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The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr −1 ) associated with drying trends, and a net change of −0.02 Pg C yr −1 was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area. Low-frequency passive microwave data (L-VOD) allow quantification of biomass change in sub-Saharan Africa between 2010 and 2016, revealing climate-induced carbon losses, particularly in drylands.

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson’s correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data.

Melt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010–2011 and 2017–2018. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from the special sensor microwave imager (SSM/I) and special sensor microwave imager sounder (SSMIS). The comparison of daily melt occurrence retrieved from 1.4 and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, a theoretical analysis is performed using a microwave emission radiative transfer model. It shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimetres of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present in depths up to hundreds of metres. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher-frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack.

Abstract 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.

Plant water storage is fundamental to the functioning of terrestrial ecosystems by participating in plant metabolism, nutrient and sugar transport, and maintenance of the integrity of the hydraulic system of the plant. However, a global view of the size and dynamics of the water pools stored in plant tissues is still lacking. Here, we report global patterns of seasonal variations in ecosystem-scale plant water storage and their relationship with leaf phenology, based on space-borne measurements of L-band vegetation optical depth. We find that seasonal variations in plant water storage are highly synchronous with leaf phenology for the boreal and temperate forests, but asynchronous for the tropical woodlands, where the seasonal development of plant water storage lags behind leaf area by up to 180 days. Contrasting patterns of the time lag between plant water storage and terrestrial groundwater storage are also evident in these ecosystems. A comparison of the water cycle components in seasonally dry tropical woodlands highlights the buffering effect of plant water storage on the seasonal dynamics of water supply and demand. Our results offer insights into ecosystem-scale plant water relations globally and provide a basis for an improved parameterization of eco-hydrological and Earth system models. Low-frequency vegetation optical depth (L-VOD) sensing reveals global patterns of seasonal variations in ecosystem-scale plant water storage and relationships with leaf phenology; results vary between tropical and temperate–boreal zones.

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region.

Anthropogenic climate change is now considered to be one of the main factors causing an increase in both the frequency and severity of wildfires. These fires are prone to release substantial quantities of CO2 into the atmosphere and to endanger natural ecosystems and biodiversity. Depending on the ecosystem and climate regime, fires have distinct triggering factors and impacts. To better analyse this phenomenon, we investigated post-fire vegetation anomalies over different biomes, from 2012 to 2020. The study was performed using several remotely sensed quantities ranging from visible–infrared vegetation indices (the enhanced vegetation index (EVI)) to vegetation opacities obtained at several passive-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 impact on low-vegetation areas (grass and shrublands) or on tree leaves, while L-VOD depicts the fire impact on tree trunks and branches better. 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.

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (LVOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse (similar to 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.

The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application (https://www.catds.fr/sipad/). The RE04 products, versions 300 and 310, used in this paper are also available at ftp://ext-catds-cpdc:catds2010@ftp.ifremer.fr/Land_products/GRIDDED/L3SM/RE04/.