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The Above Ground Biomass (AGB) is an essential component of the Earth carbon cycle. Yet, large uncertainties remain on its spatial distribution and temporal evolution. Improving the accuracy of the AGB estimates requires precise and regular monitoring. Satellite remote sensing offers such capabilities. In particular, the L-Band (1.41 GHz) Vegetation Optical Depth (VOD) derived from the SMOS (Soil Moisture and Ocean Salinity mission) multi-angle brightness temperatures is a good AGB proxy. Averaging the SMOS L-VOD over a year and linking it to a pre-existing AGB map is a well-established method to derive a spatial relationship between both quantities. After temporal extrapolation of this relation, global AGB time series are derived from the L-VOD, allowing to retrieve vegetation biomass values up to 300 Mg ha-1 from 2011 onwards. This study focuses on this protocol to produce a harmonized AGB dataset from the L-VOD and analyses the impact of three factors on the AGB/VOD calibration. First, the influence of the orbit type (ascending or descending) on the estimation is quantified. Second, the relevance of using a single global spatial calibration or several regional ones is thoroughly discussed for the first time. Third, the AGB time series from this new dataset are compared against other published AGB time series to assess the validity of extrapolating a spatial relationship over time. These comparisons highlight that the produced dataset has more inter-annual variability than the other available time series and presents globally lower AGB estimates, particularly over the equatorial part of Africa. These two limitations are inherent to the input data and method used. Overall, the resulting AGB is coherent with the AGB map from the CCI Biomass version 4 and can be used in AGB studies. The freely accessible AGB dataset has been produced from the level 2 SMOS products, mixing ascending and descending orbits altogether and using a single global relationship between the AGB and the VOD. The spatial bias associated with the AGB estimates is also provided in the files. The AGB dataset is open access and the NetCDF files are available at: https://doi.org/10.12770/95f76ff0-5d89-430d-80db-95fbdd77f543 (Boitard et al., 2024). [ABSTRACT FROM AUTHOR]

The two vegetation transfer parameters of $\tau $ τ (Vegetation Optical Depth,VOD) and $\omega $ ω (Omega) could vary significantly across microwave channels in terms of frequencies, polarizations, and incidence angles, and their channel-dependent characteristics have not yet been fully investigated. In this study, we investigate the channel dependence of vegetation effects on microwave emissions from soils using a higher-order vegetation radiative transfer model of Tor Vergata. Corn was selected as the subject of investigation, and a corn growth model was developed utilizing field data collected from the multifrequency and multi-angular ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River (SMELR). Upon compilation of the simulation dataset of microwave emissions of the corn field, the effective scattering albedo across different channels were calculated using the Tor Vergata model. Results show that vertical polarization of the vegetation optical depth is more affected by incidence angle changes, while horizontal polarization exhibits lower variations in vegetation optical depth due to incidence angle adjustments. The channel dependence of vegetation optical depth can be described as the polarization dependence parameter (${C_P}$ C P ) and the frequency dependence parameter (${C_f}$ C f ). These two parameters enable the calculation of vegetation optical depth at any channel under three adjacent frequencies (L-band, C-band and X-band). The effective scattering albedo of vegetation does not vary significantly with vegetation height or angle. It primarily depends on frequency and polarization, showing an overall increasing trend with increasing frequency. The effective scattering albedo with vertical polarization is slightly higher than that with horizontal polarization at higher frequencies, while both are lower in the L-band. This investigation is helpful for understanding the vegetation effects on microwave emissions from soils, ultimately advancing the accuracy of large-scale soil moisture retrieval in vegetated areas. [ABSTRACT FROM AUTHOR]

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is now considered to be one of the main factors causing an increase in both frequency and severity of wildfires. These fires are prone to release substantial quantities of CO2 in the atmosphere and to destroy natural ecosystems while reducing biodiversity. Depending on the ecosystem and climate regime, fires have distinct triggering factors and impacts. To better analyse and describe fire impact on different biomes, we investigated pre and post fire vegetation anomalies at global scale. The study was performed using several remotely sensed quantities ranging from optical vegetation indices (the enhanced vegetation index (EVI)) to vegetation opacities obtained at several microwave wavelengths (X-band, C-band, and L-band vegetation optical depth (X-VOD, C-VOD, and L-VOD)), ranging from 2 to 20 cm. It was found that C- and X-VOD are mostly sensitive to fire over low vegetation areas (grass and small bushes) or over tree leaves; while L-VOD depicts better the fire impact on tree trunks and branches. As a consequence, L-VOD is probably a better way of assessing fire impact on biomass. The study shows that L-VOD can be used to monitor fire affected areas as well as post-fire recovery, especially over densely vegetated areas. [ABSTRACT FROM AUTHOR]

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

A data-driven approach insensitive to the initial conditions was developed to extract governing equations for the concentration of CO 2 in the Altamira cave (Spain) and its two main drivers: the outside temperature and the soil moisture. This model was then reformulated in order to use satellite observations and meteorological predictions, as a forcing. The concentration of CO 2 inside the cave was then investigated from 1950 to 2100 under various scenarios. It is found that extreme levels of CO 2 were reached during the period 1950-1972 due to the massive affluence of visitors. It is demonstrated that it is possible to monitor the CO 2 in the cave in real time using satellite information as an external forcing. For the future, it is shown that the maximum values of CO 2 will exceed the levels reached during the 1980s and the 1990s when the CO 2 introduced by the touristic visits, although intentionally reduced, still enhanced considerably the micro corrosion of walls and pigments., (© 2024. The Author(s).)