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Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear fusion method. We found that SMOS and SMAP TB are strongly correlated (R > 0.90 over most of the globe) but present a small bias at both the horizontal and vertical polarizations. The preliminary evaluation results show that this bias can be adjusted using a linear fit, but further evaluation procedures are still needed. In the near future, we will develop a long-term time series of SM and L-VOD products based on this merged SMOS-SMAP TB record.

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.

Assessment of the model-based surface soil temperature (ST) products is very important for hydrometeorological and ecological applications, as well as model refinements. Distinguished from previous regional validations using only in situ observations from sparse networks, this study focused on the evaluation of model-based ST products by considering ground observations from 15 dense networks worldwide from April 2015 to December 2017 covering a wide range of ground conditions. Four model-based ST products were selected for the assessment, including the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Goddard Earth Observing System Model version 5 Forward Processing (GEOS-5 FP), the ERA-Interim and its successor, the newly developed ERA5. The results indicate the GEOS-5 ST product slightly outperforms other ST products by showing an averaged ubRMSD of 1.84 K. All model-based ST products underestimate in situ ST with a negative bias. All four model-based ST products are demonstrated to well capture the temporal trends of ground observations with very promising $R$ values larger than 0.97. The ERA5 shows visible improvements compared to its predecessor ERA-Interim by exhibiting smaller ubRMSD, absolute bias and larger $R$ values. These findings are expected to provide useful suggestions for the enhancement and specific usage of the model-based ST products.

The present study focuses on retrieving soil and canopy temperatures, which are key parameters to estimate soil moisture and vegetation optical depth from multi-frequency microwaves information. Several retrieval algorithms assume that canopy and vegetation temperatures are similar in thermal equilibrium conditions, while others separate their contributions, as SMOS-IC, one of the consolidated retrieval algorithms for the Soil Moisture and Ocean Salinity (SMOS) satellite mission. Soil and canopy temperatures in SMOS-IC are modelled from the ECMWF (European Centre for Medium-Range Weather Forecasts) centre. Both SMOS and the Soil Moisture Active Passive (SMAP) missions are currently the only passive L-band (1.4 GHz) missions in operation, but their lifetime is limited. In this context, the upcoming Copernicus Imaging Microwave Radiometer (CIMR) mission will provide continuity on L-band measurements with complementary information in a range of microwave frequencies, from 1.4 to 36.5 GHz. This study uses in situ soil moisture information from the International Soil Moisture Network (ISMN) as input in the SMOS-IC algorithm to retrieve vegetation optical depth (VOD) and soil/canopy effective temperature (T GC ). The retrieved effective temperature is then compared with modelled temperatures from ECMWF and with data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), which acquires the higher frequency bands (C, X, K a , and K u ) present in the future CIMR mission. Results confirm the potential of all high-frequency bands to estimate T GC , with C and X-bands being the most correlated. This study is a first approach to evaluate how microwave multi-frequency information can help modelling soil and canopy temperatures in the SMOS-IC retrieval algorithm, from which the upcoming CIMR mission may benefit.

The long time series vegetation productivity products are of great significance to the research of increasing CO 2 and global changes. In this paper, global net primary productivity (NPP) in 1981–2018 was firstly estimated with Global LAnd Surface Satellite (GLASS) data, ERA-Interim meteorological data and the other variables by using the improved Multi-source data Synergized Quantitative (MuSyQ) NPP algorithm. The average global NPP is 61.0 PgC/yr in 1981–2018, which is 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 over the past 38 years. NPP in the northern hemisphere and southern hemisphere account for 62.0% and 38.0% of the global respectively, both show an increasing trend. The overall increasing trends in NPP are also consistent among most of the biomes.

International audience; Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

NPP is calculated to characterize vegetation activity as well as improve our understanding of terrestrial ecosystem carbon cycle. In this study, we estimated a time series of NPP and the spatial and temporal variations from 1982 to 2012 in China. Subsequently, the correlations between the NPP and climate factors (temperature and precipitation) were evaluated to show the responses of vegetation NPP to climate changes. The results showed that NPP in China decreased from southeast to northwest due to the spatial variability of vegetation types and climate characteristics. Annual NPP had a fluctuating increase tendency during our study period with values ranging from 1.92 to 2.73 PgC•a-1, with an annual increase of 0.02 PgC•a-2. In addition, NPP in north China correlates positively with precipitation and negatively correlates with temperature, this is owing to the fact that this region is relatively dry and increasing precipitation extends the growing season of vegetation. In south China the results were the opposite.

Light polarization is a key factor of modern photonics. Tailoring surface plasmons (SPs) in anisotropically-shaped metallic nanostructures introduces the prospect of polarization control at small scale [1]. However, the resulting components remain much larger than the wavelength of light. Here, we present a travelling-wave helical plasmonic antenna (TW-HPA) that overcomes this limit [2]. Due to its non-resonant nature, it differs from existing helical plasmonic structures [3–5], thus extending the concept of travelling-wave helical antenna [6] to optics.