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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]

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 coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including future climate impact assessment at regional level, a resolution of 1 km is already a significant improvement with respect to most of the publicly available SM data sets, which have resolutions of about 25 km. Therefore, in this study, the S2MP algorithm was adapted to work at a resolution of 1 km and extended from croplands (cereals and grasslands) to herbaceous vegetation types. A target resolution of 1 km also allows to explore the use of NDVI derived from Sentinel-3 (S3) instead of S2. The algorithm improvements are evaluated both over Europe and other regions of the globe, for which S1 coverage is poorer. Two sets of SM maps at 1-km resolution were produced with S2MP over six regions of ~ 104 km2 in the southwest and southeast of France, Spain, Tunisia, North America, as well as Australia from 2017 to 2019. The first set of maps was derived from the combination of S1 and S2 data (S1+S2 maps), while the second one was derived from the combination of S1 and S3 (S1+S3 maps). S1+S2 and S1+S3 SM maps were compared to each other and to those of the 1-km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) data sets as well as to the SMAP+S1 product. The S2MP S1+S2 and S1+S3 SM maps are in very good agreement in terms of correlation (R = 0.9), bias (= 0.04 m3 m-3) and standard deviation of the difference (STDD = 0.03 m3 m-3) over the 6 domains investigated in this study. The S2MP maps are well correlated to those from the CGLS SM product (R ~ 0.7-0.8), but the correlations with respect to the other HR maps (CGLS SWI and SMAP+S1) drop significantly over many areas of the 6 domains investigated in this study. In addition, higher correlations between the HR maps were found over croplands and when the 1-km pixels have a very homogeneous land cover. The bias in between the different maps was found to be significant over some areas of the six domains, reaching values ± 0.1 m3 m-3. The S1+S2 maps show a lower STDD with respect to CGLS maps (= 0.06 m3 m-3) than with respect to the SMAP+S1 maps (= 0.1 m3 m-3) for all the 6 domains. Finally, all the HR data sets were also compared to in situ measurements from 5 networks across 5 countries along with coarse resolution (CR) SM products from SMAP, SMOS and the ESA Climate Change Initiative (CCI). While all the CR and HR products show different bias and STDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. [ABSTRACT FROM AUTHOR]

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation 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 (L-VOD) data sets (SMOS Level 2, Level 3 and SMOS-IC) were compared with data sets on tree height, visible/infra-red indexes (NDVI, EVI), cumulated 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 relationship linking L-VOD to tree height (R = 0.87) and Baccini's AGB (R = 0.94) was strong and linear. The relationships between L-VOD and three other AGB data sets were linear per land cover class, but with a changing slope depending on the land cover type. For low vegetation classes, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlation with the amount of the average annual precipitations. SMOS L-VOD showed a higher sensitivity to AGB as compared to NDVI and K/X/C-VOD (VOD measured, respectively, at 19, 10.7, and 6.9 GHz). The results showed that although the spatial resolution of L-VOD is coarse (~ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising index for large scale monitoring of the vegetation status, in particular biomass. [ABSTRACT FROM AUTHOR]

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 (Tb) 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 Tb's to soil temperature (Tsoil ) 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 Tb's to retrieve Tsoil 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 Tsoil 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. [ABSTRACT FROM AUTHOR]