Miguel D Mahecha

Verified

Miguel verified their affiliation via an institutional email.

Learn more

Verified

Miguel verified their affiliation via an institutional email.

Learn more

• Professor
• Professor (Full) at Leipzig University

Understanding Earth system dynamics in the light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data intero...

The planetary boundary (PB) concept has captured attention across academia and the public alike. Its unique visual representation has been key to the development of the concept and its dissemination. In this commentary, we outline three areas of concern to facilitate further enhancement in the PB concept’s visualisation. First, the radial bar plot...

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structur...

It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For example, in 2022, there are about 400 climate extremes and natural hazards worldwide, resulting in significant losse...

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample si...

With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to...

Vegetation is often viewed as a consequence of long‐term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by consuming water resources through transpiration and interception, lowering atmosp...

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structur...

The spectral signatures of vegetation are indicative of ecosystem states and health. Spectral indices used to monitor vegetation are characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in learning and predicting vegetation response, including extreme...

Transpiration (T), the component of evapotranspiration (ET) controlled by the vegetation, dominates terrestrial ET in many ecosystems; however, estimating it accurately, especially at the global scale, remains a considerable challenge. Existing approaches mostly rely on the relationship between T and photosynthesis, but untangling this relationship...

The severity of climate disaster impacts is shaped not only by the intensity of the events themselves but also by the exposure, vulnerability, and adaptive capac- ity of affected communities. The lack of globally comparable data integrating local societal and climatic conditions poses significant challenges to understand- ing which factors transfor...

The Deep Earth System Data Lab (DeepESDL) provides an AI-ready, collaborative environment for researchers aiming to study the Earth's complex dynamics using various datasets and empirical approaches. Recently opened to Early Adopters, it builds on projects like CAB-LAB and ESDL, utilizing well-established Python and Julia technology stacks. DeepESD...

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data...

The intensification of climate extremes is one of the most immediate effects of global climate change. Heatwaves and droughts have uneven impacts on ecosystems that can be exacerbated in case of compound events. To comprehensively study these events, e.g. with local high-resolution remote sensing or in-situ data, a global catalogue of such events i...

Earth observation data is key for monitoring vegetation dynamics across temporal and spatial scales. The most widely used method to estimate vegetation properties from Earth observation data is vegetation indices. However, temporal dynamics in vertical leaf angles can strongly alter reflectance signals and, hence, vegetation indices. Here, we deriv...

With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualizing, modeling, and explaining this data. To showcase how this challenge can be met, here we train a...

The sensitivity of atmospheric CO 2 growth rate to tropical temperature (γ T ) has almost doubled between 1959 and 2011, a trend that has been linked to increasing drought in the tropics. However, γ T has declined since then. Understanding whether these variations in γ T reflect forced changes or internal climate variability in the carbon cycle is...

Vegetation often understood merely as the result of long-term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by altering surface roughness, consuming significant water resources through tr...

Understanding the dynamics of the land-atmosphere exchange of CO$_2$ is key to advance our predictive capacities of the coupled climate-carbon feedback system. In essence, the net vegetation flux is the difference of the uptake of CO$_2$ via photosynthesis and the release of CO$_2$ via respiration, while the system is driven by periodic processes a...

Climate change elevates the threat of compound heat and drought events, with their ecological and socioeconomic impacts exacerbated by human ecosystem alterations such as eutrophication, salinization, and river engineering. Here, we study how multiple stressors produced an environmental disaster in a large European river, the Oder River, where a to...

Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time. Here, we introduce an innovative spatio‐temporal machine learning methodology that harnesses such crowd...

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous an...

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global...

With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to...

The Sentinel-2 (S2) mission from the European Space Agency's Copernicus program provides essential data for Earth surface analysis. Its Level-2A products deliver high-to-medium resolution (10-60 m) surface reflectance (SR) data through the MultiSpectral Instrument (MSI). To enhance the accuracy and comparability of SR data, adjustments simulating a...

Remote sensing is an essential technology in environmental science to study Earth surface processes. In optical remote sensing, spectral indices (SI) are widely used to quantify the properties of specific surface characteristics. SI mathematically combine reflectance values measured at different wavelengths. To gain an overview and access to such i...

Soil is central to the complex interplay among biodiversity, climate, and society. This paper examines the interconnectedness of soil biodiversity, climate change, and societal impacts, emphasizing the urgent need for integrated solutions. Human‐induced biodiversity loss and climate change intensify environmental degradation, threatening human well...

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well‐being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and man...

Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks sign...

Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO2 flux, especially significant for forests. Eddy Covariance (EC) measurements are widely used for ecosystem-scale GPP quantification but are globally sparse. In areas lacking local EC mea...

The Sentinel-2 (S2) mission from the European Space Agency's Copernicus program provides essential data for Earth surface analysis. Its Level-2A products deliver high-to-medium resolution (10-60 m) surface reflectance (SR) data through the MultiSpectral Instrument (MSI). To enhance the accuracy and comparability of SR data, adjustments simulating a...

Quantifying Gross Primary Production (GPP) is fundamental for understanding terrestrial carbon dynamics, particularly in forests. The overarching question we address here is whether integrating remote sensing (RS) with deep learning (DL) methodologies can enhance the estimation of daily forest GPP on a European scale. The Eddy Covariance (EC) metho...

Droughts often lead to cross-sectoral and interconnected socio-economic impacts, affecting human well-being, ecosystems, and economic development. Extended drought periods, such as the 2018–2022 event in Germany, amplify these impacts due to temporal carry-over effects. Yet, our understanding of drought impact dynamics during increasingly frequent...

Terrestrial surface processes exhibit distinctive spectral signatures captured by optical satellites. Despite the development of over two hundred spectral indices (SIs), current studies often narrow their focus to individual SIs, overlooking the broader context of land surface processes. This project seeks to understand the holistic features of Sen...

Understanding the implications of climate change on ecosystems necessitates continuous monitoring of plant phenology. While citizen science data collected through smartphone applications offer a rich source of information, existing phenology studies predominantly focus on individual species. This study introduces a pioneering data science approach...

Understanding Earth's terrestrial biosphere dynamics is vital for comprehending our planet's environmental health and sustainability. Recently, the frequency and intensity of extreme climate events have risen, significantly impacting the biosphere. Given the advancements of recurrent neural networks in modeling complex, nonlinear dynamics, we explo...

Non-technical summary Greenhouse gas emissions and land use change – from deforestation, forest degradation, and agricultural intensification – are contributing to climate change and biodiversity loss. Important land-based strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals...

Interannual variability of vegetation activity (i.e., photosynthesis) is strongly correlated with El Niño Southern Oscillation (ENSO). Globally, a reduction in carbon uptake by terrestrial ecosystems has been observed during the ENSO warm phase (El Niño) and the opposite during the cold phase (La Niña). However, this global perspective obscures the...

Climate change elevates the threat of compound heat and drought events, with their ecological and socioeconomic impacts exacerbated by human ecosystem alterations such as eutrophication, salinization, and river engineering. Here, we study how multiple stressors produced an environmental disaster in a large European river, the Oder, where a toxic bl...

Droughts often lead to cross-sectoral and interconnected socio-economic impacts, affecting human well-being, ecosystems, and economic development. During extended drought periods, such as the 2018–2022 event in Germany, these impacts are amplified due to temporal carry-over effects. Yet, our understanding of drought impact dynamics during increasin...

The Planetary Boundary (PB) concept has captured attention across academia and the public alike. Its unique visual representation has been key to the development of the concept and its dissemination. In this commentary, we outline three areas of concern to facilitate further enhancement in the PB concept’s visualisation. Firstly, the radial bar plo...

Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is chal...

Vegetation state variables are key indicators of land-atmosphere interactions characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in capturing vegetation state responses, including extreme behavior driven by atmospheric conditions. While machine learn...

Many subsystems of Earth are constantly monitored in space and time and undergo continuous anthropogenic interventions. However, research into this transformation remains largely inaccessible to the public due to the complexity of the big data generated by models and Earth Observation (EO). To overcome this barrier, we present the Leipzig Explorer...

Quantifying changes in hot temperature extremes is key for developing adaptation strategies. Changes in hot extremes are often determined on the basis of air temperatures; however, hydrology and many biogeochemical processes are more sensitive to soil temperature. Here we show that soil hot extremes are increasing faster than air hot extremes by 0....

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and man...

Progress in Earth system science is accelerating rapidly, due to the increasing availability of multivariate datasets, often global, with moderate to high spatio-temporal resolutions. Turning these data into knowledge presents interoperability, technical, analytical, and other challenges. Earth System Data Cubes (ESDCs) have surfaced as essential t...

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories –...

p>Preprint of a paper that is submitted to the "Journal of Selected Topics in Earth Observation and Remote sensing" with the following abstract: It is widely assumed that C-Band Synthetic Aperture Radar (SAR) signal do not reach the forest floor in dense forests, and that hence C-Band SAR cannot be used for sub-canopy flood mapping in tropical for...

p>Preprint of a paper that is submitted to the "Journal of Selected Topics in Earth Observation and Remote sensing" with the following abstract: It is widely assumed that C-Band Synthetic Aperture Radar (SAR) signal do not reach the forest floor in dense forests, and that hence C-Band SAR cannot be used for sub-canopy flood mapping in tropical for...

Aim Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both m...

Compound heat waves and drought events draw our particular attention as they become more frequent. Co-occurring extreme events often exacerbate impacts on ecosystems and can induce a cascade of detrimental consequences. However, the research to understand these events is still in its infancy. DeepExtremes is a project funded by the European Space A...

Hot temperature extremes are changing in intensity and frequency. Quantifying these changes is key for developing adaptation strategies [1]. The conventional approach to study changes in hot extremes is based on air temperatures. However, hydrology [2] and many biogeochemical processes, e.g. decomposition of organic material and release of CO 2 [3]...

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N) and phosphorus (P) concentrations play an important role in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations.Here, we intercompare such global...

Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources ar...

Identifying the thresholds of drought that, if crossed, suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and change. We present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using fo...

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether trade-offs and optimality principles in functional traits of leaves are conserved at the ecosystem level. We tested three well-known leaf- and plant-level coordination theories...

Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form...

The ecosystem carbon turnover time—an emergent ecosystem property that partly determines the feedback between the terrestrial carbon cycle and climate—is strongly controlled by temperature. However, it remains uncertain to what extent hydrometeorological conditions may influence the apparent temperature sensitivity of τ, defined as the factor by wh...

Enough of silos: develop a joint scientific agenda to understand the intertwined global crises of the Earth system. Enough of silos: develop a joint scientific agenda to understand the intertwined global crises of the Earth system.

One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of g...

Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportu...

In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is emerging as a promising tool to map plant biodiversity from space. Proposed approaches often rely on the Spectral Variation Hypothesis (SVH), linking the heterogeneity of terrestrial vegetation to the variability of the spectroradiometric signals. Yet, due to observ...

Spectral Indices derived from Remote Sensing (RS) data are widely used for characterizing Earth System dynamics. The increasing amount of spectral indices led to the creation of spectral indices catalogues, such as the Awesome Spectral Indices (ASI) ecosystem. Google Earth Engine (GEE) is a cloud-based geospatial processing service with an Applicat...

We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. It is designed for temporal or sequential tasks such as time series prediction and modeling complex dynamical systems. As such it is suited to process a range of complex spatio-temporal data sets, from mathematical models to climate data. The key ideas...

Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are represent...

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cr...

Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning. Despite great success in many areas, machine learning algorithms in the Earth and climate sciences, and more broadly in physical sciences, are not...

We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. The software offers a great number of algorithms presented in the literature, and allows to expand on them with both internal and external tools in a simple way. The implementation is highly modular, fast and comes with a comprehensive documentation, wh...

One of the least understood temporal–scales of global carbon cycle (C–cycle) dynamics is its inter–annual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large–scale modes of atmospheric variability. Here, we quantify the fraction of...

Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of pho...

This Briefing Note represents an integrated perspective of climate, environmental and disaster risk science and practice regarding systemic risk. It provides an overview of the concepts of systemic risk that have evolved over time and identifies commonalities across terminologies and perspectives associated with systemic risk used in different cont...

The ecosystem carbon turnover time (τ) is an emergent ecosystem property that partly determines the feedback between the terrestrial carbon cycle and climate which is strongly controlled by temperature 1–3 . However, it remains uncertain to what extent hydrometeorological conditions may influence the apparent temperature sensitivity of τ, defined a...

Extreme hydrological and meteorological conditions can severely affect ecosystems, parts of the economy, and consequently society. These impacts are expected to be aggravated by climate change. Here we analyze and compare the impacts of multiple types of extreme events across several domains in Europe, to reveal corresponding impact signatures. We...

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variat...