeosciences Discussions  
., 2006; Reichstein et al., 2005) to assure 15 consistent quality of the observations. Eddy-covariance GPP results were based on the flux partitioning method of Reichstein (2005) .  ... 
doi:10.5194/bg-2016-524 fatcat:iczlggtvcff7dlzgqrjuy5hhce

GO WIDE TO GO DEEP: AFFORDABLE, REPLICABLE ROBOTIC MINIRHIZOTRON SAMPLING FOR PHENOLOGY STUDIES [article]

Richard Nair, Martin Strube, Martin Hertel, Olaf Kolle, Markus Reichstein, Mirco Migliavacca
2022 bioRxiv   pre-print
Minirhizotrons (paired camera systems and buried observatories) are the best current method to make repeatable measurements of fine roots in the field. Automating the technique is also the only way to gather high resolution data necessary for comparison with phenology-relevant above-ground remote sensing, and, when appropriately validated, to assess with high temporal resolution belowground biomass, which can support carbon budgets estimates. Minirhizotron technology has been available for half
more » ... a century but there are many challenges to automating the technique for global change experiments. Instruments must be cheap enough to replicate on field scales given their shallow field of view, and automated analysis must both be robust to changeable soil and root conditions because ultimately, image properties extracted from minirhizotrons must have biological meaning. Both digital photography and computer technology are rapidly evolving, with huge potential for generating belowground data from images using modern technological advantages. Here we demonstrate a homemade automatic minirhizotron scheme, built with off-the-shelf parts and sampling every two hours, which we paired with a neural network-based image analysis method in a proof-of-concept mesocosm study. We show that we are able to produce a robust daily timeseries of root cover dynamics. The method is applied at the same model across multiple instruments demonstrating good reproducibility of the measurements and a good pairing with an above-ground vegetation index and root biomass recovery through time. We found a sensitivity of the root cover we extracted to soil moisture conditions and time of day (potentially relating to soil moisture), which may only be an issue with high resolution automated imagery and not commonly reported as encountered when training neural networks on traditional, time-distinct minirhizotron studies. We discuss potential avenues for dealing with such issues in future field applications of such devices. If such issues are dealt with to a satisfactory manner in the field, automated timeseries of root biomass and traits from replicated instruments could add a new dimension to phenology understanding at ecosystem level by understanding the dynamics of root properties and traits.
doi:10.1101/2022.01.06.475082 fatcat:jhr4chpvgrhbrh3rsxupw5kjpu

Widespread inhibition of daytime ecosystem respiration

Trevor F. Keenan, Mirco Migliavacca, Dario Papale, Dennis Baldocchi, Markus Reichstein, Margaret Torn, Thomas Wutzler
2019 Nature Ecology & Evolution  
The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however,
more » ... ests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.
doi:10.1038/s41559-019-0809-2 pmid:30742107 pmcid:PMC6421340 fatcat:vvog5vg5zrcthguk532gybfmh4

Summarizing the state of the terrestrial biosphere in few dimensions

Guido Kraemer, Gustau Camps-Valls, Markus Reichstein, Miguel D. Mahecha
2020 Biogeosciences  
Mahecha and Markus Reichstein have been supported by the Horizon 2020 EU project BACI under grant agreement no. 640176.  ...  ., 2011; Reichstein et al., 2013) .  ... 
doi:10.5194/bg-17-2397-2020 fatcat:t3cbkasa75av5hypelojlshqpe
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