The need of a mission ensuring the continuation of L-band measurements from space with increased spatial resolution with respect to the current generation of sensors is discussed. The status of the SMOS-High Resolution (HR) mission project, currently under a Phase A study at CNES, is presented.

The Soil Moisture and Ocean Salinity (SMOS) satellite has been providing global data for more than 11 years. The algorithm to retrieve surface soil moisture (SM) and vegetation optical depth (VOD) from Brightness Temperatures (TB) has constantly evolved as our understanding of the instrument and of the responses to various ecosystems increased. The evolution of the radiative transfer modelling used in the inversion process is in part based upon ground measurements done on experimental sites. We present the particular case of an L-band radiometer placed on top of a 800- meter-high cliff. This design allows us to study large and various scenes when compared to classical experiments where a radiometer is placed 10–20 m above a surface. With our set up, the footprints cover areas larger than 300 m in diameter, covering thus several land use types and the footprint can be directed towards different dominant land classes (forest, crops, urban etc…). This paper presents the whole set up consisting of in situ measurements (soil moisture and surface temperatures) and L-band radiometry acquired by the LEWIS instrument at high incidence angles. We perform SM/VOD retrievals from brightness temperatures using various configurations which are compared to the in situ observations. We test in particular a 2-VOD retrieval approach that consists in deriving one SM for the entire scene but two VOD, one for agricultural/low vegetation and a VOD for the forest parts, which improves the performance of the SM/VOD retrievals.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (∼ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ∼ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from > 1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products.

Soil moisture retrieval from microwave remote sensing brightness temperatures is in continuous development. In this study, the most recent and latest microwave remote sensing soil moisture products were evaluated against ground-based measurements. We compared, for the first time, the latest versions of SMOS (L2V650 and SMOS-IC V105), SMAP (L3V4), and CCI (V03.2) soil moisture products with respect to ground-based measurements obtained from ISMN (International Soil Moisture Network). Time series were plotted over some sites and it was found that all these products capture well the temporal dynamics over all the sites used in this study. However, CCI was wetter than the in situ measurements over Niger and both SMOS products (IC and L2) and SMAP were drier than the in situ observations over Biebrza site in Poland.

In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center.

Core Ideas AMMA‐CATCH is a long‐term critical zone observatory in West Africa. Four sites sample the sharp ecoclimatic gradient characteristic of this region. Combined measurements of meteorology, water, and vegetation dynamics began in 1990. Intensification of rainfall and hydrological cycles is observed. The strong overall re‐greening may hide contrasted changes. West Africa is a region in fast transition from climate, demography, and land use perspectives. In this context, the African Monsoon Multidisciplinary Analysis (AMMA)–Couplage de l'Atmosphère Tropicale et du Cycle eco‐Hydrologique (CATCH) long‐term regional observatory was developed to monitor the impacts of global change on the critical zone of West Africa and to better understand its current and future dynamics. The observatory is organized into three thematic axes, which drive the observation and instrumentation strategy: (i) analyze the long‐term evolution of eco‐hydrosystems from a regional perspective; (ii) better understand critical zone processes and their variability; and (iii) meet socioeconomic and development needs. To achieve these goals, the observatory has gathered data since 1990 from four densely instrumented mesoscale sites (∼10 4 km 2 each), located at different latitudes (Benin, Niger, Mali, and Senegal) so as to sample the sharp eco‐climatic gradient that is characteristic of the region. Simultaneous monitoring of the vegetation cover and of various components of the water balance at these four sites has provided new insights into the seemingly paradoxical eco‐hydrological changes observed in the Sahel during the last decades: groundwater recharge and/or runoff intensification despite rainfall deficit and subsequent re‐greening with still increasing runoff. Hydrological processes and the role of certain key landscape features are highlighted, as well as the importance of an appropriate description of soil and subsoil characteristics. Applications of these scientific results for sustainable development issues are proposed. Finally, detecting and attributing eco‐hydrological changes and identifying possible regime shifts in the hydrologic cycle are the next challenges that need to be faced.

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels'' at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m(3) m(-3) or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m(3) m(-3) (0.030 m(3) m(-3)) at the 9-km scale and 0.035 m(3) m(-3) (0.026 m(3) m(-3)) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m(3) m(-3) (0.032 m(3) m(-3)). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 m3/m3 volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 m3/m3, and the radar-based product meets its target accuracy of 0.06 m3/m3 (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive Cal/Val phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the Cal/Val Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.

L-band passive measurements (1.4GHz) over continental areas are known to be related to surface soil moisture. Two satellite missions were recently launched to measure land surface emissions at this frequency band (SMOS-Soil Moisture and Ocean Salinity in 2009 and SMAP-Soil Moisture Active/Passive in 2015). In order to improve soil moisture retrievals from satellite data, ground-based radiometer systems operating at the same frequency were deployed over specified areas to investigate the L-band emission of various land covers under various climatological conditions. In this study, three years of L-band passive measurements from a radiometer installed on top of a steep mountain in the French Alps were analyzed and compared to L-band passive simulations. The innovative radiometer location led to large footprints due to the distance between the radiometer and the area under study. This experiment also produced microwave measurements affected by various potential difficulties typically encountered in SMOS/SMAP satellite missions: topography, heterogeneous footprints, dry/wet snow events, dew and vegetation litter. Based on in situ and modeling data, this paper investigates the potential of a radiative transfer model (L-band Microwave Emission of the Biosphere, L-MEB) to simulate L-band measurements and analyzes the differences with ELBARA observations. First, it was found that the topography generated a mixing of the horizontal and vertical polarizations. In addition, a large positive bias was found on ELBARA measurements (31K and 12K in horizontal and vertical polarizations respectively). Investigations showed that the sky reflection measured by the radiometer was partially substituted by land reflection coming from the surrounding topography. Second, the low-vegetation emission was investigated and highlighted the inability of the MODIS NDVI product to correctly represent the vegetation dynamics. Finally, dry snow conditions were found to have non-negligible impact at L-band and a particular signature was found during snow melting periods, with potential applications at the SMOS/SMAP spatial scales (~40km).

The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

This study aims in filling the gap in understanding the relationship between trend and extreme in diurnal and nocturnal temperatures (Tx and Tn) over the Gulf of Guinea area and the Sahel. Time-evolution and trend of Tx and Tn anomalies, extreme temperatures and heat waves are examined using regional and station-based indices over the 1900–2012 and 1950–2012 periods respectively. In investigating extreme temperature anomalies and heat waves, a percentile method is used. At the regional and local scales, rising trends in Tx and Tn anomalies, which appear more pronounced over the past 60years, are identified over the two regions. The trends are characterized by an intensification of: i) nocturnal/Tn warming over the second half of the 20th century; and ii) diurnal/Tx warming over the post-1980s. This is the same scheme with extreme warm days and warm nights. Finally annual number of diurnal and nocturnal heat waves has increase over the Gulf of Guinea coastal regions over the second half of the 20th century, and even more substantially over the post-1980s period. Although this trend in extreme warm days and nights is always overestimated in the simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), those models display rising trends whatever the scenario, which are likely to be more and more pronounced over the two regions in the next 50years.

This paper discusses soil moisture retrievals over the Tibetan Plateau from brightness temperature (TB's) observed by the Special Sensor Microwave Imagers (SSM/I's) during the warm seasons of the period from July 1987 to December 2008. The Fundamental Climate Data Record (FCDR) of F08, F11 and F13 SSM/I satellites by the Precipitation Research Group of Colorado State University is used for this study. A soil moisture retrieval algorithm is developed based on a radiative transfer model that simulates top-of-atmosphere TB's whereby effects of atmosphere are calculated from near-surface forcings obtained from a bias-corrected dataset. Validation of SSM/I retrievals against in situ measurements for a two-and-half year period (225 matchups) gives a Root Mean Squared Error of 0.046 m3 m−3. The agreement between retrievals and Noah simulations from the Global Land Data Assimilation System is investigated to further provide confidence in the reliability of SSM/I retrievals at the Plateau-scale. Normalised soil moisture anomalies (N) are computed on a warm seasonal (May–October) and on a monthly basis to analyse the trends present within the products available from July 1987 to December 2008. The slope of linear regression functions between N and time is used to quantify the trends. Both the warm season and monthly N indicate severe wettings of 0.8 to almost 1.6 decade−1 in the centre of the Plateau. Correlations are found by the trend with elevation for the warm season as a whole and the individual months May, September and October. The observed wetting of the Tibetan Plateau agrees with recent findings on permafrost retreat, precipitation increase and potential evapotranspiration decline.

The application of Soil-Vegetation-Atmosphere-Transfer (SVAT) scheme into the estimation of soil moisture profile in semi-arid regions is largely constrained by a scarcity of spatially distributed soil and hydraulic property information. Especially, on a large scale in very dry and sandy soils or other extreme conditions, it is difficult to accurately map soil and hydraulic properties with soil maps-based Pedo-Transfer Functions (PTFs), because PTFs are usually semi-empirically defined for specific sites. One strategy to overcome this limitation is to employ satellite data for a purpose of calibration. This paper provides an operational framework of inverting the SVAT soil hydraulic variables from the deterministic ensemble Kalman filter (DEnKF) analysis of Soil Moisture and Ocean Salinity (SMOS) surface soil moisture product. This inverse calibration was first verified with the Analyses Multidisciplinaires de la Mousson Africaine (AMMA) super site data representative of a single grid cell (0.25 degrees) of satellite data. At this local scale, the results demonstrated that the mis-estimation problems of soil surface variable C-1 and equilibrium soil moisture theta(geq) were successfully solved after calibration, demonstrating a better agreement with the field measurement of soil moisture profile than the SMOS product and un-calibrated SVAT scheme using soil maps-based PTFs. On the meso scale, the calibrated SVAT scheme using inverted surface variables appropriately captured a non-linear relationship between surface and root zone soil moisture by showing a typical soil moisture profile in dry climates, where dry surface soil moisture is spatially consistent with rainfall events, but wet root zone soil moisture shows low correlations with surface soil moisture distributions and rainfall events. In contrast, the un-calibrated SVAT scheme using soil maps-based PTFs significantly overestimated surface soil moisture and rainfall effect. This approach suggests several operational merits in that there is no need to heavily rely on empirically defined PTFs or recalibrate land surface parameters for different land surface conditions, and this can be applied even when parameter measurements are unavailable or highly uncertain. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

Recently, Crow et al. (2009) developed an algorithm for enhancing satellite‐based land rainfall products via the assimilation of remotely sensed surface soil moisture retrievals into a water balance model. As a follow‐up, this paper describes the benefits of modifying their approach to incorporate more complex data assimilation and land surface modeling methodologies. Specific modifications improving rainfall estimates are assembled into the Soil Moisture Analysis Rainfall Tool (SMART), and the resulting algorithm is applied outside the contiguous United States for the first time, with an emphasis on West African sites instrumented as part of the African Monsoon Multidisciplinary Analysis experiment. Results demonstrate that the SMART algorithm is superior to the Crow et al. baseline approach and is capable of broadly improving coarse‐scale rainfall accumulations measurements with low risk of degradation. Comparisons with existing multisensor, satellite‐based precipitation data products suggest that the introduction of soil moisture information from the Advanced Microwave Scanning Radiometer via SMART provides as much coarse‐scale (3 day, 1°) rainfall accumulation information as thermal infrared satellite observations and more information than monthly rain gauge observations in poorly instrumented regions.