Clement Albergel

• PhD
• Researcher at European Space Agency

In this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosyst...

Large scale hydrological models like CTRIP and MGB are essential for simulating river dynamics and supporting large-scale climate studies. Their accuracy can be significantly improved through satellite data assimilation. This study leverages 20 years of high-resolution discharge data (2000–2020) from the ESA Climate Change Initiative (CCI) to enhan...

The terrestrial biosphere plays a major role in the global carbon cycle, and there is a recognized need for regularly updated estimates of land‐atmosphere exchange at regional and global scales. An international ensemble of Dynamic Global Vegetation Models (DGVMs), known as the “Trends and drivers of the regional scale terrestrial sources and sinks...

In this study, we provide an update of the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosyst...

ASCAT normalized backscatter and slope contain valuable information about soil moisture and vegetation. While backscatter has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate ASCAT observables directly to constrain vegetation states. Here, we assimilate backscatter and...

A study was carried out to investigate the effects of wildfires on lake water quality using a source dataset of 2024 lakes worldwide, covering different lake types and ecological settings. Satellite-derived datasets (Lakes_cci and Fire_cci) were used and a Source Pathway Receptor approach applied which was conceptually represented by fires (burned...

Climate change exerts a profound impact on lakes, eliciting responses that range from gradual to abrupt transitions. When reaching critical tipping points, the established lake dynamics stand to undergo substantial modifications, setting off a chain reaction that reverberates through the entire ecosystem. This lake shift ripples into related ecosys...

The Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances and climate change have impacted the Amazon carbon balance. Here we conduct a comprehensive synthesis of existing state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon using a set of...

Emissions from fossil fuel exploitation are a leading contributor to global anthropogenic methane emissions, but are highly uncertain. The lack of reliable estimates hinders monitoring of the progress on pledges towards methane reductions. Here we analyze methane emissions from exploitation of coal, oil and gas for major producing nations across a...

Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in t...

Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag‐1 month temporal autocorrelation (TAC), to vegetation optical depth data in C‐band (a good proxy of the whole canopy water c...

Very high-resolution (∼10–100 m) surface soil moisture (SM) observations are important for applications in agriculture, among other purposes. This is the original goal of the S2MP (Sentinel-1/Sentinel-2-Derived Soil Moisture Product) algorithm, which was designed to retrieve surface SM at the agricultural plot scale by simultaneously using Sentinel...

Lakes have been observed as sentinels of climate change. In the last decades, global warming and increasing aridity has led to an increase in both the number and severity of wildfires. This has a negative impact on lake catchments by reducing forest cover and triggering cascading effects in freshwater ecosystems. In this work we used satellite remo...

The Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances and climate change have impacted the Amazon carbon balance. Here we conduct a comprehensive analysis of state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon. Over the whole Amazon r...

Vegetation optical depth (VOD) is seasonally sensitive to plant water content and aboveground biomass. This index has a strong penetrability within the vegetation canopy and is less impacted by atmosphere aerosol contamination effects, clouds and sun illumination than optical vegetation indices. VOD is thus increasingly applied in ecological applic...

A consistent dataset of lake surface water temperature, ice cover, water-leaving reflectance, water level and extent is presented. The collection constitutes the Lakes Essential Climate Variable (ECV) for inland waters. The data span combined satellite observations from 1992 to 2020 inclusive and quantifies over 2000 relatively large lakes, which r...

Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e.\\ without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in...

With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited with respect to the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related t...

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L‐Band Vegetation Optical Depth (L‐VOD) data that provide an integrated (top‐down) estimate of biomass carbon to track changes over 2011–2019. Because the s...

The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their ut...

The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources...

The beginning of the 21 st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land,...

A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ40o), slope (σ′) and curvature (σ″) over France. The Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM) is used to produce land surface variables (LSVs) that are input to the DNN. The DNN is t...

Compounded weather events such as sequential heatwaves are likely to increasingly impact freshwater ecosystems in the future. Satellite-derived chlorophyll-a concentration estimates for 36 European lakes during a widespread double heatwave event in the summer of 2019 show that deep and medium depth lakes at higher latitudes displayed a synchronous...

The Okavango River system in southern Africa is known for its strong interannual variability of hydrological conditions. Here, we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model, using an inter-calibrated, long-term, multi-sensor passive...

High-resolution (HR) surface soil moisture (SM) observations are important for applications in hydrology and agriculture, among other purposes. For instance, the S2MP (Sentinel-1/Sentinel-2 derived Soil Moisture Product) algorithm was designed to retrieve surface SM at agricultural plot scale using simultaneously Sentinel-1 (S1) backscatter coeffic...

Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed...

Lakes are significant emitters of methane to the atmosphere, and thus are important components of the global methane budget. Methane is typically produced in lake sediments, with the rate of methane production being strongly temperature dependent. Local and regional studies highlight the risk of increasing methane production under future climate ch...

Topographic data are an important source of information for the processing of Earth Observation (EO) products and, thus, for developing reliable EO-based services. Within this context, the Copernicus Programme made the important effort of making available a high-quality elevation dataset that can be used as harmonised elevation reference for downst...

Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals...

The land data assimilation system, LDAS-Monde, developed by the research department of the French meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing land surface variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) a...

In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by cou...

High-Mountain Asia exhibits one of the highest increases in vegetation greenness on Earth, subsequently influencing the exchange of water and energy between the land surface and the atmosphere. Given the strong interactions between the hydrosphere, the biosphere, and the cryosphere, understanding the drivers of greening in this highly complex regio...

Much of the focus of global warming impacts on lakes have focused on changes in mean temperature. However, lakes are also highly vulnerable to thermal extremes. Such extremes occur, by definition, during lake heatwaves. Heatwaves in lakes have occurred globally in recent decades and have had severe negative impacts. However, unlike their atmospheri...

The Okavango river system in southern Africa is known for its strong interannual variability of hydrological conditions. Here we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model using an inter-calibrated, long term, multi-sensor passive mi...

An emerging concern for lake ecosystems is the occurrence of compound extreme events i.e., situations where multiple within-lake extremes occur simultaneously. Of particular concern are the co-occurrence of lake heatwaves (anomalously warm temperatures) and high chlorophyll-a extremes, two important variables that influence the functioning of aquat...

With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited, especially in the representation of the impact of agriculture on land surface variables. This is particularly true for agro-hydrologica...

The land data assimilation system, LDAS-Monde, developed by the Research Department of the French Meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing Land Surface Variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) a...

Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span fr...

Lake heatwaves – prolonged periods of hot surface water temperature in lakes – have recently been shown to increase in intensity and duration, with numerous potential implications for aquatic ecosystems. However, an important physical attribute of lake heatwaves that has not yet been investigated is their spatial extent, and how it varies within a...

In support of the Global Stocktake of the Paris Agreement on Climate change, this study presents a comprehensive framework to process the results of atmospheric inversions in order to make them suitable for evaluating UNFCCC national inventories of land-use carbon dioxide (CO2) emissions and removals, corresponding to the Land Use, Land Use Change...

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium‐Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to ∼10°C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) w...

The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous so...

Monitoring the evolution of snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In situ and remotely sensed observations provide precious information on the state of the snowpack but usually offer limited spatio-temporal coverage of bulk or surface variables only. In particular, visib...

Framed within the Copernicus Climate Change Service of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the 5th generation of European ReAnalysis (ERA5), hereafter named as ERA5-Land. Once completed, the period covered will span from 1950 to pre...

The present study aims to investigate the potential of multi-configuration Sentinel-1 (S-1) synthetic aperture radar (SAR) images for characterizing four wheat parameters: total fresh mass (TFM), total dry mass (TDM), plant heights (He), and water content (WC). Because they are almost independent on the weather conditions, we have chosen to use onl...

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensi...

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes...

Droughts can have strong environmental and socioeconomic impacts in the Mediterranean region, in particular for countries relying on rain-fed agricultural production, but also in areas in which irrigation plays an important role and in which natural vegetation has been modified or is subject to water stress. The purpose of this review is to provide...

Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Inte...

LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and foreca...

The high computational resources and the time-consuming IO (input/output) are major issues in offline ensemble-based high-dimensional data assimilation systems. Bearing these in mind, this study proposes a sophisticated dynamically running job scheme as well as an innovative parallel IO algorithm to reduce the time to solution of an offline framewo...

The MARINE hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are exploited in this work: on the one hand, formerly relying on water height, transfers of water through the subsurface now take place in a homogeneous soil column based on the volumetric soil water content (SSF model)....

Monitoring the evolution of the snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In-situ and remotely sensed observations provide precious information on the snowpack but usually offer a limited spatio-temporal coverage of bulk or surface variables only. In particular, visible-near...

This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of st...

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI...

EGU 2020 presentation

Soil moisture is a key parameter when it comes to understanding the processes related to the water cycle on continental surfaces (infiltration, evapotranspiration, runoff, etc [...]

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, ap...

Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Inte...

This paper introduces an ensemble square root filter (EnSRF) in the context of jointly assimilating observations of surface soil moisture (SSM) and the leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction between Soil, Biosphere and Atmosphere (IS...

This chapter proposes a short review of selected recent activities on the use of Earth observation data for monitoring the hydrological, vegetation, and energy cycles of continental surfaces with a focus on the Euro-Mediterranean region. It is structured in three sections: (1) observing terrestrial variables from space; (2) integration of satellite...

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ •) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASC...

Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution wh...

This paper presents the forward modelling aspects of the SMOS (Soil Moisture and Ocean Salinity) activities at ECMWF (European Centre for Medium-Range Weather Forecasts). Several parameterizations of the Community Microwave Emission Modelling Platform (CMEM) are used to simulate L-band Brightness Temperatures (TBs) and compared to the SMOS TBs for...

This study demonstrates that LDAS-Monde, a global and offline Land Data Assimilation System (LDAS), that integrates satellite Earth observations into the ISBA (Interaction between Soil Biosphere and Atmosphere) Land Surface Model (LSM), is able to detect, monitor and forecast the impact of extreme weather on land surface states. LDAS-Monde jointly...

This paper introduces an Ensemble Square Root Filter (EnSRF), a deterministic Ensemble Kalman Filter, to the context of assimilating jointly observations of surface soil moisture (SSM) and leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction betwe...

This study demonstrates LDAS-Monde, global and offline integration of satellite Earth observations in the ISBA (Interaction between Soil Biosphere and Atmosphere) Land Surface Model (LSM), great potential to detect, monitor and forecast the impact of extremes weather on land surface conditions. LDAS-Monde jointly assimilates Earth observations of s...

The assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB) data in numerical weather prediction systems influences the state of the soil, which in turn affects the exchange of energy and water fluxes between the soil and the near‐surface atmosphere, with potential implications for the prediction of atmospheric variables...