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Osteochondromas, the most frequent type of bone tumor, develop from the metaphysis region of bones. Osteochondroma often occurs in bones, however, it is rare when it comes to rib tumors. It is often asymptomatic and observed incidentally.We present a case of a 14-year-old male patient who had been experiencing cough and mild fever for approximately a week. We requested a CXR PA and LAT. It showed a pulmonary nodule measuring 1.5 cm in diameter in the upper segment of the left lower lobe.Taking into account the unclear nature of the diagnosis, we requested CT scan with contrast of the chest to obtain a better view. It showed:The nodule visualized on the CXR corresponded to a posteriorly directed, well-defined lesion arising from the costal cartilage of the third left rib, measuring 1.2 × 1.3 × 1.1 cm, likely representing an osteochondroma.The case we discussed highlights a rib osteochondroma that initially seemed like a pulmonary nodule on an X-ray, pointing out the importance of using CT scans for accurate diagnosis in such cases, and reminding us to consider osteochondroma when we see similar symptoms and to regularly check the tumor with medical imaging after it's been confirmed by a pathological test.

Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km2). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10–20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr−1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources.

Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

La mission SMOS (Soil moisture and Ocean Salinity) a été lancée avec succès le 2 novembre 2009. Cette mission menée par l'ESA (Agence Spatiale Europénne) est dédiée à la mesure de l'humidité superficielle des sols sur les continents (avec une précision recherchée de 0,04m3/m3) et la salinité des océans (objectif 0.1psu). Ces deux quantités géophysiques sont très importantes car elle contrôle le budget énergétique à l'interface sol-atmosphère. Leur connaissance à l'échelle globale est utile pour les recherches sur le climat et la météorologie, en particulier pour les modèles de prévision numérique. Elles ont aussi un très grand potentiel un très grand nombre d'application, comme par exemple pour le suivi des ouragans ou la gestion des ressources en eau. Les six premiers mois ont été dédiés à la recette en vol qui a permis de vérifier le satellite le segment sol et l'étalonnage. Cette phase s'est achevée avec succès en mai 2010 et SMOS fonctionne de façon opérationnelle depuis, fournissant de données à la communauté internationale. Les performances de l'instrument sont globalement conformes aux spécifications. Cependant, les interférences radio sont présentes au-dessus de l'Europe, du Moyen-Orient et de l'Asie. Ces émissions parasites dans la bande protégée perturbent la mesure de façon significative. La génération des produits de niveau 2 et 3 est une activité en cours avec des améliorations régulières de sorties.

Study region: Two irrigation Consortia in Italy: the Chiese in Lombardia Region and the Capitanata in Puglia Region. Study focus: Drought monitoring is crucial especially where the rainfall regime is irregular and agriculture is mainly based on irrigated crops, such as in Mediterranean countries. In this work, the main objective is to develop an EO-based agricultural drought monitoring indicator (ADMIN) for operative irrigation networks management. The ADMIN indicator considers different levels of drought conditions combining anomalies of rainfall, soil moisture, land surface temperature and vegetation indices. Multiple remote sensing data, which differ on sensing techniques, spatial and temporal resolutions and spectral bands, are used and the uncertainty in anomalies computation derived from the use of multiple sources of remote sensing datasets is also discussed. The analyses are performed for the two Irrigation Consortia, which differ for climate, irrigation volumes and techniques, and crop types. New hydrological insights for the region: The obtained results show an inverse dependency between the cumulated ADMIN and the irrigation volumes in the Capitanata area (which has on-demand irrigation), whereas the dependency is much weaker in the Chiese Consortium (where irrigation is provided on a fixed basis, independently from the drought conditions). In both areas, the role of irrigation is critical to sustain production and preserve crop yields, which seem almost uncorrelated to ADMIN. ADMIN has demonstrated to outperform the use of single anomalies.

Ukraine was the seventh-biggest worldwide producer of wheat in 2021 before the Russian invasion. The war caused a disruption of the global wheat market but the impact on the different levels of the production chain remains unclear. Here we leverage high resolution satellite imagery and crop modeling to analyze specifically the impact of the war on the evolution of the dry aboveground biomass in wheat fields. We compare the Kherson region, a battlefield occupied by the Russian army in 2022 with the Poltava region which was not invaded. We also compare the wheat biomass in both regions in 2022 with the two previous years before the war. We find that the wheat biomass sharply declined in the Kherson region in 2022 only. We interpret this result as a consequence of the abandonment of invaded land by farmers. The lack of agricultural management, in particular the lack of fertilization and irrigation could explain the reduced crop growth in summer, thereby contributing to shortages in the wheat production chain.

Building climate data records of soil moisture (SM) requires computing long time series by merging retrievals from sensors on-board different satellites, which implies to perform a bias correction or rescaling on the original time series. Due to their long time span and high temporal frequency, model data could be used as a common reference for the rescaling. However, avoiding model dependence in observational climate data records is needed for some applications. In this article, the possibility of using as reference remote sensing data from one of the $L$-band sensors specifically designed to measure SM is discussed. Advanced Microwave Scanning Radiometer 2 SM time series were rescaled by matching their cumulative distribution functions (CDFs) to those of Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Global Land Data Assimilation System (GLDAS) NOAH model time series. The CDF computation was investigated as a function of the time series length, finding significant differences from four to nine years. Replacing temporal by spatial variance does not allow us to compute better CDFs from short time series. The rescaled time series show a high correlation ($R>0.8$) to the original ones and a low bias with respect to the reference ($< $0.03 m $^{3}\cdot$ m$^{-3}$). The time series rescaled using several SMOS or SMAP datasets were also evaluated against in situ measurements and show performances similar to or slightly better than those rescaled using the model GLDAS. The impact of random errors and gaps of the observational data into the rescaling was evaluated. These results show that it is actually possible to use $L$-band data as reference to rescale time series from other sensors to build long time series of SM.

The Madeira is one of the major tributaries of the Amazon River and is characterized by a large alluvial floodplain throughout the stream continuum. This study aims to better assess the hydrological functioning of the Madeira Basin over its alluvial floodplains at both local and global scales. We used the semi-distributed hydrological Soil and Water Assessment Tool (SWAT) model to simulate water discharge at a daily time step and water resources for each hydrological compartment. A new hydraulic module for water routing was implemented in the SWAT model considering the floodplain either as a simple reservoir or as a continuum where the water can flow along with the floodplain network. Both water surface estimated by L-band passive microwaves (SWAF data) and digital elevation model—shuttle radar topography mission (DEM–SRTM data) were used to delineate the floodplain, as inputs for the model. On the global scale, the amount of water stored in the Madeira floodplain is between 810 ± 230 km3 per year when the floodplains are delimited with SWAF and 1,300 ± 350 km3 per year with the DEM floodplain delineation between 2008 and 2018. Spatial altimetry (Jason 2-3) data were also applied to alluvial areas to validate the water height dynamic in floodplains at a local scale. Results show that more than 60% of the alluvial validation points display a correlation above 0.40 ± 0.02 regardless of the floodplain delineation. This study permits us to better characterize the spatio-temporal storage dynamics of the Madeira floodplains at both local and global scales, and it underlines the importance of a precise floodplain delineation, before computing biogeochemical fluxes and sediment yield.

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process.

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process. Results show that data assimilation leads to statistically significant better predictions than the simulation alone (from an RMSE of 9.88 q·ha−1 to an RMSE 7.49 q·ha−1). Significant improvement is achieved by relying on smoothed LAI rather than raw LAI. Nevertheless, there is still an over estimation of the grain yield that can be partially explained by the limiting factors observed on the fields and the forecast yield still need improvements to meet the required applications’ accuracy.

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO2, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO2 fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO2 flux (root mean square error (RMSE) = 0.97 gC·m−2·d−1 and determination coefficient (R2) = 0.83) and water flux (RMSE = 0.68 mm·d−1 and R2 = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m−2, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m3/m3 for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m3/m3 and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions.

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

Soil moisture plays an important role in the water, carbon, and energy cycles. We summarize the 13 articles collected in this Special Issue on soil moisture remote sensing across scales in terms of the spatial, temporal, and frequency scales studied. We also review these papers regarding the data, the methods, and the different applications discussed.

As a major hotspot of climate change, Lebanon suffers from a water resources crisis enhanced by the increase of anthropogenic activities. In this paper, the impacts of climate change and of the Syrian refugee crisis are combined with the impact of demographic growth to assess their aggregated impact on seawater intrusion in the Tripoli aquifer. A hydrogeological model is used to assess the seawater intrusion evolution for the next 25 years with respect to three phenomena: seawater rise, variation of incoming freshwater flux, and the change of the extraction rate of the pumping wells. Our study shows that the freshwater/seawater interface will move forward inland about 103 m in the next 25 years, leading to the salinization of the aquifer at the position of the pumping wells. Only about 1% of the advancement of the interface is associated with seawater rise; the remaining contributions are 79% from climate change and 20% from demographic growth. Adding the impact of migration reduces the contribution of climate change from 79% to 52%. The results suggest that the remediation solutions and recommendations should take into account the long-term impacts of climate change and the impact of population migration.

This study evaluates the accuracy of several recent remote sensing Surface Soil Moisture (SSM) products at sites in southwestern France. The products used are Soil Moisture Active Passive “SMAP” (level 3: 36 km × 36 km, level 3 enhanced: 9 km × 9 km, and Level 2 SMAP/Sentinel-1: 1 km × 1km), Advanced Scatterometer “ASCAT” (level 2 with three spatial resolution 25 km × 25 km, 12.5 km × 12.5 km, and 1 km × 1 km), Soil Moisture and Ocean Salinity “SMOS” (SMOS INRA-CESBIO “SMOS-IC”, SMOS Near-Real-Time “SMOS-NRT”, SMOS Centre Aval de Traitement des Données SMOS level 3 “SMOS-CATDS”, 25 km × 25 km) and Sentinel-1(S1) (25 km × 25 km, 9 km × 9 km, and 1 km × 1 km). The accuracy of SSM products was computed using in situ measurements of SSM observed at a depth of 5 cm. In situ measurements were obtained from the SMOSMANIA ThetaProbe (Time Domaine reflectometry) network (7 stations between 1 January 2016 and 30 June 2017) and additional field campaigns (near Montpellier city in France, between 1 January 2017 and 31 May 2017) in southwestern France. For our study sites, results showed that (i) the accuracy of the Level 2 SMAP/Sentinel-1 was lower than that of SMAP-36 km and SMAP-9 km; (ii) the SMAP-36 km and SMAP-9 km products provide more precise SSM estimates than SMOS products (SMOS-IC, SMOS-NRT, and SMOS-CATDS), mainly due to higher sensitivity of SMOS to RFI (Radio Frequency Interference) noise; and (iii) the accuracy of SMAP-36 km and SMAP-9 km products was similar to that of ASCAT (ASCAT-25 km, ASCAT-12.5 km and ASCAT-1 km) and S1 (S1-25 km, S1-9 km, and S1-1 km) products. The accuracy of SMAP, Sentinel-1 and ASCAT SSM products calculated using the average of statistics obtained on each site is defined by a bias of about −3.2 vol. %, RMSD (Root Mean Square Difference) about 7.6 vol. %, ubRMSD (unbiased Root Mean Square Difference)about 5.6 vol. %, and R coefficient about 0.57. For SMOS products, the station average bias, RMSD, ubRMSD, and R coefficient were about −10.6 vol. %, 12.7 vol. %, 5.9 vol. %, and 0.49, respectively.

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.

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.

Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm—Merge Active and Passive microwave Soil Moisture—uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m3/m3 and 0.069 m3/m3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

Water resources in Mediterranean coastal aquifers are subject to overexploitation leading to an increase in seawater intrusion. Based on the United Nations Environment Program, “UNEP” 75% of people in the world will live in coastal cities by 2020. This is having a major impact on the salinization process. This paper deals with the impact of demographic evolution on seawater intrusion and considers the case of the lower Tripoli aquifer in Lebanon. A numerical model based on the sharp interface approach is implemented using “Freefem++” to access the seawater intrusion. The model is verified against an analytic and a numerical solution. It is calibrated and validated against hydraulic head observations (RMSD = 0.819 m). Then several scenarios of pumping are applied based on available demographic growth rates to quantify the impact on seawater intrusion. The projection scenarios show that if the current pumping rates are maintained while maintaining the demographic evolution, the pumping wells will be salinized within 2 decades in the highly populated areas.