Ground-based Stations Research Articles

Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliability of the new model and provides a reference method for meteorological monitoring and weather forecasting.

Assessing the impact of land cover on air quality parameters in Jordan: A spatiotemporal study using remote sensing and cloud computing (2019–2022)

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively. The Google Earth Engine (GEE) platform and JavaScript were used to produce monthly short-term average concentration maps and time series for the three pollutants. Pearson correlation analysis was performed to evaluate the performance of Sentinel-5P data against ground-based monitoring stations in estimating NO2, SO2, and CO concentration at a regional scale. Results revealed a moderate correlation, with r-values of 0.42, 0.43, and 0.40, for NO2, SO2, and CO, respectively. The spatiotemporal analysis showed a higher concentration of SO2 and NO2 in the northern and middle regions of the country, coinciding with the spatial distribution of built-up areas and the main urban centers. On a temporal scale, the highest concentration of the three pollutants was observed in the winter months for all governorates of Jordan. For instance, it was found that the highest value of NO2 was in Balqa Governorate in December 2022, 1.57 * 10^4 mol/m2. The highest average monthly SO2 values were observed in Jerash Governorate in December 2019, 7.36 * 10^4 mol/m2. CO concentrations were mainly concentrated in the western parts of the Jordan rift valley.

A multi-method and multi-duration trend analysis of temperature and precipitation in Istanbul, Turkey, by using meteorological records, MERRA-2 reanalysis, and IMERG estimations

In this study, temperature and precipitation trends in Istanbul were analyzed using data from a ground-based station for the 1960–2019 interval, Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (GPM IMERG) for 2000–2019, and Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) temperature reanalysis for 1980–2019. Single-duration analysis revealed significant increasing temperature trends with minor downward and major upward tendencies in precipitation. Multi-duration analysis resulted with dominant decreasing and increasing temperature trends, respectively, in 1980–1999 and 2000–2019, alongside decreasing precipitation trends in 1980–1999 winter and 2000–2019 spring, which were undetected in the single-duration analysis. As a novel attempt, trends based on MERRA-2 and IMERG data were compared with ground-based records in Istanbul and revealed better compatibility in MERRA-2, especially in 2000–2019 compared to IMERG data. By comparing various statistical methods and data sources, this study offers valuable insights into hydrometeorological trend analysis and aids transition from conventional to novel data sources.

INNOVATIVE AIRSHIP DESIGN FOR REAL-TIME AIR QUALITY MONITORING USING IOT TECHNOLOGY

The effect of poor air quality on human health has been linked to both short-term and long-term exposure to air pollutants. One of the most important steps in reducing emissions is accurate identification of these pollutants. This can be achieved by using Internet of Things equipped remotely operated airships that can survey large areas and gather pollution data. The ability of an airship to function at low altitudes for extended periods is the main design requirement of airships. This study describes a methodology for conceptualizing and building a helium-filled blimp for remote measurement of atmospheric pollutants (Carbon monoxide, Carbon dioxide, and Sulphur dioxide), temperature, and pressure. An onboard telemetry system measures the data while the airship is in flight and transmits them in real time to a ground-based station. The experiment showed that remotely operated airships are capable of gathering air data and their quality, enabling environmental scientists and regulatory agencies to better understand the behaviour of air pollutants and take steps to mitigate them.

Full-coverage estimation of CO2 concentrations in China via multisource satellite data and Deep Forest model

Monitoring China’s carbon dioxide (CO2) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO2) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model. The 10-fold cross-validation results indicate a correlation coefficient (R) of 0.95 and a determination coefficient (R²) of 0.90. Validation against ground-based station data yielded R values of 0.93, and R² values reached 0.81. Further validation from the Greenhouse Gases Observing Satellite (GOSAT) and the Copernicus Atmosphere Monitoring Service Reanalysis dataset (CAMS) produced R² values of 0.87 and 0.80, respectively. During the study period, CO2 concentrations in China were higher in spring and winter than in summer and autumn, indicating a clear annual increase. The estimates generated by this study could potentially support CO2 monitoring in China.

Performance evaluation of UKESM1 for surface ozone across the pan-tropics

Abstract. Surface ozone monitoring sites in the tropics are limited, despite the risk that surface ozone poses to human health, tropical forest and crop productivity. Atmospheric chemistry models allow us to assess ozone exposure in unmonitored locations and evaluate the potential influence of changing policies and climate on air quality, human health and ecosystem integrity. Here, we utilise in situ ozone measurements from ground-based stations in the pan-tropics to evaluate ozone from the UK Earth system model, UKESM1, with a focus on remote sites. The study includes ozone data from areas with limited previous data, notably tropical South America, central Africa and tropical northern Australia. Evaluating UKESM1 against observations beginning in 1987 onwards, we show that UKESM1 is able to capture changes in surface ozone concentration at different temporal resolutions, albeit with a systematic high bias of 18.1 nmol mol−1 on average. We use the diurnal ozone range (DOR) as a metric for evaluation and find that UKESM1 captures the observed DOR (mean bias of 2.7 nmol mol−1 and RMSE of 7.1 nmol mol−1) and the trend in DOR with location and season. Results from this study reveal that hourly ozone concentrations from UKESM1 require bias correction before use for impact assessments based on human and ecosystem health. Indeed, hourly surface ozone data have been crucial to this study, and we encourage other modelling groups to include hourly surface ozone output as a default.

Mapping the ionosphere with millions of phones

The ionosphere is a layer of weakly ionized plasma bathed in Earth’s geomagnetic field extending about 50–1,500 kilometres above Earth1. The ionospheric total electron content varies in response to Earth’s space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting in one of the largest sources of error for position, navigation and timing services2. Networks of high-quality ground-based GNSS stations provide maps of ionospheric total electron content to correct these errors, but large spatiotemporal gaps in data from these stations mean that these maps may contain errors3. Here we demonstrate that a distributed network of noisy sensors—in the form of millions of Android phones—can fill in many of these gaps and double the measurement coverage, providing an accurate picture of the ionosphere in areas of the world underserved by conventional infrastructure. Using smartphone measurements, we resolve features such as plasma bubbles over India and South America, solar-storm-enhanced density over North America and a mid-latitude ionospheric trough over Europe. We also show that the resulting ionosphere maps can improve location accuracy, which is our primary aim. This work demonstrates the potential of using a large distributed network of smartphones as a powerful scientific instrument for monitoring Earth.

Impact of Tonga volcanic eruption on the ionosphere based on ground-based GNSS and COSMIC occultation data

The eruption of large volcanoes may potentially disturb the upper atmosphere, causing disturbances in ionospheric plasma and triggering irregular changes in the Total Electron Content (TEC). The GNSS ionospheric inversion method has been used to analyze ionospheric anomalies caused by the volcano, while it was limited to the spatial distribution of ground-based GNSS. The joint observation with space-based GNSS can further improve the spatio-temporal distribution of ionospheric monitoring. The Tonga volcano erupted on January 15th, 2022, is the largest volcanic eruption since the beginning of the 21st century and generated intense acoustic, atmospheric gravity waves and ionospheric disturbance. In this paper, we used the GIM products, ground-based GNSS station data around the volcanic eruption point and COSMIC occultation data to analyze the short-term TEC change. The experiments showed that: the average TEC of GIM grid data on eruption day dropped by 7.5 TECu compared to the last day which was substantially larger than other three days. The TEC extracted from nearby tracking stations was smaller than GIM and the difference was inversely proportional to the distance between the station and the volcanic eruption site. The average daily ionospheric TEC obtained from the nearest TONG station was the biggest, as much as 3.44 TECu lower. The peak electron density of COSMIC occultation data gradually diminished and then recovered with TEC about 7.32 TECu lower than GIM products. The experments showed that there was a good consistency in the TEC obtained from space-based and ground-based GNSS, the Tonga volcano produced a huge TEC change over 10 TECu or even bigger. As a global grid product, GIM underestimated ionospheric changes due to its limited spatial resolution, which needs further improvement.

A Case Study on Deep Learning for Photovoltaic Power Forecasting Combining Satellite and Ground Data

The increasing demand for clean energy presents challenges in energy supply management, largely due to their intermittency. Photovoltaic power generation, in specific, is greatly affected by weather factors, which may render power grids susceptible to instability, quality and balance issues. In this context, photovoltaic power generation forecasting is crucial not only to enhance the management of diverse energy sources through generation planning, but also to ensure widespread adoption of photovoltaic energy. To address the predictability issue in generation, this study aims to investigate the combination of satellite data with meteorological data to predict the energy generation potential in photovoltaic panels within 30, 60, 120, and 180-minute horizons. For this purpose, images from the GOES-16 satellite are used in combination with data from a ground-based weather station, located at Florianópolis – Santa Catarina – Brazil. The data is fed to a convolutional neural network, where convolutions are employed to extract features from the satellite images, aiming to establish a relationship with solar irradiation. The output of the convolutional network serves as input for a multilayer perceptron network, which utilizes the data to predict the Global Horizontal Irradiance (GHI). Our results support that models incorporating satellite images provide forecasts approximately 41% better for the 30-minute horizon and 21% better for the 180-minute horizon, when compared to models without satellite images.

Integrating time series Sentinel-2 images and tide height to mapping tidal flats in the Chinese mainland

As a unique and important ecosystem, tidal flats provide a variety of ecosystem functions and services. Mapping tidal flats is essential for the protection and management of coastal ecosystems. However, large-scale tidal flats mapping still faces challenges due to the tidal variation and spectral similarity between tidal flats and inland wetlands. Previous methods rely on the coastlines or maximum seawater extent to exclude inland areas, which is limited by its inability to effectively differentiate tidal flats from spectrally similar inland wetlands. To address these issues, we proposed a new tidal-flat mapping method by integrating Sentinel-2 time series imagery with tide height (TH) data from ground-based tide stations on Google Earth Engine. We first generated images at the lowest and highest tidal stages, established the statistical relationship between the Normalized Difference Water Index (NDWI) of each pixel and TH, and then concatenated them into a Random Forest classifier for further classification. The statistical relationship between NDWI and TH amplified the difference between tidal flats and inland wetlands, thus significantly reducing the influence of spectral similarity. This method could produce a high-precision tidal flats map with an overall accuracy of 97.30% in the coastal zone of the Chinese mainland. By quantitatively comparing with the previous tidal flat maps, we found that the strategies of tidal-level information simulation and inland area exclusion were the two main reasons producing the differences among the maps. The proposed method does not rely on space constraints to exclude inland wetlands and can capture more estuarine tidal flats, so it can be used as a reliable means to monitor the tidal flats in large-scale areas.

Monitoring meteorological drought in Hoa Vang district, Da Nang City, using Google Earth Engine and satellite-derived precipitation data combined with ground-based weather stations

Monitoring meteorological drought in Hoa Vang district, Da Nang City, using Google Earth Engine and satellite-derived precipitation data combined with ground-based weather stations

Augmented Reality for Air Quality Monitoring: Case Study in the Marche Region (Italy)

Abstract. Augmented Reality (AR) allows the real world to be enhanced through the overlapping of digital information generated by an algorithm or software amplifying the user's perception. In this research, the potential of AR is exploited by applying it to the problem of air pollution visualization to raise awareness of the issue. It is well known that the principal sources of air pollution include industry, domestic heating, traffic, production processes and more others. According to European Environment Agency, air pollution is the largest environmental health risk in Europe in 2022 affecting respiratory and cardiovascular diseases and premature deaths. A suitable combination of GIS-based procedures for data analysis and georeferencing, AR systems for the visualization and exploitation of the results, can be a strategic driver. The research presented in this paper has been experimented in the Marche Region, in the central part of Italy, and in particular the city of Ancona. The pollutant data collected come from 17 Agenzia Regionale per la Protezione Ambientale delle Marche (ARPAM) meteorological ground-based stations located in Marche Region. The data processing have been executed using the software ArcGis Pro instead of a 3D City Model has been acquired from the online 3D ArcGIS platform. According to the Dlgs.155/2010 regulation, the AQI index (Air Quality Index) has been provided by the interpolation of the meteorological ground stations data. The visualization phase has been tackled exploiting in a WebGIS platform and an APP by AR. A 3D immersive visualization is made using five AQI level of air quality (excellent, good, fair, poor, bad) in order to increase the urban sensing.

Aerosol loading in the guinea coast climate region of Nigeria: comparison of MODIS and AERONET data sources

This study compares aerosol optical depth (AOD) measurements from satellite data with that from a ground-based station. It aims at validating monthly AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 6.1 (Aqua and Terra) at 550 nm against AOD data from the Aerosol Robotic Network (AERONET) station at 500 nm in Ilorin over the period 2003–2022. The analysis focuses on eight selected cities/locations within the Guinea Coast region of Nigeria. Descriptive and inferential statistical methods, including correlation, regression, standard deviation, coefficient of variation, the Mann–Kendall trend test, root mean square error (RMSE), mean absolute error (MAE), and relative mean bias (RMB), were employed. The results indicate a positive correlation in most locations, with correlation values ranging from 0.3 to 0.7. The correlation between the datasets in Ilorin showed a positive and significant relationship while the other seven locations presented a weak and insignificant relationship. Additionally, the period from 2013–2022 showed better correlation compared to 2003–2012. AOD levels were highest during the harmattan season (DJF), exceeding 0.8, and lowest during the monsoon (JJA) and post-monsoon (SON) seasons, with values below 0.5, reflecting the effects of wet scavenging. The highest agreement between MODIS Aqua and AERONET data was observed during the harmattan season. The study also found that MODIS tends to overestimate AOD compared to AERONET and that MODIS-AOD exhibits greater spread and variability than AERONET-AOD.

Remote detection of water stress in cotton using a center pivot irrigation system-mounted sensor package.

Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

Geomagnetic and ionospheric effects two consecutive strong earthquakes in Morocco on september 08, 2023

The geophysical effects of a strong seismic event in the form of two earthquakes of magnitude 6.8 and 4.9 that occurred on September 08, 2023 in Morocco at close times 22:11 and 22:30 UTC with an epicentral distance between the foci of ~ 4 km are considered. We used data from a number of observatories of the INTERMAGNET network and the results of magnetic registration at the Mikhnevo Geophysical Observatory of IDG RAS. It was shown that in the absence of significant global disturbances of the Earth’s magnetic field, earthquakes were accompanied by a series of three positive bay-shaped geomagnetic variations with maximum amplitudes from ~1 to ~10 nT, following each other after ~60 min. The maxima of the induced magnetic field variations were observed almost synchronously at distances from ~800 to ~10000 km. The delay time of the magnetic effect relative to the main shock of the first earthquake was ~70 min. Taking into account the almost planetary nature and high synchronicity of the magnetic field disturbances caused over a significant range of distances, as well as time delays corresponding in order of magnitude to the travel time of the seismic signal of a distance multiple of the size of the Earth, it is suggested that the magnetic effect of the seismic event in question was caused by a global source, which can serve as an excited geodynamo. The ionospheric effect of the seismic event under consideration is presented in the form of variations of the critical frequency f0F2 calculated from the data of the ground-based sounding station of the del Ebre Observatory.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

Unusual Forbush Decreases and Geomagnetic Storms on 24 March, 2024 and 11 May, 2024

As the current solar cycle 25 progresses and moves towards solar maxima, solar activity is increasing and extreme space weather events are taking place. Two severe geomagnetic storms accompanied by two large Forbush decreases in galactic cosmic ray intensity were recorded in March and May, 2024. More precisely, on 24 March 2024, a G4 (according to the NOAA Space Weather Scale for Geomagnetic Storms) geomagnetic storm was registered, with the corresponding geomagnetic indices Kp and Dst equal to 8 and −130 nT, respectively. On the same day, the majority of ground-based neutron monitor stations recorded an unusual Forbush decrease. This event stands out from a typical Forbush decrease because of its high amplitude decrease phase and rapid recovery phase, i.e., 15% decrease and an extremely rapid recovery of 10% within 1.5 h, as recorded at the Oulu neutron monitor station. Furthermore, on 10–13 May 2024, an unusual G5 geomagnetic storm (geomagnetic indices Kp = 9 and Dst = −412 nT) was registered (the last G5 storm had been observed in 2003). In addition, the polar neutron monitor stations recorded a Ground Level Enhancement (GLE74) during the recovery phase of a large Forbush decrease of 15%, which started on 10 May 2024. In this study, a detailed analysis of these two severe events in regard to the accompanying solar activity, interplanetary conditions and solar energetic particle events is provided. Moreover, the results of the NKUA “GLE Alert++ system”, the NKUA/IZMIRAN “FD Precursory Signals” method and the NKUA “ap Prediction tool” concerning these events are presented.

The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU): a mission overview, instrument description, and results from the first flight

BackgroundThe Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU) is a new joint project by Japan Aerospace Exploration Agency (JAXA) and ANA HOLDING INC. (ANAHD), which operates ANA flights. GOBLEU aims to visualizes our climate mitigation effort progress in support of subnational climate mitigation by collecting greenhouse gas (GHG) data as well as relevant data for emissions (nitrous dioxide, NO2) and removals (Solar-Induced Fluorescence, SIF) from regular passenger flights. We developed a luggage-sized instrument based on the space remote-sensing techniques that JAXA has developed for Japan’s Greenhouse gas Observing SATellite (GOSAT). The instrument can be conveniently installed on a coach-class passenger seat without modifying the seat or the aircraft.ResultsThe first GOBLEU observation was made on the flight from the Tokyo Haneda Airport to the Fukuoka Airport, with only the NO2 module activated. The collected high-spatial-resolution NO2 data were compared to that from the TROPOspheric Monitoring Instrument (TROPOMI) satellite and surface NO2 data from ground-based air quality monitoring stations. While GOBLEU and TROPOMI data shared the major concentration patterns largely driven by cities and large point sources, regardless of different observation times, we found fine-scale concentration pattern differences, which might be an indication of potential room for GOBLEU to bring in new emission information and thus is worth further examination. We also characterized the levels of NO2 spatial correlation that change over time. The quickly degrading correlation level of GOBLEU and TROPOMI suggests a potentially significant impact of the time difference between CO2 and NO2 as an emission marker and, thus, the significance of co-located observations planned by future space missions.ConclusionsGOBLEU proposes aircraft-based, cost-effective, frequent monitoring of greenhouse emissions by GOBLEU instruments carried on regular passenger aircraft. Theoretically, the GOBLEU instrument can be installed and operated in most commercially used passenger aircraft without modifications. JAXA and ANAHD wish to promote the observation technique by expanding the observation coverage and partnership to other countries by enhancing international cooperation under the Paris Agreement.

Evaluating surface and subsurface fluxes in hydrological models to advance basin-scale operational water supply forecasting

ABSTRACT Comprehensive assessments of hydrological components are crucial for enhancing operational water supply simulations. However, hydrological models are often evaluated based on their surface flow simulations, while the validation of subsurface and groundwater components tends to be overlooked or not well documented. In this study, we evaluated the outputs of two hydrological models, the Large Basin Runoff Model (LBRM) and the Weather Research and Forecasting – Hydrological modeling extension package (WRF-Hydro), for potential implementation in operational water balance forecasting in the Great Lakes region. We examined the simulated hydrological variables including surface (e.g. snow water equivalent, evapotranspiration, and streamflow), subsurface (e.g. soil moisture at different layers), and groundwater components with observed or reference data from ground-based stations and remotely sensed images. The findings of this study provide valuable insights into the capabilities and limitations of each model. These findings contribute to more informed water management strategies for the Great Lakes region.

Evaluating soil moisture retrieval in Arctic and sub-Arctic environments using passive microwave satellite data

ABSTRACT Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m 3 m − 3 ) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the τ − ω model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m 3 m − 3 and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.