Displays

The need for accurate information to characterize the evolution of forest cover at the tropical scale is widely recognized, particularly to assess carbon losses from processes of disturbances such as deforestation and forest degradation¹. In fact, the contribution of degradation is a key element for REDD+ activities and is presently mostly ignored in national reporting due to the lack of reliable information at such scale.
Recently Vancutsem et al.² produced a dataset at 30m resolution which delineates the tropical moist forest (TMF) cover changes from 1990 to 2019. The use of the Landsat historical time-series at high temporal and spatial resolution allows accurate monitoring of deforestation and degradation, from which the carbon losses from disturbances in TMFs can be estimated. A degradation event is defined here as temporary absence of tree cover (visible within a Landsat pixel during a maximum of three years duration) and includes impacts of fires and logging activities.
We quantify the annual losses in above-ground carbon stock associated to degradation and deforestation in TMF over the period 2011-2019 by combining the annual disturbances in forest cover derived from the Landsat archive the pan-tropical map of aboveground live woody biomass density (AGB) from Santoro et al.³ at 100 m. To reduce the local variability within the estimation of AGB values, we apply a moving average filter under the TMF cover for the year 2010. 
The carbon loss due to degradation is accounted as full carbon loss within a pixel (like a deforestation). The reason is that logging activities usually remove large trees with higher biomass densities than the average value of the disturbed pixel indicated by the pan-tropical maps. To avoid double counting of carbon removal, deforestation happening after degradation is not accounted as carbon loss.
Our results are compared with estimates of previous studies that cover different periods and forest domains: (i) Tyukavina et al.⁴ provide estimates of carbon loss from deforestation for the period 2000-2012 for all forests (evergreen and deciduous) discriminating natural forests from managed forests, and (ii) Baccini et al.⁵ provide estimates of carbon loss from deforestation and degradation for the period 2003-2014 for both evergreen and deciduous forests.

In a further step, we will analyze the sensitivity of the results to the input AGB values by applying the same approach to other AGB maps (e.g. Baccini et al. 2012⁶).
Finally we intend to use Sentinel-2 data (10 m) for monitoring the location and extent of logging activities and burnt areas and further improve the estimates of carbon losses from forest degradation. 

1. Achard F, House JI 2015 doi 10.1088/1748-9326/10/10/101002
2. Vancutsem C. et al. 2019 Submitted to Nat. Geoscience
3. Santoro M et al. 2018 doi 10.1594/PANGAEA.894711
4. Tuykavina A et al 2018 http://iopscience.iop.org/1748-9326/10/7/074002
5. Baccini A et al. 2017 doi 10.1126/science.aam5962
6. Baccini A et al. 2012 doi 10.1038/nclimate1354

How to cite: Achard, F., Vancutsem, C., Avitabile, V., and Langner, A.: Estimating carbon losses from disturbances in tropical moist forests (deforestation and forest degradation) since 2011, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19649, https://doi.org/10.5194/egusphere-egu2020-19649, 2020.

The Amazon rainforest is among the most vital ecosystems on earth, holding about a quarter of the global terrestrial carbon sink. Since 2005, three 100-year return period droughts have occurred, the likes of which have the potential to turn the forest from a carbon sink to a carbon source. Monitoring the Amazon is essential to understand the functioning of the various ecoregions and how they respond to water stress. 

In this study, we investigate the ASCAT backscatter and dynamic vegetation parameters (DVP) over the Amazon region as a potential source of information about the vegetation. The dynamic vegetation parameters are slope and curvature of the second order Taylor polynomial used to represent the incidence angle dependence of backscatter. We looked for spatial and temporal patterns in the backscatter and DVP over Amazonia, and related them to climatic variables such as radiation and precipitation from the Princeton Global Meteorological Forcing Dataset, as well as variations in terrestrial water storage from GRACE. 

Results will be presented from the first ten years of ASCAT observations over the Amazon region, including the Cerrado grasslands southeast of the Amazon forest. We found that spatial patterns of the backscatter and ASCAT DVP reflect the distribution of major land cover types in the region. Seasonal variations in the parameters match the seasonality of moisture demand and availability, and show an influence of vegetation phenology. Diurnal differences in backscatter between the morning (~10:00 AM) and evening overpasses (~10:00 PM) suggest that the backscatter is sensitive to vegetation water dynamics. Significant anomalies were observed during the Amazon droughts of 2010 and 2015, indicating that ASCAT could detect water stress and drought effects in the vegetation. Therefore, the ASCAT DVP show promise for long-term monitoring of the Amazon with respect to vegetation water dynamics and droughts. 

How to cite: Petchiappan, A., Steele-Dunne, S., Vreugdenhil, M., Hahn, S., and Wagner, W.: Relating ASCAT backscatter and dynamic vegetation parameters to vegetation water dynamics in the Amazon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11398, https://doi.org/10.5194/egusphere-egu2020-11398, 2020.

Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.

Our study spans from April 2015 to March 2019 and is structured in two parts:

Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (mg) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and mg independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.

Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.

References

[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.

[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.

[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353

[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.

[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.

How to cite: Chaparro, D., Jagdhuber, T., Entekhabi, D., Piles, M., Fluhrer, A., Feldman, A., Jonard, F., and Vall-llossera, M.: Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18686, https://doi.org/10.5194/egusphere-egu2020-18686, 2020.

Radar observations of vegetated surfaces are highly affected by water in the soil and canopy. Consequently, radar has been used to monitor surface soil moisture for decades now. In addition, radar has been proven a useful tool for monitoring agricultural crop growth and development and forest fuel load estimation, as a result of the sensitivity of backscatter to vegetation water content (VWC). These current applications are based on satellite revisit periods of days to weeks. However, with future satellite constellations and geosynchronous radar missions, such as ESA’s Earth Explorer candidate mission HydroTerra, we will be able to monitor soil and vegetation multiple times per day. This opens up opportunities for new applications.

Examples could be (1) early detection of water stress in vegetation through anomalies in daily cycles of VWC, and (2) spatio-temporal estimations of rainfall interception, an important part of the water balance. However, currently, we lack the knowledge to physically understand sub-daily patterns in backscatter. Hence, the aim of our research is to understand the effect of water-related factors on sub-daily patterns of radar backscatter of a growing corn canopy.

Two intensive field campaigns were conducted in Florida (2018) and The Netherlands (2019). During both campaigns, soil moisture, external canopy water (dew, interception), soil water potential, and weather conditions were monitored every 15 minutes for the entire growing season. In addition, regular destructive sampling was performed to measure seasonal and sub-daily variations of vegetation water content. In Florida, hourly field scans were made with a truck-mounted polarimetric L-band scatterometer. In The Netherlands, these measurements were extended with X- and C-band frequencies.

Here, results will be presented from both campaigns. Different periods in the growing season will be highlighted. In particular, we will elaborate on the effects of variations in internal and external canopy water, and soil moisture on diurnal backscatter patterns.

How to cite: Vermunt, P., Steele-Dunne, S., Khabbazan, S., Judge, J., and Guerriero, L.: Effects of sub-daily internal and external canopy water fluctuations on radar backscatter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13128, https://doi.org/10.5194/egusphere-egu2020-13128, 2020.

To monitor ecosystems at large spatial scale multiple data sources are needed. We developed a methodology to simulate ecosystem functional properties (EFPs): light use efficiency (LUE), water use efficiency (WUE), and evaporative fraction (EF) with Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model at global scale using weather and optical satellite data.

EFPs, metrics that integrate ecosystem processes and environmental conditions, are calculated from ecosystem fluxes: gross primary productivity (GPP), sensible (H) and latent (LE) heat flux. These fluxes were simulated by SCOPE from weather parameters and plant traits (leaf area index (LAI), leaf chlorophyll content (Cab)). The weather data was taken from ECMWF ERA5-Land dataset, the plant traits were retrieved with look-up table (LUT) from Sentinel-2 Level 2 composites, exported from Google Earth engine at 10 km resolution.

LUT retrieval was optimized on a synthetic dataset to reach acceptable quality for the key drivers of GPP flux: LAI (R² = 0.75) and Cab (R² = 0.62). The global retrieved LAI showed some discrepancies with MODIS LAI product MCD15, especially in forest regions (RMSE = 1.73 m² m^-2). As a consequence, SCOPE-simulated GPP was lower in those regions, compared to MODIS GPP product (MYD17) (RMSE = 0.81 kgC m^-2 yr^-1). SCOPE-simulated heat fluxes were compared to ECMWF energy flux from ERA5-Land dataset (RMSE_H = 35.4 W m^-2, RMSE_LE = 41.6 W m^-2). EFPs validation is in progress.

The discrepancies in LAI can be explained by the fact that we did not use plant functional type information during LUT retrieval, in contrast to the MODIS algorithm. Significant overestimation of LE in dry areas is the consequence of the absence of water balance routine in SCOPE model. We consider SCOPE to be a promising tool for optical and weather data fusion.

The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.

How to cite: Prikaziuk, E., van der Tol, C., and Migliavacca, M.: Global maps of ecosystem functional properties with the SCOPE model on Google earth engine Sentinel-2 composites, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10347, https://doi.org/10.5194/egusphere-egu2020-10347, 2020.

Earth system models (ESMs) need to correctly simulate the impact of climate on vegetation, as well as the feedback of vegetation on climate. Improving the skill of ESMs in representing climate—biosphere interactions is crucial to enhance predictions of climate and ecosystem functioning. Correlation and regression techniques are commonly used to study these interactions statistically, but these methods lack the ability to unravel the bidirectional nature of the climate–biosphere system. Here, we explore these interactions across multiple temporal scales by adopting a spectral Granger causality framework that allows identifying potentially inter-dependent variables. Multi-decadal remotely-sensed records are used to analyse the impact of key climatic drivers (precipitation, radiation and temperature) on vegetation (Leaf Area Index, LAI), as well as the biophysical feedback on local climate. These observational results are in turn used to benchmark a set of Coupled Model Intercomparison Project Phase 5 (CMIP5) members at the global scale.

Results show that the climate control on LAI variability increases with longer temporal scales, being the highest at inter-annual scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions, as expected. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in northern latitudes at inter-annual scales. ESMs have a tendency to over-represent the climate control on LAI dynamics, and especially the role of precipitation at inter-annual scales. Likewise, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is also overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of climate—biosphere interactions in online ESMs using causal statistics in combination with observational data.

How to cite: Claessen, J., Molini, A., Martens, B., Detto, M., Demuzere, M., and Miralles, D. G.: Climatic drivers and biogeophysical feedbacks: a causal inference approach over multiple temporal scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9057, https://doi.org/10.5194/egusphere-egu2020-9057, 2020.

How to cite: Winkler, A. J., Myneni, R. B., Hannart, A., and Brovkin, V.: Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO2, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8066, https://doi.org/10.5194/egusphere-egu2020-8066, 2020.

Among Essential Climate Variables (ECVs) for global climate observation, the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are the most widely used to study land vegetated surfaces. The NASA’s Moderate  Resolution Imaging Spectro-radiometer (MODIS) is a key instrument aboard the Terra and Aqua platforms and allows to estimate both biophysical variables at coarse resolution (500 m) and global scales. The MODIS operational algorithm to retrieve LAI and FAPAR (MOD15/MYD15/MCD15) uses a physically-based radiative transfer model (RTM) to compute their estimates with corrected surface spectral information content. This algorithm has been heavily validated and compared with field measurements and other sensors but, so far, no equivalent products at high spatial resolution and continental or global scales are routinely produced. 

Here, we introduce and validate a methodology to create a set of high spatial resolution LAI/FAPAR products by learning the MODIS RTM using advanced machine learning approaches and gap filled Landsat surface reflectances. The latter are smoothed and gap-filled by the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM). HISTARTFM has a great potential to improve the original Landsat reflectances by reducing their noise and recovering missing data due to cloud contamination. In addition, HISTARFM runs very fast in cloud computing platforms such as Google Earth Engine (GEE) and provides uncertainty estimates which can be propagated through the models. These estimates allow to compute numerical uncertainties beyond the typical and qualitative control information layers provided in operational products such as the MODIS LAI/FAPAR. The introduced high spatial resolution biophysical products here could be of interest to the users to achieve the needed levels of spatial detail to adequately monitor croplands and heterogeneously vegetated landscapes.

How to cite: Moreno Martínez, Á., Izquierdo Verdiguier, E., Camps Valls, G., Maneta, M., Muñoz Marí, J., Robinson, N., Adsuara, J. E., Campos Taberner, M., García Haro, F. J., Pérez Suay, A., and Running, S. W.: Down-scaling MODIS operational vegetation products with machine learning and fused gap-free high resolution reflectance data in Google Earth Engine, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19616, https://doi.org/10.5194/egusphere-egu2020-19616, 2020.

To ensure effective management of Alien plant species especially the invasive demands for knowledge of their spatial availability. The use of satellite remote sensing tools has increasingly provided potential ways to assess spatial availability as compared to the traditional ways that are inadequate to provide similar information in a detailed way. The Copernicus Sentinel satellite images with a high spatial resolution and easy access at no charge provides an opportunity for mapping the spatial variability at a regional scale and in a detailed manner. In this study, we assess the potential of Sentinel 2 images vegetation indices and using ensemble machine learning techniques, map the spatial variability of invasive species (Opuntia stricta) in an arid and semi-arid region of Kenya. To actualize this, we use Sentinel 2 bands and thirty-one vegetation and elevation indices for classification. Field data collected is divided into two (training & validation) and used to get the best model to classify Opuntia stricta and eight other control classes. The best performing model and the highest contributing features are selected for final Opuntia stricta estimation. The random forest algorithm yields the highest accuracy 89% hence is used to classify Opuntia stricta species. Our observation of the overall results indicates that Sentinels in combination with the indices characterized by spatial resolution provide an importance that can be used to discriminate Opuntia stricta species hence providing an opportunity for long term monitoring and management at a fairly acceptable accuracy hence ensuring limited pasture degradation. Therefore, future research should focus on exploring Sentinel time-series images for estimating Opuntia stricta species at a temporal variability.

How to cite: Muthoka, J., Rowhani, P., and Antonarakis, A.: Predicting Opuntia stricta (Haw.) in arid and semi-arid environment of Kenya using Sentinel imagery and ensemble machine learning classifiers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21152, https://doi.org/10.5194/egusphere-egu2020-21152, 2020.

Tree cover (TC) and biomass carbon stocks (CS) are key parameters for characterizing the states and dynamics of tropical ecosystems. Despite the presence of several datasets with high spatial resolution, differences among data products prevail and systemic inter-relations between TC and CS remain poorly quantified. Further, the role of land use in explaining disagreements among datasets remains largely unexplored. Here, by combining established spatially-explicit estimates of TC and CS over contemporary timescales, we analyse uncertainties between these two ecosystem parameters across the global tropics (~ 23.4°N to 23.4°S). We quantify land use effects by contrasting actual and potential (ie. in the hypothetical absence of land use) states of vegetation and by correlating TC and CS changes with land use intensity. Our results show that land use strongly alters both TC and CS, with disproportionate impacts on CS and large variations across tropical ecozones. Differences between potential and actual vegetation CS remain above 50% across the tropics except for rainforests (34%). Differences within corresponding TC estimates are more variable, and higher among sparsely-vegetated landscapes (81% for shrublands), highlighting the overwhelming extent of land use impacts. Cross-comparisons across available TC and CS datasets reveal large spatial disagreements. More than a third of all identified co-located TC and CS change datasets show disagreements in the direction of change (Gain vs Loss), and these divergences persist as a function of land use intensities. Our results provide a characterization of the prevailing uncertainty structures of input datasets and the spatial patterns of land use-induced disturbances at the pixel and ecozone-levels. This assumes added significance in light of the stock-taking exercises envisaged as part of the Paris Agreement, the advancement of terrestrial carbon modelling initiatives as well as emerging, novel remote sensing products.

How to cite: Bhan, M., Fritz, S., Gingrich, S., and Erb, K.: Land use shapes the relationship between tree cover and carbon stocks in the tropics, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4721, https://doi.org/10.5194/egusphere-egu2020-4721, 2020.

Monitoring ecosystem dynamics is fundamental to understanding and eventually forecasting ecosystem states. To achieve this, it is crucial to identify and understand potential negative/ positive effects from a changing world on the system. As one key aspect of every ecosystem are the living organisms it involves, our research focuses on vegetation, since it has major implications for both the climate, because plants absorb carbon dioxide, and human well-being, because people depend on the products of plants. Specifically addressing global drylands, where vegetation productivity is tightly linked to the availability of water (mainly through rainfall), we quantify changes in vegetation functioning by analyzing the slopes of a sequential linear regression (SeRGS) over a time series of remote sensing data (NDVI and rainfall), as introduced in Abel et al., 2019. Further, we apply a data-driven, empirical approach to estimate the relative importance of potential drivers of identified changes, as in Abel et al., 2020 (in revision). We show that there are substantial regional and continental differences in vegetation functioning and that these trends can be linked to global trends of population expansion, large-scale agriculture intensification/ expansion and changing climatic conditions. Results from these studies, follow-up research and perspectives will be presented and discussed at EGU.

References:

Abel, C., Horion, S., Tagesson, T., Brandt, M., Fensholt, R. (2019). Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS). Remote Sens. Environ. 224, 317–332.

Abel, C., Horion, S., Tagesson, T., De Keersmaecker, W., Seddon, A. W. R., Abdi A. M., Fensholt, R. (2020). How the human-environment nexus changes global dryland vegetation functioning, in revision.

How to cite: Abel, C., Horion, S., Tagesson, T., De Keersmaecker, W., Seddon, A. W. R., Abdi, A. M., and Fensholt, R.: How global dryland vegetation dynamics relate to changing climatic conditions and anthropogenic dynamics, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7723, https://doi.org/10.5194/egusphere-egu2020-7723, 2020.

        Knowledge of the current severe global environmental changes, vegetation has faced the dual challenges posed by climate change and human activities. Quantitatively distinguishing the influence of climate change and human activities on vegetation changes is a key to develop adaptive ecological protection policies. This study used the Normalized Difference Vegetation Index (NDVI) and meteorological data from 1982 to 2015 to analyze the characteristic of vegetation changes and the relationship with climate factors in Mainland China. The contribution rates of climate change and human activities to vegetation dynamics are further calculated by the improved trend method of residual analysis. The results show that 68.81% vegetation of Mainland China is in a state of sustainable increase and cultivated vegetation (CV) and grass are the main greening vegetation types. The impact of human activities (54.45%-75.27%) on vegetation changes in Mainland China is higher than climate change (24.73%-45.46%). Human activities mainly affect grass, mixed coniferous broad-leaved forest (MCBF) and cultivated vegetation (CV), while swamp is more sensitive to climate change. The improved residual trend method considering temporal and spatial dimensions can reduce the uncertainty of the methods. This study provides a theoretical basis for future government implementation of ecological management.

How to cite: Zhang, Y. and Ye, A.: Quantitatively distinguish the impact of climate change and human activities on the vegetation changes in Mainland China based on the improved residual method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2283, https://doi.org/10.5194/egusphere-egu2020-2283, 2020.

Identifying causal relations from observational data is key to understand Earth system interactions. Extensions to spatio-temporal analysis at different scales are of vital importance for better understanding dynamical phenomenon of natural complex systems. Soil moisture-vegetation interactions constitute a central part of ecosystem functioning and health. Here we are interested in uncovering (potentially nonlinear) spatio-temporal causal relations at different time scales between two relevant Earth observation variables: soil moisture (SM) and vegetation optical depth (VOD). To aboard the complexity data problem, we extract relevant and expressive feature components with the nonlinear kernel-based dimensional reduction method ROCK-PCA in [1]. The method yields the main modes of variability of the variables that are then used to study causal relations. To infer causality relations we use the cross-information kernel Granger causality (XKGC) method introduced in [2], which accounts for nonlinear cross-relations between the involved variables and generalizes nonlinear GC methods. Results are succesfully compared to standard correlation analysis, transfer entropy and convergent cross-mapping alternative methods. In general XKGC identifies a sparser connectivity than correlation. Also, well-known wet and dry patterns are identified as reported in the literature, but other interesting unreported connections and spatio-temporal SM<-->VOD emerge.

REFERENCES
[1] D. Bueso, M. Piles and G. Camps-Valls, "Nonlinear PCA for Spatio-Temporal Analysis of
Earth Observation Data," in IEEE Transactions on Geoscience and Remote Sensing, accepted (2020).
[2] Brajard, J., Charantonis, A., Chen, C., & Runge, J. (Eds.). (2019). Proceedings of the
9th International Workshop on Climate Informatics: CI 2019 (No. NCAR/TN-561+PROC).

How to cite: Bueso, D., Piles, M., and Camps-Valls, G.: Unraveling the time-scale teleconnections between soil moisture and vegetation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18028, https://doi.org/10.5194/egusphere-egu2020-18028, 2020.

The annual phenological cycle is of key importance for the carbon and energy fluxes in terrestrial ecosystems. Although the processes controlling budburst and leaf senescence are fairly well known, the connection between plant phenology and the carbon fluxes remains a challenging aspect in land surface modelling (LSM). In this study, the modelling strategies of three well stablished LSM are compared. The LSM considered in this study were: ORCHIDEE, ISBA-A-gs and the model driving the LSA-SAF evapotranspiration product (https://landsaf.ipma.pt). The latter model does not simulate the carbon fluxes but focuses on the computation of evapotranspiration and energy fluxes.
The phenological cycle is simulated explicitly in the ORCHIDEE model, using empirical relations based on temperature sum, water availability, and other variables. In the ISBA-A-gs model, phenology and LAI development is fully photosynthesis-driven. The phenology in the LSA-SAF model is driven by remote sensing forcing variables, such as LAI observations. Alternatively, the assimilation of remote sensing LAI products is a convenient method to improve the simulated phenological cycle in land surface models. A dedicated module for this operation is available in ISBA-A-gs.
Simulations were performed over a wide range of climatological conditions and plant functional types. The results were then validated with in-situ measurements conducted at Fluxnet stations. In addition to the comparison between measured and modelled carbon fluxes, the validation in this study included the intra-annual variation in the simulated phenological cycle.

How to cite: De Pue, J., Barrios, J. M., Maignan, F., Liu, L., Ciais, P., Arboleda, A., Hamdi, R., Balzarolo, M., and Gellens-Meulenberghs, F.: Phenology-induced energy and carbon fluxes in land surface models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17702, https://doi.org/10.5194/egusphere-egu2020-17702, 2020.

LDAS-Monde is the offline land data assimilation system (LDAS) developed by Météo-France’s research centre (CNRM) aiming to monitor the evolution of land surface variables (LSVs) at various scales, from regional to global. It combines numerical simulations from the multilayer and interactive vegetation ISBA land surface model and satellite-derived observations of surface soil moisture and leaf area index (LAI). LDAS-Monde has been successfully validated over the globe.

In this work, we study the possibility to set up LDAS-Monde to the context of the kilometric spatial resolution. In this context, we assimilate satellite observations of LAI from the Copernicus Global Land Service (CGLS) into the ISBA land surface model forced with Météo-France’s small scale numerical weather prediction system AROME. We produce a reanalysis of LSVs at 2.5-km spatial resolution over the AROME domain centred on France starting from 2017. The quality of this reanalysis is assessed by comparing the obtained reanalysis with satellite products of LAI and surface soil moisture from e.g. CGLS and in-situ measurements of soil moisture from various networks (SMOSMANIA, …). We also show the ability of our system to monitor the evolution of LSVs in the context of the severe drought that France suffered during the summer 2018. LDAS-Monde at 2.5-km spatial resolution displays a great potential for agricultural monitoring at high resolution. We also plan to adapt our framework to 1.0-km spatial resolution.

How to cite: Bonan, B., Albergel, C., Napoly, A., Zheng, Y., and Calvet, J.-C.: An offline reanalysis of land surface variables with LDAS-Monde forced by a kilometric scale NWP system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17527, https://doi.org/10.5194/egusphere-egu2020-17527, 2020.

Earth observations were used to evaluate and improve the representation of Land Surface Temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models - the European Center for Medium Range Weather Forecasting (ECMWF) Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) and the Méteo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modelling platform (SURFEX-ISBA) for the 2004-2015 period.

The results show that the daily maximum LST simulated by HTESSEL over Iberia is affected by a large cold bias during summer months when compared against the Satellite Application Facility Land Surface Analysis (LSA-SAF), reaching magnitude larger than 10ºC over wide portions of central and southwestern Iberia. This error is shown to be tightly linked to a misrepresentation of the vegetation cover.  In contrast, SURFEX simulations did not had such a cold bias. This was due to the better representation of vegetation coverage in SURFEX, which uses an updated land cover dataset (ECOCLIMAP II) and an interactive vegetation evolution, representing seasonality.

The representation of vegetation over Iberia in HTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) Leaf Area Index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement vegetation includes a clumping approach to introduce seasonality to the vegetation coverage. The results show significant added value, removing the daily maximum LST summer cold bias completely while never reducing the accuracy over all seasons and hours of the day.

This work has important implications: First, LST is a key variable in surface-atmosphere energy and water exchanges and, thus, its accurate representation in earth system models is very important. Second, HTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis, hence systematic errors are propagated into these products. Indeed, we show that the summer daily maximum LST cold bias over Iberia in HTESSEL is present in the widely used ECMWF fifth generation reanalysis (ERA5) and fourth generation reanalysis (ERA-Interim).  Finally, our results provide hints into the interaction between vegetation land-atmosphere exchanges, highlight the consistent relevance of the vegetation cover and seasonality in representing land surface temperature in both models, and how earth observations play a critical role for constraining and improving weather and climate simulations.

How to cite: Nogueira, M., Albergel, C., Boussetta, S., Johanssen, F., and Dutra, E.: On the added value of improving the spatial representation and seasonal variations of vegetation cover in land surface models for simulated land surface temperature, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18110, https://doi.org/10.5194/egusphere-egu2020-18110, 2020.

The global biomass is determined by the vegetation turnover times (τ) and carbon fixation through photosynthesis. Vegetation turnover time is a central parameter that not only partially determines the terrestrial carbon sink but also the response of terrestrial vegetation to the future changes in climate. However, the change of magnitude, spatial patterns and uncertainties in τ as well as the sensitivity of these processes to climate change is not well understood due to lack of observations on global scale. In this study, we explore a new dataset of annual above-ground biomass (AGB) change from 1993 to 2018 from spaceborne scatterometer observations. Using the long-term, spatial-explicit global dynamic dataset, we investigated how τ change over almost three decades including the uncertainties. Previous estimations of τ under steady-state assumption can now be challenged acknowledging that terrestrial ecosystems are, for the most of cases, not in balance. In this study, we explore this new dataset to derive global maps of τ in non-steady-state for different periods of time. We used a non-steady-state carbon model in which the change of AGB is a function of Gross Primary Production (GPP) and τ (ΔAGB = α*GPP-AGB/ τ). The parameter α represents the percentage of incorporation of carbon from GPP to biomass. By exploring the AGB change in 5 to 10 years of time step, we were able to infer τ and α from the observations of AGB and GPP change by solving the linear equation. We show how τ changes after potential disturbances in the early 2000s in comparison to the previous decade. We also show the spatial distributions of α from the change of AGB. By accessing the change in biomass, τ and α as well as their associated uncertainties, we provide a comprehensive diagnostic on the vegetation dynamics and the potential response of biomass to disturbance and to climate change.   

How to cite: Fan, N., Besnard, S., Santoro, M., Cartus, O., and Carvalhais, N.: Inferring non-steady-state terrestrial vegetation carbon turnover times from multi-decadal space-borne observations on global scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17717, https://doi.org/10.5194/egusphere-egu2020-17717, 2020.

The large uncertainty characterizing the terrestrial carbon (C) cycle is a consequence of the sparse and irregular observations on the ground. In terms of observations, spaceborne remote sensing has been achieving global, repeated coverages of the Earth since the late 1970s, with a continuous increase in terms of density of observations in time and spatial resolution, thus potentially qualifying as data source to fill such gap in knowledge. Above-ground biomass is a baseline for quantifying the terrestrial C pool; however, remote sensing observations do not measure the organic mass of vegetation. Above-ground biomass (AGB) of forests can only be inferred by inverting numerical models relating and combining multiple remote sensing observations. One of the longest time record of observations from space is represented by the backscattered intensity from the European Remote Sensing Wind Scatterometer (ERS WindScat) and the MetOp Advanced Scatterometer (ASCAT), both operating at C-band (wavelength of 6 cm). An almost unbroken time series of backscatter observations at 0.25° spatial resolution exists since 1991 and data continuity is guaranteed in the next decades. In spite of the weak sensitivity of C-band backscatter to AGB, wall-to-wall estimates of AGB have been derived from high-resolution SAR observations by exploiting multiple observations acquired in a relatively short time period  (Santoro et al., Rem. Sens. Env., 2011; Santoro et al., Rem. Sens. Env., 2015). We have now applied this approach to generate a global time series of AGB estimates for each year between 1992 and 2018 from the C-band scatterometer data at 0.25° spatial resolution. The spatial patterns of AGB match known patterns from in situ records and other remote sensing datasets. The uncertainty of our AGB estimates is between 30% and 40% of the estimated value at the pixel level, providing strong confidence in multi-decadal AGB trends. We identify a constant increase of biomass across most boreal and temperate forests of the northern hemisphere. In contrast, we detect severe loss of biomass throughout the wet tropics during the 1990s and the beginning of the 2000 decade in consequence of massive deforestation. This loss in biomass is followed by a steady increase during the 2000s and the beginning of the most recent decade, coming more recently into saturation. Overall, we find that the global AGB density at 0.25° steadily increased by 9% from 71.8 Mg ha^-1 Pg in the 1990s to 78.1 Mg ha^-1 in the 2010s. Combining our AGB density estimates with the annual maps of the Climate Change Initiative (CCI) Land Cover dataset, we show that total AGB in forests decreased slightly from 566 Pg in the 1990s to 560 Pg in the 2000s, then increased to 593 Pg in the 2010s, resulting in an almost 5% net increase during the last three decades.

How to cite: Santoro, M., Cartus, O., Carvalhais, N., Besnard, S., and Fan, N.: Forest above-ground biomass estimates across three decades from spaceborne scatterometer observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19673, https://doi.org/10.5194/egusphere-egu2020-19673, 2020.

We analyse inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010–2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r > 0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r > 0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT’s sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation.

[1] Somkuti et al., "A new space-borne perspective of crop productivity variations over the US Corn Belt." Agricultural and Forest Meteorology 281 (2020): 107826.

How to cite: Somkuti, P., Boesch, H., Parker, R., Webb, A., Feng, L., Palmer, P., and Quaife, T.: A new space-borne perspective of crop productivity variations over the US Corn Belt, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19566, https://doi.org/10.5194/egusphere-egu2020-19566, 2020.

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, current studies have generally been limited by the spatial resolution of datasets with a sufficiently long archive. For example, while available since 2007, the commonly used GOME-2 SIF data has a spatial resolution in the order of 0.5° (~50km), too coarse to effectively separate the competing effects of different types of vegetation from the overall ecosystem dynamics, or to draw general conclusions relevant to the land cover of a region. While finer SIF retrievals are becoming available, such as from the TROPOMI instrument on-board of the Sentinal-5P platform, several years will be needed before their archives reach a sufficient temporal depth.

Using GOME-2 SIF retrievals, downscaled to a resolution of 0.05° (~5km) via a proven methodology [1], comparisons are made with the data-driven FLUXCOM GPP dataset and divergences and convergences explored to see where high-resolution SIF can enhance our understanding of GPP. This includes an exploration of the spatial and temporal relationships between estimates of GPP and SIF at a global scale. The high resolution of the SIF data allows the relationships to be broken down by plant functional type (PFT) for separate climate zones, thus enabling a confrontation between FLUXCOM GPP and SIF at fine granularity and eventually a future integration of SIF in the estimation of data-driven GPP products.

Whilst a linear relationship is generally observed between SIF and GPP in all vegetation categories, areas of non-linearity suggest where SIF could potentially provide more information about ecosystem dynamics that are not represented in the GPP dataset. For example some vegetation types experience saturation in the seasonal GPP measurements (likely driven by the saturation of the fraction of absorbed PAR), that are not emerging from the SIF signal. In addition, in highly productive ecosystems like tropical rainforests, a wide range of spatio-temporal variation in SIF is observed, while only a considerably smaller variability is reproduced in the modelled GPP. Further studies are conducted on how SIF and GPP behave differently in anomalies of air temperature and soil moisture. Overall, the study suggests there is room to improve global land-climate models by incorporating information from SIF.

[1] Duveiller, G., Filipponi, F., Walther, S., Köhler, P., Frankenberg, C., Guanter, L., and Cescatti, A.: A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity, Earth Syst. Sci. Data Discuss., , in review, 2019.

How to cite: Pickering, M., Cescatti, A., and Duveiller, G.: Convergences and divergences between data-driven GPP estimates and high-resolution SIF measurements across vegetation and climatic gradients, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20596, https://doi.org/10.5194/egusphere-egu2020-20596, 2020.

ly during 1981 and 2018, which was in great agreement with the other similar products. The global NPP has shown a significant increase trend, with an annual growth rate of 0.10 PgC/yr (R²=0.4684)

How to cite: Sun, R., Wang, J., Xiao, Z., Zhu, A., Wang, M., and Yu, T.: Estimation of global vegetation productivity from 1981 to 2018 With remote sensing data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4596, https://doi.org/10.5194/egusphere-egu2020-4596, 2020.

Gross Primary Production (GPP) describes the uptake of C0₂ by plants through photosynthesis and is essential to monitor and analyze ecosystem dynamics. Teubner et al.¹ developed a carbon sink-driven approach to estimate GPP on a global scale using Vegetation Optical Depth (VOD), derived from active and passive microwave observations. This allows to analyze GPP variability, complementing existing optical GPP products which are more affected by weather conditions. The short operation time of the individual microwave sensors and the bias between them prohibit analyzing GPP variability. This issue can potentially be overcome by using the Vegetation Optical Depth Climate Archive (VODCA) developed by Moesinger et al.², which merges multiple VOD products into a single data record. However, the use of a long-running VOD composite for estimating global GPP is challenging because the implications of the VOD aggregation process on the modelling of GPP are difficult to identify a priori.

Here, we present the results of applying the carbon sink-driven GPP estimation approach on the VODCA datasets. As model input for each pixel we used raw VOD from VODCA as well as changes in VOD and median VOD, the latter serves as proxy for vegetation cover. In order to analyze the performance of the carbon sink-driven approach when using VODCA as input, the model is cross-validated against single-sensor (AMSR-E) VOD estimates and commonly used carbon source-driven estimates (MODIS/FLUXCOM). We assessed the ability to model GPP based on single-frequency VODCA (C-, X- and Ku-band) as well as using multiple frequencies as model input.

Overall, the results show that single-band as well as multi-band VODCA performs slightly better in predicting GPP than single-sensor based VOD. Especially in the tropical regions multi-frequency VODCA GPP outperforms single-sensor based estimates. Compared to source-driven approaches, VOD based GPP estimates are higher than FLUXCOM and MODIS GPP. The spatial patterns, however, show good correspondence with the carbon source-driven GPP products, confirming that VODCA can be used to extend the GPP estimates to the past three decades.

¹Teubner, I., Forkel, M., Camps-Valls, G., Jung, M., Miralles, Diego, Tramontana, G., van der Schalie, R., Vreugdenhil, M., Moesinger, L., Dorigo, W.:A carbon sink-driven approach to estimate gross primary production from microwave satellite observations, 2019. Remote Sensing of Environment. 229. 100-113. 10.1016/j.rse.2019.04.022.

²Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-42, in review, 2019.

How to cite: Wild, B., Teubner, I., Moesinger, L., and Dorigo, W.: A new global Gross Primary Production (GPP) dataset based on microwave Vegetation Optical Depth Climate Archive (VODCA), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17875, https://doi.org/10.5194/egusphere-egu2020-17875, 2020.

Since the late 1970s, spaceborne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions and coverage need to be overcome.

Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA; Moesinger et al., 2019). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, Windsat and AMSR-2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely Ku-band (period 1987-2017), X-band (1997-2018) and C-band (2002-2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture^1,2. 

The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with Leaf Area Index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-Band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China and central north America. In summary, VODCA shows vast potential for monitoring spatial-temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such it complements existing long-term optical indices of greenness and leaf area. 

¹Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739. https://doi.org/10.5194/essd-11-717-2019

²Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001.

How to cite: Moesinger, L., Zotta, R., van der Schalie, R., Forkel, M., Scanlon, T., Teubner, I., de Jeu, R., and Dorigo, W.: The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18889, https://doi.org/10.5194/egusphere-egu2020-18889, 2020.

Multi-decadal records of global Vegetation Optical Depth (VOD) derived from spaceborne microwave sensors provide novel opportunities to observe and analyze both the current state as well as the temporal changes of vegetation. Theoretically, VOD is sensitive to the biomass and moisture content of vegetation. Past studies found relations between VOD and leaf area index (LAI), productivity, biomass, and vegetation water status. In addition, VOD has been used to investigate or estimate changes in biomass, vegetation isohydricity, and tree mortality. However, VOD is not directly measured with ground observations and therefore difficult to evaluate. Several VOD products exist that have been retrieved using different satellite sensors, microwave frequencies (e.g. Ku, X, C, and L-bands) and retrieval algorithms. These products show differences in both their temporal (e.g. short-term variability) as well as spatial dynamics (e.g. their relation with LAI). Hence from a user point-of-view, it is difficult to assess which VOD products might be the most suitable for a certain ecological application.

Here we aim to develop and present initial results of an ecological-oriented assessment of several VOD products. Based on the theoretical assumption that VOD is sensitive to vegetation biomass and moisture content, we assess the co-varying sensitivities of high- (Ku, X, C-bands) and low-frequency (L-band) VOD products to biomass and moisture content within a consistent evaluation framework. High-frequency VOD was taken from the recent developed VODCA products and low-frequency VOD from SMAP and SMOS retrievals. Biomass was derived from global above-ground biomass maps and MODIS LAI. Canopy moisture content was estimated from MODIS retrievals.

The first results confirm previous findings that VOD is both sensitive to biomass and moisture content. High-frequency VOD products are mainly sensitive to short-term changes in canopy biomass and moisture content and low frequency VOD to woody biomass. However, we also found that high-frequency VOD shows high sensitivity to aboveground biomass in Savannahs and boreal forests. Also low-frequency VOD includes a clear signal of vegetation moisture that cannot be explained by biomass changes. This suggests that multi-frequency VOD products and estimates of vegetation biomass and moisture content should be integrated and jointly analyzed to provide a consistent picture of ecosystem dynamics.

How to cite: Schmidt, L., Forkel, M., Dorigo, W. A., Moesinger, L., van der Schalie, R., Yebra, M., and Pugh, T. A. M.: Assessing the sensitivity of multi-frequency vegetation optical depth to biomass and canopy moisture content: towards an ecological-oriented evaluation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10931, https://doi.org/10.5194/egusphere-egu2020-10931, 2020.

Severe drought and extreme heat associated with the 2015–2016 El Niño event have led to large carbon emissions from the tropical vegetation to the atmosphere. With the return to normal climatic conditions in 2017, tropical forest aboveground carbon (AGC) stocks are expected to partly recover due to increased productivity, but the intensity and spatial distribution of this recovery are unknown. Simulations from land-surface models used in the global carbon budget (GCB) suggest a strong reinvigoration of the tropical land sink after the 2015–2016 El Niño. However, models and atmospheric inversions display large divergences in tropical CO₂ fluxes during the 2017 recovery event. For instance, models predict a total net land sink recovery (2017 sink minus the 2015–2016 average sink) ranging from 0.3 to 2.6 Pg C, and the land sink recovery estimated from five atmospheric inversions ranges from −0.08 to +1.92 Pg C. The results of different inversions show a large spread in the tropics due to the scarcity of stations and uncertainties in atmospheric transport simulations.

We used low-frequency microwave satellite data (L-VOD) to feature precise monitoring of AGC changes and show that the AGC recovery of tropical ecosystems was slow and that by the end of 2017, AGC had not reached predrought levels of 2014¹. From 2014 to 2017, tropical AGC stocks decreased by 1.3 Pg C due to persistent AGC losses in Africa (-0.9 Pg C) and America (-0.5 Pg C). Pantropically, drylands recovered their carbon stocks to pre–El Niño levels, but African and American humid forests did not, suggesting carryover effects from enhanced forest mortality.

Reference

How to cite: Fan, L., Wigneron, J., Ciais, P., Bastos, A., Brandt, M., Chave, J., Saatchi, S., Baccini, A., and Fensholt, R.: SMOS-IC L-VOD reveals that tropical forests did not recover from the strong 2015–2016 El Niño event, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4020, https://doi.org/10.5194/egusphere-egu2020-4020, 2020.