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  • 1
    Online Resource
    Online Resource
    Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. (2021)
    Newark :John Wiley & Sons, Incorporated,
    Details
    Keywords: Earth sciences-Study and teaching. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (435 pages)
    Edition: 1st ed.
    ISBN: 9781119646150
    URL: https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=6706281
    DDC: 550.285631
    Language: English
    Note: Cover -- Title Page -- Copyright -- Contents -- Foreword -- Acknowledgments -- List of Contributors -- List of Acronyms -- Chapter 1 Introduction -- 1.1 A Taxonomy of Deep Learning Approaches -- 1.2 Deep Learning in Remote Sensing -- 1.3 Deep Learning in Geosciences and Climate -- 1.4 Book Structure and Roadmap -- Part I Deep Learning to Extract Information from Remote Sensing Images -- Chapter 2 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks -- 2.1 Introduction -- 2.2 Sparse Unsupervised Convolutional Networks -- 2.2.1 Sparsity as the Guiding Criterion -- 2.2.2 The EPLS Algorithm -- 2.2.3 Remarks -- 2.3 Applications -- 2.3.1 Hyperspectral Image Classification -- 2.3.2 Multisensor Image Fusion -- 2.4 Conclusions -- Chapter 3 Generative Adversarial Networks in the Geosciences -- 3.1 Introduction -- 3.2 Generative Adversarial Networks -- 3.2.1 Unsupervised GANs -- 3.2.2 Conditional GANs -- 3.2.3 Cycle‐consistent GANs -- 3.3 GANs in Remote Sensing and Geosciences -- 3.3.1 GANs in Earth Observation -- 3.3.2 Conditional GANs in Earth Observation -- 3.3.3 CycleGANs in Earth Observation -- 3.4 Applications of GANs in Earth Observation -- 3.4.1 Domain Adaptation Across Satellites -- 3.4.2 Learning to Emulate Earth Systems from Observations -- 3.5 Conclusions and Perspectives -- Chapter 4 Deep Self‐taught Learning in Remote Sensing -- 4.1 Introduction -- 4.2 Sparse Representation -- 4.2.1 Dictionary Learning -- 4.2.2 Self‐taught Learning -- 4.3 Deep Self‐taught Learning -- 4.3.1 Application Example -- 4.3.2 Relation to Deep Neural Networks -- 4.4 Conclusion -- Chapter 5 Deep Learning‐based Semantic Segmentation in Remote Sensing -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Basics on Deep Semantic Segmentation: Computer Vision Models -- 5.3.1 Architectures for Image Data. , 5.3.2 Architectures for Point‐clouds -- 5.4 Selected Examples -- 5.4.1 Encoding Invariances to Train Smaller Models: The example of Rotation -- 5.4.2 Processing 3D Point Clouds as a Bundle of Images: SnapNet -- 5.4.3 Lake Ice Detection from Earth and from Space -- 5.5 Concluding Remarks -- Chapter 6 Object Detection in Remote Sensing -- 6.1 Introduction -- 6.1.1 Problem Description -- 6.1.2 Problem Settings of Object Detection -- 6.1.3 Object Representation in Remote Sensing -- 6.1.4 Evaluation Metrics -- 6.1.4.1 Precision‐Recall Curve -- 6.1.4.2 Average Precision and Mean Average Precision -- 6.1.5 Applications -- 6.2 Preliminaries on Object Detection with Deep Models -- 6.2.1 Two‐stage Algorithms -- 6.2.1.1 R‐CNNs -- 6.2.1.2 R‐FCN -- 6.2.2 One‐stage Algorithms -- 6.2.2.1 YOLO -- 6.2.2.2 SSD -- 6.3 Object Detection in Optical RS Images -- 6.3.1 Related Works -- 6.3.1.1 Scale Variance -- 6.3.1.2 Orientation Variance -- 6.3.1.3 Oriented Object Detection -- 6.3.1.4 Detecting in Large‐size Images -- 6.3.2 Datasets and Benchmark -- 6.3.2.1 DOTA -- 6.3.2.2 VisDrone -- 6.3.2.3 DIOR -- 6.3.2.4 xView -- 6.3.3 Two Representative Object Detectors in Optical RS Images -- 6.3.3.1 Mask OBB -- 6.3.3.2 RoI Transformer -- 6.4 Object Detection in SAR Images -- 6.4.1 Challenges of Detection in SAR Images -- 6.4.2 Related Works -- 6.4.3 Datasets and Benchmarks -- 6.5 Conclusion -- Chapter 7 Deep Domain Adaptation in Earth Observation -- 7.1 Introduction -- 7.2 Families of Methodologies -- 7.3 Selected Examples -- 7.3.1 Adapting the Inner Representation -- 7.3.2 Adapting the Inputs Distribution -- 7.3.3 Using (few, well‐chosen) Labels from the Target Domain -- 7.4 Concluding Remarks -- Chapter 8 Recurrent Neural Networks and the Temporal Component -- 8.1 Recurrent Neural Networks -- 8.1.1 Training RNNs -- 8.1.1.1 Exploding and Vanishing Gradients. , 8.1.1.2 Circumventing Exploding and Vanishing Gradients -- 8.2 Gated Variants of RNNs -- 8.2.1 Long Short‐term Memory Networks -- 8.2.1.1 The Cell State ct and the Hidden State ht -- 8.2.1.2 The Forget Gate ft -- 8.2.1.3 The Modulation Gate vt and the Input Gate it -- 8.2.1.4 The Output Gate ot -- 8.2.1.5 Training LSTM Networks -- 8.2.2 Other Gated Variants -- 8.3 Representative Capabilities of Recurrent Networks -- 8.3.1 Recurrent Neural Network Topologies -- 8.3.2 Experiments -- 8.4 Application in Earth Sciences -- 8.5 Conclusion -- Chapter 9 Deep Learning for Image Matching and Co‐registration -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Classical Approaches -- 9.2.2 Deep Learning Techniques for Image Matching -- 9.2.3 Deep Learning Techniques for Image Registration -- 9.3 Image Registration with Deep Learning -- 9.3.1 2D Linear and Deformable Transformer -- 9.3.2 Network Architectures -- 9.3.3 Optimization Strategy -- 9.3.4 Dataset and Implementation Details -- 9.3.5 Experimental Results -- 9.4 Conclusion and Future Research -- 9.4.1 Challenges and Opportunities -- 9.4.1.1 Dataset with Annotations -- 9.4.1.2 Dimensionality of Data -- 9.4.1.3 Multitemporal Datasets -- 9.4.1.4 Robustness to Changed Areas -- Chapter 10 Multisource Remote Sensing Image Fusion -- 10.1 Introduction -- 10.2 Pansharpening -- 10.2.1 Survey of Pansharpening Methods Employing Deep Learning -- 10.2.2 Experimental Results -- 10.2.2.1 Experimental Design -- 10.2.2.2 Visual and Quantitative Comparison in Pansharpening -- 10.3 Multiband Image Fusion -- 10.3.1 Supervised Deep Learning‐based Approaches -- 10.3.2 Unsupervised Deep Learning‐based Approaches -- 10.3.3 Experimental Results -- 10.3.3.1 Comparison Methods and Evaluation Measures -- 10.3.3.2 Dataset and Experimental Setting -- 10.3.3.3 Quantitative Comparison and Visual Results -- 10.4 Conclusion and Outlook. , Chapter 11 Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives -- 11.1 Introduction -- 11.2 Deep Learning for RS CBIR -- 11.3 Scalable RS CBIR Based on Deep Hashing -- 11.4 Discussion and Conclusion -- Acknowledgement -- Part II Making a Difference in the Geosciences With Deep Learning -- Chapter 12 Deep Learning for Detecting Extreme Weather Patterns -- 12.1 Scientific Motivation -- 12.2 Tropical Cyclone and Atmospheric River Classification -- 12.2.1 Methods -- 12.2.2 Network Architecture -- 12.2.3 Results -- 12.3 Detection of Fronts -- 12.3.1 Analytical Approach -- 12.3.2 Dataset -- 12.3.3 Results -- 12.3.4 Limitations -- 12.4 Semi‐supervised Classification and Localization of Extreme Events -- 12.4.1 Applications of Semi‐supervised Learning in Climate Modeling -- 12.4.1.1 Supervised Architecture -- 12.4.1.2 Semi‐supervised Architecture -- 12.4.2 Results -- 12.4.2.1 Frame‐wise Reconstruction -- 12.4.2.2 Results and Discussion -- 12.5 Detecting Atmospheric Rivers and Tropical Cyclones Through Segmentation Methods -- 12.5.1 Modeling Approach -- 12.5.1.1 Segmentation Architecture -- 12.5.1.2 Climate Dataset and Labels -- 12.5.2 Architecture Innovations: Weighted Loss and Modified Network -- 12.5.3 Results -- 12.6 Challenges and Implications for the Future -- 12.7 Conclusions -- Chapter 13 Spatio‐temporal Autoencoders in Weather and Climate Research -- 13.1 Introduction -- 13.2 Autoencoders -- 13.2.1 A Brief History of Autoencoders -- 13.2.2 Archetypes of Autoencoders -- 13.2.3 Variational Autoencoders (VAE) -- 13.2.4 Comparison Between Autoencoders and Classical Methods -- 13.3 Applications -- 13.3.1 Use of the Latent Space -- 13.3.1.1 Reduction of Dimensionality for the Understanding of the System Dynamics and its Interactions -- 13.3.1.2 Dimensionality Reduction for Feature Extraction and Prediction. , 13.3.2 Use of the Decoder -- 13.3.2.1 As a Random Sample Generator -- 13.3.2.2 Anomaly Detection -- 13.3.2.3 Use of a Denoising Autoencoder (DAE) Decoder -- 13.4 Conclusions and Outlook -- Chapter 14 Deep Learning to Improve Weather Predictions -- 14.1 Numerical Weather Prediction -- 14.2 How Will Machine Learning Enhance Weather Predictions? -- 14.3 Machine Learning Across the Workflow of Weather Prediction -- 14.4 Challenges for the Application of ML in Weather Forecasts -- 14.5 The Way Forward -- Chapter 15 Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting -- 15.1 Introduction -- 15.2 Formulation -- 15.3 Learning Strategies -- 15.4 Models -- 15.4.1 FNN‐based Models -- 15.4.2 RNN‐based Models -- 15.4.3 Encoder‐forecaster Structure -- 15.4.4 Convolutional LSTM -- 15.4.5 ConvLSTM with Star‐shaped Bridge -- 15.4.6 Predictive RNN -- 15.4.7 Memory in Memory Network -- 15.4.8 Trajectory GRU -- 15.5 Benchmark -- 15.5.1 HKO‐7 Dataset -- 15.5.2 Evaluation Methodology -- 15.5.3 Evaluated Algorithms -- 15.5.4 Evaluation Results -- 15.6 Discussion -- Appendix -- Acknowledgement -- Chapter 16 Deep Learning for High‐dimensional Parameter Retrieval -- 16.1 Introduction -- 16.2 Deep Learning Parameter Retrieval Literature -- 16.2.1 Land -- 16.2.2 Ocean -- 16.2.3 Cryosphere -- 16.2.4 Global Weather Models -- 16.3 The Challenge of High‐dimensional Problems -- 16.3.1 Computational Load of CNNs -- 16.3.2 Mean Square Error or Cross‐entropy Optimization? -- 16.4 Applications and Examples -- 16.4.1 Utilizing High‐dimensional Spatio‐spectral Information with CNNs -- 16.4.2 The Effect of Loss Functions in Retrieval of Sea Ice Concentrations -- 16.5 Conclusion -- Chapter 17 A Review of Deep Learning for Cryospheric Studies -- 17.1 Introduction -- 17.2 Deep‐learning‐based Remote Sensing Studies of the Cryosphere -- 17.2.1 Glaciers. , 17.2.2 Ice Sheet.
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  • 2
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    Enhanced seasonal CO_2 exchange caused by amplified plant productivity in northern ecosystems, link to model results (2016)
    PANGAEA
    In:  Supplement to: Forkel, Matthias; Carvalhais, Nuno; Rödenbeck, Christian; Keeling, Ralph F; Heimann, Martin; Thonicke, Kirsten; Zaehle, Sönke; Reichstein, Markus (2016): Enhanced seasonal CO_2 exchange caused by amplified plant productivity in northern ecosystems. Science, 6274, 696-699, https://doi.org/10.1126/science.aac4971
    Details
    Publication Date: 2023-01-13
    Description: Atmospheric monitoring of high northern latitudes (〉 40°N) has shown an enhanced seasonal cycle of carbon dioxide (CO2) since the 1960s but the underlying mechanisms are not yet fully understood. The much stronger increase in high latitudes compared to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. Here we show that the latitudinal gradient of the increasing CO2 amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results emphasize the importance of climate-vegetation-carbon cycle feedbacks at high latitudes, and indicate that during the last decades photosynthetic carbon uptake has reacted much more strongly to warming than carbon release processes.
    Type: Dataset
    Format: application/zip, 1.7 GBytes
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    DATA PANGAEA
  • 3
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    Soil nitrogen isotope database (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-20
    Description: This dataset comprises a compilation of soil bulk delta-15-N nitrogen isotopic composition that has been measured and/or published since the compilation of d15N data by Craine et al. (2015; doi:10.1007/s11104-015-2542-1; doi:10.1038/srep08280). The data was measured by the data owner / contact indicated in the dataset. All data remains the property of the listed owner but may be used for non-commercial purposes. In the case of significant use of this data for scientific research, please cite this dataset as well as the associated publication(s) and consider contacting data owners to offer co-authorship where relevant. Project: Identifying drivers of N2O emissions in a changing climate (https://www.oecd.org/agriculture/crp/fellowships/). Award: OECD Cooperative Research Program for Sustainable Agricultural and Food Systems (OECD-CRP) grant.
    Keywords: isotope; Soil nitrogen
    Type: Dataset
    Format: text/plain, 132.5 kBytes
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    DATA PANGAEA
  • 4
    Electronic Resource
    Electronic Resource
    Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of current hypotheses? (2002)
    Oxford, UK : Blackwell Science Ltd
    Global change biology 8 (2002), S. 0 
    Details
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Eddy covariance and sapflow data from three Mediterranean ecosystems were analysed via top-down approaches in conjunction with a mechanistic ecosystem gas-exchange model to test current assumptions about drought effects on ecosystem respiration and canopy CO2/H2O exchange. The three sites include two nearly monospecific Quercus ilex L. forests – one on karstic limestone (Puéchabon), the other on fluvial sand with access to ground water (Castelporziano) – and a typical mixed macchia on limestone (Arca di Noè). Estimates of ecosystem respiration were derived from light response curves of net ecosystem CO2 exchange. Subsequently, values of ecosystem gross carbon uptake were computed from eddy covariance CO2 fluxes and estimates of ecosystem respiration as a function of soil temperature and moisture. Bulk canopy conductance was calculated by inversion of the Penman-Monteith equation. In a top-down analysis, it was shown that all three sites exhibit similar behaviour in terms of their overall response to drought. In contrast to common assumptions, at all sites ecosystem respiration revealed a decreasing temperature sensitivity (Q10) in response to drought. Soil temperature and soil water content explained 70–80% of the seasonal variability of ecosystem respiration. During the drought, light-saturated ecosystem gross carbon uptake and day-time averaged canopy conductance declined by up to 90%. These changes were closely related to soil water content. Ecosystem water-use efficiency of gross carbon uptake decreased during the drought, regardless whether evapotranspiration from eddy covariance or transpiration from sapflow had been used for the calculation. We evidence that this clearly contrasts current models of canopy function which predict increasing ecosystem water-use efficiency (WUE) during the drought. Four potential explanations to those results were identified (patchy stomatal closure, changes in physiological capacities of photosynthesis, decreases in mesophyll conductance for CO2, and photoinhibition), which will be tested in a forthcoming paper. It is suggested to incorporate the new findings into current biogeochemical models after further testing as this will improve estimates of climate change effects on (semi)arid ecosystems' carbon balances.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1046/j.1365-2486.2002.00530.x
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  • 5
    Electronic Resource
    Electronic Resource
    On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm (2005)
    Oxford, UK : Blackwell Science Ltd
    Global change biology 11 (2005), S. 0 
    Details
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (Reco). In particular, we analyse the effect of the extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets. For this analysis, we used 16 one-year-long data sets of carbon dioxide exchange measurements from European and US-American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems.We show that the temperature sensitivity of Reco, derived from long-term (annual) data sets, does not reflect the short-term temperature sensitivity that is effective when extrapolating from night- to daytime. Specifically, in summer active ecosystems the long-term temperature sensitivity exceeds the short-term sensitivity. Thus, in those ecosystems, the application of a long-term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half-hourly to annual time-scales, which can reach 〉25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long-term temperature sensitivity is lower than the short-term temperature sensitivity resulting in underestimation of annual sums of respiration.We introduce a new generic algorithm that derives a short-term temperature sensitivity of Reco from eddy covariance data that applies this to the extrapolation from night- to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and Reco, we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-2486.2005.001002.x
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  • 6
    Electronic Resource
    Electronic Resource
    Drought controls over conductance and assimilation of a Mediterranean evergreen ecosystem: scaling from leaf to canopy (2003)
    Oxford, UK : Blackwell Science Ltd
    Global change biology 9 (2003), S. 0 
    Details
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Drought control over conductance and assimilation was assessed using eddy flux and meteorological data monitored during four summer periods from 1998 to 2001 above a closed canopy of the Mediterranean evergreen oak tree Quercus ilex. Additional discrete measurements of soil water content and predawn leaf water potential were used to characterize the severity of the drought.Canopy conductance was estimated through the big-leaf approach of Penman–Monteith by inverting latent heat fluxes. The gross primary production (GPP) was estimated by adding ecosystem respiration to net ecosystem exchange. Ecosystem respiration was deduced from night flux when friction velocity (u*) was greater than 0.35 m s−1. Empirical equations were identified that related maximal canopy conductance and daily ecosystem GPP to relative soil water content (RWC), the ratio of current soil water content to the field capacity, and to the predawn leaf water potential. Both variables showed a strong decline with soil RWC for values lower than 0.7. The sharpest decline was observed for GPP. The curves reached zero for RWC=0.41 and 0.45 for conductance and GPP, respectively. When the predawn leaf water potential was used as a surrogate for soil water potential, both variables showed a hyperbolic decline with decreasing water potential.These results were compared with already published literature values obtained at leaf level from the same tree species. Scaling up from the leaf to ecosystem highlighted the limitation of two big-leaf representations: Penman–Monteith and Sellers' Π factor. Neither held completely for comparing leaf and canopy fluxes. Tower measurements integrate fluxes from foliage elements clumped at several levels of organization: branch, tree, and ecosystem. The Q. ilex canopy exhibited non-random distribution of foliage, emphasizing the need to take into account a clumping index, the factor necessary to apply the Lambert–Beer law to natural forests.Our results showed that drought is an important determinant in water losses and CO2 fluxes in water-limited ecosystems. In spite of the limitations inherent to the big-leaf representation of the canopy, the equations are useful for predicting the influence of environmental factors in Mediterranean woodlands and for interpreting ecosystem exchange measurements.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-2486.2003.00687.x
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  • 7
    Electronic Resource
    Electronic Resource
    Does the temperature sensitivity of decomposition of soil organic matter depend upon water content, soil horizon, or incubation time? (2005)
    Oxford, UK : Blackwell Science Ltd
    Global change biology 11 (2005), S. 0 
    Details
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Several studies have shown multiple confounding factors influencing soil respiration in the field, which often hampers a correct separation and interpretation of the different environmental effects on respiration. Here, we present a controlled laboratory experiment on undisturbed organic and mineral soil cores separating the effects of temperature, drying–rewetting and decomposition dynamics on soil respiration. Specifically, we address the following questions:〈list xml:id="l1" style="custom"〉1Is the temperature sensitivity of soil respiration (Q10) dependent on soil moisture or soil organic matter age (incubation time) and does it differ for organic and mineral soil as suggested by recent field studies.2How much do organic and mineral soil layers contribute to total soil respiration?3Is there potential to improve soil flux models of soil introducing a multilayer source model for soil respiration?Eight organic soil and eight mineral soil cores were taken from a Norway spruce (Picea abies) stand in southern Germany, and incubated for 90 days in a climate chamber with a diurnal temperature regime between 7 and 23°C. Half of the samples were rewetted daily, while the other half were left to dry and rewetted thereafter. Soil respiration was measured with a continuously operating open dynamic soil respiration chamber system. The Q10 was stable at around 2.7, independent of soil horizon and incubation time, decreasing only slightly when the soil dried. We suggest that recent findings of the Q10 dependency on several factors are emergent properties at the ecosystem level, that should be analysed further e.g. with regard to rhizosphere effects. Most of the soil CO2 efflux was released from the organic samples. Initially, it averaged 4.0 μmol m−2 s−1 and declined to 1.8 μmol m−2 s−1 at the end of the experiment. In terms of the third question, we show that models using only one temperature as predictor of soil respiration fail to explain more than 80% of the diurnal variability, are biased with a hysteresis effect, and slightly underestimate the temperature sensitivity of respiration. In contrast, consistently more than 95% of the diurnal variability is explained by a dual-source model, with one CO2 source related to the surface temperature and another CO2 source related to the central temperature, highlighting the role of soil surface processes for ecosystem carbon balances.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-2486.2005.001010.x
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  • 8
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    Constraining Uncertainty in Projected Gross Primary Production With Machine Learning (2021)
    Details
    Publication Date: 2021-07-03
    Description: The terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2 concentration is expected to increase GPP (“CO2 fertilization effect”). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2 fertilization with a machine learning approach to constrain the spatial variations of multimodel GPP projections. In a first step, we use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091–2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 ± 12 Gt C yr−1, compared to the unconstrained model range of 156–247 Gt C yr−1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data are fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between present‐day physically relevant diagnostics and the target variable. In a leave‐one‐model‐out cross‐validation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator.
    Description: Plain Language Summary: About a quarter of human emissions of carbon dioxide (CO2) is absorbed by vegetation and soil on the Earth's surface and hence does not contribute to global warming caused by CO2 in the atmosphere. Thus, in order to better define the remaining carbon budgets left to meet particular warming targets like the 1.5°C of the Paris Agreement, it is important to accurately quantify the carbon uptake by plants in the future. Currently, this is modeled by Earth system models yet with great uncertainties. In this work, we present an alternative machine learning approach to reduce spatial uncertainties of vegetation carbon uptake in future climate projections using observations of today's conditions.
    Description: Key Points: An emergent constraint on CO2 seasonal cycle amplitude changes reduces uncertainties in global mean gross primary production projections. A machine learning model with multiple predictors can further constrain the spatial distribution of gross primary production. High‐latitude ecosystems show higher gross primary production increase over the 21st century compared to regions closer to the equator.
    Description: EC | Horizon 2020 Framework Programme 4C
    Description: EC | Horizon 2020 Framework Programme CRESCENDO
    Description: ERC Consolidator Grant SEDAL
    Description: ERC Synergy Grant USMILE
    Keywords: 551.6 ; future climate projections ; modeling
    Type: article
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    CITATION GEO-LEO
  • 9
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    How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem (2021)
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    Publication Date: 2021-07-21
    Description: Nutrient availability, especially of nitrogen (N) and phosphorus (P), is of major importance for every organism and at a larger scale for ecosystem functioning and productivity. Changes in nutrient availability and potential stoichiometric imbalance due to anthropogenic nitrogen deposition might lead to nutrient deficiency or alter ecosystem functioning in various ways. In this study, we present 6 years (2014–2020) of flux‐, plant‐, and remote sensing data from a large‐scale nutrient manipulation experiment conducted in a Mediterranean savanna‐type ecosystem with an emphasis on the effects of N and P treatments on ecosystem‐scale water‐use efficiency (WUE) and related mechanisms. Two plots were fertilized with N (NT, 16.9 Ha) and N + P (NPT, 21.5 Ha), and a third unfertilized plot served as a control (CT). Fertilization had a strong impact on leaf nutrient stoichiometry only within the herbaceous layer with increased leaf N in both fertilized treatments and increased leaf P in NPT. Following fertilization, WUE in NT and NPT increased during the peak of growing season. While gross primary productivity similarly increased in NT and NPT, transpiration and surface conductance increased more in NT than in NPT. The results show that the NPT plot with higher nutrient availability, but more balanced N:P leaf stoichiometry had the highest WUE. On average, higher N availability resulted in a 40% increased leaf area index (LAI) in both fertilized treatments in the spring. Increased LAI reduced aerodynamic conductance and thus evaporation at both fertilized plots in the spring. Despite reduced evaporation, annual evapotranspiration increased by 10% (48.6 ± 28.3 kg H2O m−2), in the NT plot, while NPT remained similar to CT (−1%, −6.7 ± 12.2 kgH2O m−2). Potential causes for increased transpiration at NT could be increased root biomass and thus higher water uptake or rhizosphere priming to increase P‐mobilization through microbes. The annual net ecosystem exchange shifted from a carbon source in CT (75.0 ± 20.6 gC m−2) to carbon‐neutral in both fertilized treatments [−7.0 ± 18.5 gC m−2 (NT) 0.4 ± 22.6 gC m−2 (NPT)]. Our results show, that the N:P stoichiometric imbalance, resulting from N addition (without P), increases the WUE less than the addition of N + P, due to the strong increase in transpiration at NT, which indicates the importance of a balanced N and P content for WUE.
    Description: Plain Language Summary: The availability of nutrients like nitrogen (N) and phosphorus (P) is important for every living organism on Earth. Due to human activities, especially combustion processes large amounts of N are transported into the atmosphere and ecosystems. Therefore, ecosystems receive additional N but no other nutrients. We are investigating if the addition of N alone will lead to deficits in other nutrients and thus impact the functioning of ecosystems. Hence, we set up a large‐scale ecosystem experiment in a Mediterranean tree‐grass ecosystem where we fertilized two plots with N (16.9 ha) and N + P (21.5 ha). A third plot served as the control treatment. While the N‐only treatment created an imbalance between the available N and P, this imbalance was relieved in the N + P treatment where both N and P were provided. Our measurements showed that both fertilized treatments increased their carbon uptake and turned the ecosystem from a carbon source to carbon neutral. One of the main differences between the fertilized treatments which is associated with the imbalance of available N and P is the loss of water through the vegetation (transpiration). This increase in transpiration was only observed in the N‐only but not in the N + P treatment. Our results show, that the N:P stoichiometric imbalance, resulting from N‐only addition, increases the water‐use efficiency (i.e., the carbon gain per water loss) less than the addition of N + P, due to the strong increase in transpiration at the N‐only treatment.
    Description: Key Points: Stoichiometric N:P‐ratio imbalance increases ecosystem transpiration. High nitrogen availability increases carbon uptake and changed the ecosystem from a carbon source to carbon neutral. Ecosystem scale functional relationships are altered through nutrient availability and imbalance.
    Description: Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329
    Description: Deutsches Zentrum für Luft‐ und Raumfahrt http://dx.doi.org/10.13039/501100002946
    Keywords: 577.2 ; Eddy covariance ; MANIP ; nutrient availability ; stoichiometric imbalance ; transpiration ; water use efficiency
    Type: article
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    CITATION GEO-LEO
  • 10
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    Where Are Global Vegetation Greening and Browning Trends Significant? (2021)
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    Publication Date: 2021-07-21
    Description: Global greening trends have been widely reported based on long‐term remote sensing data of terrestrial ecosystems. Typically, a hypothesis test is performed for each grid cell; this leads to multiple hypothesis testing and false positive trend detection. We reanalyze global greening and account for this issue with a novel statistical method that allows robust inference on greening regions. Based on leaf area index (LAI) data, our methods reduce the detected greening from 35.2% to 15.3% of the terrestrial land surface; this reduction is most notable in nonwoody vegetation. Our results confirm several greening regions (China, India, Europe, Sahel, North America, Brazil, and Siberia), that are also supported by independent data products. We also report evidence for an increasing seasonal amplitude in LAI north of 35°N. Considering the widespread use of spatially replicated trend tests in global change research, we recommend adopting the proposed multiple testing procedure to control false positive outcomes.
    Description: Plain Language Summary: Using satellite data, recent studies have detected an increase in vegetation greenness around the globe. These studies attribute this vegetation increase to different factors, such as warming or land use change. However, we argue that the commonly used analysis method is detecting too many regions with trends. In this work, we reanalyze vegetation data using the leaf area index, which measures the area occupied by leaves in any given area. With our refined methods, we too detect greening regions around the world, however these regions are smaller and less abundant. Our research introduces a step in the statistical analysis that increases the reliability of the detected vegetation greening. This can help establish more consensus on what the main contributing factors are for the observed vegetation increase.
    Description: Key Points: Many studies have consistently reported on global greening trends, but repeatedly without rigorous significance testing. Although global greening has been overestimated, significant greening can still be rigorously detected. We observe an increase in the seasonal amplitude of leaf area index around the glob.
    Keywords: 581.9 ; 910.285 ; Global Greening ; Leaf Area Index (LAI) ; Multiple Testing ; Statistical significance
    Type: article
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