Search

Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products. [ABSTRACT FROM AUTHOR]

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring. [ABSTRACT FROM AUTHOR]

In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation. [ABSTRACT FROM AUTHOR]

The Amazon River is a large source of terrigenous dissolved organic carbon (tDOC) to the Atlantic Ocean. The fate of this tDOC in the ocean remains unclear despite its importance to the global carbon cycle. Here, we used two decades of satellite ocean color to describe variability in tDOC in the Amazon River plume. Our analyses showed that tDOC distribution has a distinct seasonal pattern, reaching northwest toward the Caribbean during high discharge periods, and moving eastward entrained in the North Brazil Current retroflection during low discharge periods. Elevated tDOC content extended beyond the shelfbreak in all months of the year, suggesting that cross‐shelf carbon transport occurs year‐round. Maximum variability was found at the plume core, where seasonality accounted for 40% of the total variance, while interannual variability accounted for 15% of the variance. Our results revealed a seasonal pattern in tDOC removal over the shelf with increased consumption in May when river discharge is high. Anomalies in tDOC removal over the shelf with respect to the seasonal cycle were significantly correlated with anomalies in tDOC concentration offshore of the shelfbreak with a lag of 30–40 days, so that anomalously high inshore tDOC removal was associated with anomalously low tDOC content offshore. This suggests that variability in the offshore transport of tDOC in the Amazon River plume is modulated by interannual changes in tDOC removal over the shelf. Plain Language Summary: The Amazon River is an important source of dissolved organic carbon (DOC) of terrigenous origin to the Atlantic Ocean. It is often difficult to characterize the distribution and variability of this material in the ocean due to the lack of high resolution in situ observations spanning long periods of time. Here, we used satellite observations of terrigenous DOC (tDOC) to describe the behavior of the plume at seasonal and interannual scales. Our analyses showed that enhanced tDOC content is observed offshore of the continental margin during all months of the year, extending toward the Caribbean during high river discharge conditions and eastward during low discharge conditions associated with variability in the large‐scale circulation. Our results also revealed that tDOC removal over the shelf varies seasonally, being enhanced earlier in the year when river discharge is high. Anomalies in tDOC removal over the shelf are significantly correlated with anomalies in tDOC content offshore with a lag of 30–40 days, suggesting that the offshore transport of carbon is modulated by tDOC consumption over the shelf. Key Points: Satellite ocean color was used to describe variability in terrigenous dissolved organic carbon (tDOC) in the Amazon River plumetDOC degradation and/or flocculation over the shelf increase early in the year when river discharge also increasesInterannual variability in tDOC content off the continental margin is related to variability in tDOC removal over the shelf [ABSTRACT FROM AUTHOR]

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[Formula: see text] m[Formula: see text]) 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 [Formula: see text] 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[Formula: see text] m[Formula: see text] 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.

This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using in situ measurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m3m−3 and an interquartile range of 0.030 m3m−3). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m3m−3 and an interquartile range of 0.031 m3m−3) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the in situ measurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the in situ sensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.

Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O'Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water ("Angola water") forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally. [ABSTRACT FROM AUTHOR]

Root zone soil moisture (RZSM) refers to the amount of water present in the soil layer where plants can freely absorb water, and its information is crucial for various applications such as hydrology and agriculture. SMOS and SMAP remote sensing satellites provide soil moisture (SM) data on a global scale but limit their sensing capability to a depth of approximately 5 cm. However, for a comprehensive understanding of soil water content in the root zone, a deeper insight into RZSM is essential, which extends from 5 cm to 100 cm. Hence, to bridge the gap between the surface SM and RZSM, SMOS surface SM information (5 cm) was integrated into the root zone (100 cm) using a simple subsurface physical model. SMOS RZSM data are available on a global scale with 25 km sampling on EASE grid 2, which provides a daily temporal scale from 2010 to the present. The main aims of this study were to i) check the efficacy of a simple model-based approach and ii) investigate the importance of remote sensing SM observations for the retrieval of RZSM. Here, we investigate the benefit of the simple model-based approach by comparing SMOS RZSM (simple model) and SMAP, ERA5, and GLDAS RZSM (complex model or data-assimilation) with in-situ SM. We then investigated the role of remote sensing SM observation in the retrieval of RZSM by comparing SMOS RZSM and SMAP RZSM products over rice-irrigated areas for dry seasons (minimal rainfall) in Telangana, South India. First, SMOS RZSM was evaluated with in-situ SM data for four distinct networks: SCAN, HOBE, SMOSMANIA, and Amma catch from 2011 to 2017. The results between SMOS RZSM and in-situ SM show an average correlation coefficient between 0.54 and 0.8 with an average unbiased root mean square difference (ubRMSD) within the threshold of 0.04 m3/m3. The average correlation coefficient between the RZSM and in-situ SM for the SMOS and SMAP RZSM shows better performance in the range (0.55 to 0.93) than the ERA5 and GLDAS RZSM in the range (0.20 to 0.93). Finally, the outcomes of SMOS and SMAP RZSM over irrigated areas show that only SMOS RZSM captures changes in SM dynamics due to irrigation, particularly during the dry season.

Background: Established health-related quality of life scores do not consider both subjective and objective indices of health. We propose the subjective and objective quality of life score (SOQOL) for the comprehensive assessment of health-related quality of life and aim to provide normative population data. The SOQOL is compatible with smartphone applications, allowing widespread use on a global scale. Methods: Normative SOQOL population data was sourced from pre-existing datasets on the EQ-5D-5L, daily step count, and walking speed. Normative values were calculated using weighted grand means. We trialled the SOQOL in a group of five patients presenting to a spinal neurosurgery clinic. Results: SOQOL scores decreased with age, and women had lower scores in every age group. In our case series, the spine patients with the biggest SOQOL deficit compared to age- and sex-matched population averages were found to be surgical while the rest were non-surgical. Conclusions: The SOQOL shows promise as a simple and effective scoring tool that is compatible with smartphones, potentially useful for screening in primary and specialized care settings, and for assessment following healthcare interventions. This study, however, is preliminary, and the findings are primarily suggestive. They underline the necessity for future, more comprehensive studies to validate and expand upon these initial observations. The conclusion of both this abstract and the full paper will clearly state these limitations and the preliminary nature of the study. [ABSTRACT FROM AUTHOR]

The present study provides a thorough evaluation of the SMOS surface soil moisture (SSM) product in a typical Mediterranean setting in Greece. For this purpose, a total of 4 agricultural sites were used for which co-orbital in-situ measurements from ground SSM sensors were available for year 2020. In this context, the effect of topographical and geomorphological features, land use/cover and the satellite orbit type and the Radio Frequency Interference (RFI) were also examined. A series of statistical metrics were computed, which allowed evaluating the agreement between the 2 datasets. In overall, results showed a reasonable agreement in specific land use/cover types between the SMOS product and the corresponding in-situ measurements obtained from the 0–5 cm soil moisture layer. In most cases, Root Mean Square Difference (RMSD) was close to 0.15 m3 m−3 (minimum 0.126 m3 m−3). Tomato and vineyard showed a satisfactory agreement compared to walnut and cotton crops. The autumn period had the highest agreement for tomato crop. The effect of RFI was also quite noticeable, as after the exclusion of pixels with high RFI, statistical agreement was noticeably improved. This study is, to our knowledge, one of the few that investigates in a Greek setting the accuracy of the SMOS product. The study results can contribute to the understanding of the practical value of the SMOS product in agricultural and arid/semi-arid Mediterranean environments while support efforts ongoing globally aiming at improving the SMOS SSM product accuracy. [ABSTRACT FROM AUTHOR]

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring.

With the increasing availability and diversity of satellite imagery, the multisensor fusion of data can more effectively address the improved monitoring of temporary water bodies. This study supports the attempt to apply well established methods to detect spatial and temporal changes in ephemeral shallow lakes in lowland karst terrain, as well as to improve the understanding concerning the dynamics of water storage and hydrological mechanisms during extreme precipitation events. Based on the joint analysis of Copernicus Sentinel SAR and optical mission data, as well as soil moisture and EO-based rainfall observations over the period of 2015–2020, we demonstrated the control of the karst system on the ephemeral lake appearances in the broader area of Chalkida (Evvia, Greece). A connection between the prolonged and extended water coverage in the ephemeral lakes and flooding in the area is documented. Our EO-supported findings may serve as indicators for flood alerts in future extreme precipitation events, improving responses in cases of emergencies.

Background: Preventing fatal traffic accidents towards Vision Zero is a challenge for the society. The collection of critical events from video recorded traffic data is of essential value for a better understanding on how and under what circumstances critical situations evolve. Identified behavioral patterns and derived infrastructural measures cannot only help to make driving safer, but also help to mature automated driving functions (ADFs) to make automated vehicles drive and interact more like humans especially in challenging situations. One flaw when developing ADFs is the dependency on synthetic simulated traffic scenarios. Method: In this paper, a novel probability-based framework is proposed allowing to measure the degree of criticality C(d) based on two dimensions explaining risk: severity (delta-v) and proximity (distance). Results: This metric is applied on real data of a roundabout. An initial evaluation of it was conducted using both a novel proposed method that takes the reaction of the second vehicle merged into account, and a practical application that shows a potential correlation between the traffic expert's perceived risk and the metric. Conclusion: Quantifying risk on each of the collected real traffic scenarios makes testing ADFs possible in further more reliable and significant scenarios like near-crashes. [ABSTRACT FROM AUTHOR]

Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O’Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water (“Angola water”) forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally.

Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

North American nitrous oxide (N2O) emissions over 2011–2018 are estimated using the CarbonTracker‐Lagrange regional inversion framework. Emissions are strongest in the Midwestern corn/soybean belt and display a distinct dual maxima seasonal pattern. The first maximum occurs in late winter/early spring, suggestive of freeze‐thaw (FT) effects on denitrification rates and associated N2O emissions. The second maximum occurs in late spring/early summer, consistent with a growing season nitrogen fertilizer‐driven source, although fertilizer applied in late fall may contribute to the FT pulse as well. Interannual variability in the first maximum correlates significantly to soil freeze thaw status derived from remote sensing data. A requisite frozen period in the preceding early winter appears necessary to create conditions for the N2O pulse after thawing. The FT pulse is a prominent feature of the annual cycle in Canadian cropland, where it may be of comparable magnitude to growing season emissions. In contrast, the growing season peak in N2O dominates the FT peak in the Midwestern Corn‐Soybean region of the United States. Key Points: Early spring peaks in N2O flux from North American agriculture correlate to satellite soil freeze/thaw status from the previous winterA requisite frozen period in early winter appears necessary to prepare the soil for the freeze‐thaw (FT) N2O pulseGrowing season N2O emissions dominate FT emissions in U.S. agriculture but the two have comparable magnitude in Canada [ABSTRACT FROM AUTHOR]

With the increasing availability and diversity of satellite imagery, the multisensor fusion of data can more effectively address the improved monitoring of temporary water bodies. This study supports the attempt to apply well established methods to detect spatial and temporal changes in ephemeral shallow lakes in lowland karst terrain, as well as to improve the understanding concerning the dynamics of water storage and hydrological mechanisms during extreme precipitation events. Based on the joint analysis of Copernicus Sentinel SAR and optical mission data, as well as soil moisture and EO-based rainfall observations over the period of 2015–2020, we demonstrated the control of the karst system on the ephemeral lake appearances in the broader area of Chalkida (Evvia, Greece). A connection between the prolonged and extended water coverage in the ephemeral lakes and flooding in the area is documented. Our EO-supported findings may serve as indicators for flood alerts in future extreme precipitation events, improving responses in cases of emergencies. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies. [ABSTRACT FROM AUTHOR]

The SM2RAIN (soil moisture to rain) model has been widely used for rainfall estimation worldwide. However, due to the lack of sufficient ground observation, the SM2RAIN model driven by different passive microwave soil moisture products over the Tibetan Plateau has not been fully validated. In this article, four widely used satellite microwave soil moisture products (including SMAP, ASCAT, SMOS, and AMSR2) were used as input data for rainfall estimation. Rainfall data from eight ground observation stations during 2016–2018 were used to evaluate the overall performance of the SM2RAIN algorithm under various soil moisture products at different time aggregation scales. In addition, different satellite soil moisture products were merged to evaluate whether the combined soil moisture products could improve the performance of the SM2RAIN model. Finally, the rainfall estimates with different soil moisture data were further evaluated and compared with two benchmark rainfall products (IMERG and ERA5). Results indicate that: 1) Overall, SM2RAIN-SMAP has the highest rainfall estimation accuracy, but with the time aggregation scale up to 30 days, the mean R of the four rainfall estimates could reach above 0.8 and the mean value of Kling–Gupta efficiency could reach above 0.8. 2) Combined satellite soil moisture products can significantly improve the rainfall estimates. The SM2RAIN model performed the best when SMAP and ASCAT soil moisture products were combined. 3) Using the SMAP product or combined soil moisture products yielded more accurate rainfall estimates than the two benchmark rainfall products (IMERG and ERA5).

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a τ - ω vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (τ , ω) and soil roughness ( S D ) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m3 for all stations, with a mean bias of 9.4 kg/m3. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (τ , ω , and S D ) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics. [ABSTRACT FROM AUTHOR]

The Atlantic Niño exerts great impact on surrounding weather and climate anomalies, leading to anomalous wind, temperature, precipitation, etc. However, the impact of Atlantic Niño on ocean salinity in the tropical Atlantic has not been well disclosed. The present study examines the Atlantic Niño induced sea surface salinity (SSS) distribution in both boreal summer and winter seasons by using the satellite data as well as various reanalysis and objective analysis data sets. It concludes that the summer Atlantic Niño leads to large fresh SSS anomalies in the eastern equatorial basin, while the winter Atlantic Niño leads to a meridional dipole structure of SSS anomalies. The former is mostly controlled by the dynamical processes of ocean, while the latter is largely controlled by the atmospheric processes. Accordingly, two SSS indices are developed to describe the relationships in the two seasons. The present study advances our understanding of the Atlantic Niño and its associated SSS variability and reveals the possible deficiencies of current reanalysis and objective analysis data sets in the tropical Atlantic Ocean.

The Gulf of Ob is a unique ecosystem characterized by a complex interaction of river and sea factors. It is exposed to extremely strong seasonal pressure of highly variable temperature, insolation, biogenic load and many other factors, including anthropogenic and technogenic effects. Operational satellite data on the hydrological regime of the Gulf of Ob are crucial for the analysis of different environmental and climate processes as well as assessment of the intensity and scale of anthropogenic and technogenic impacts on the adjacent areas. This study presents an analysis of seasonal and interannual variations of brightness temperature in different parts of the Gulf of Ob in the freezing period based on SMOS (Soil Moisture and Ocean Salinity) data. It was found that in the southern part of the Gulf of Ob, the seasonal and interannual brightness temperature dynamics are similar to freshwater lakes. However, the closer to the Kara Sea the more these dynamics deteriorate and, finally, in the northern part of the Gulf, become similar to those of the central Kara Sea. During the freezing period, changes in the brightness temperature dynamics occur in different parts of the Gulf of Ob. This is explained by an increase in the salinity of water under ice. It was shown that during winter seasons, the zone of fresh and salt waters mixing (the transition zone) can shift far to the south of the Gulf of Ob. Winter shift of the transition zone was compared with climate changes in the region that determine the river runoff and the state of permafrost. The revealed patterns of brightness temperature seasonal variations in different parts of the Gulf of Ob and the associated ice cover phases can be used to assess the hydrological regime in large estuaries of the Arctic in winter, as well as climate changes in the adjacent areas on the basis of satellite microwave radiometry data.

For the first time, an accurate and complete picture of Mixed Layer (ML) water mass dynamics can be inferred at high spatio-temporal resolution via the material derivative derived from Sea Surface Salinity/Temperature (SSS/T) and Currents (SSC). The product between this satellite derived material derivative and in-situ derived Mixed Layer Depth (MLD) provides a satellite based kinematic approach to the water mass (trans)formation framework (WMT/F) above ML. We compare this approach to the standard thermodynamic approach based on air-sea fluxes provided by satellites, an ocean state estimate and in-situ observations. Southern Hemisphere surface density flux and water mass (trans)formation framework (WMT/F) were analysed in geographic and potential density space for the year 2014. Surface density flux differences between the satellite derived thermodynamic and kinematic approaches and ECCO (an ocean state estimate) underline: 1) air-sea heat fluxes dominate variability in the thermodynamic approach; and 2) fine scale structures from the satellite derived kinematic approach are most likely geophysical and not artefacts from noise in SSS/T or SSC—as suggested by a series of smoothing experiments. Additionally, ECCO revealed surface density flux integrated over ML are positively biased as compared to similar estimates assuming that surface conditions are homogeneous over ML—in part owing to the e-folding nature of shortwave solar radiation. Major differences between the satellite derived kinematic and thermodynamic approaches are associated to: 1) lateral mixing and mesoscale dynamics in the kinematic framework; 2) vertical excursions of, and vertical velocities through the ML base; and 3) interactions between ML horizontal velocities and ML base spatial gradients.

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument's inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products' errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions. [ABSTRACT FROM AUTHOR]

The Subtropical Front (STF) plays a key role in the ecology of Chilean jack mackerel Trachurus murphyi. Nonetheless, there are few remote sensing studies of the STF in the open Southeast Pacific, and almost all of them have been conducted by satellite oceanographers in Russia and Ukraine to support respective large-scale fisheries of jack mackerel in this region. We reviewed these studies that documented long-term seasonal and interannual variability of the STF from sea surface temperature (SST) and sea surface height (SSH) data. We also mapped the STF from satellite sea surface salinity (SSS) data of the SMOS mission (2012–2019). The Subtropical Front consists of two fronts–North and South STF about 500 km apart–that border the Subtropical Frontal Zone (STFZ) in-between. The STF is density-compensated, with spatially divergent manifestations in temperature and salinity. In the temperature field, the STF extends in the WNW to ESE direction in the Southeast Pacific. In the salinity field, the STFZ appears as a broad frontal zone, extending zonally across the entire South Pacific. Three major types of satellite data-SST, SSH, and SSS-can be used to locate the STF. The SSH data is most advantageous with regard to the jack mackerel fisheries, owing to the all-weather capability of satellite altimetry and the radical improvement of the spatial resolution of SSH data in the near future. Despite the dearth of dedicated in situ studies of the South Pacific STFZ, there is a broad consensus regarding the STFZ being the principal spawning and nursing ground of T. murphyi and a migration corridor between Chile and New Zealand. Major data/knowledge gaps are identified, and key next steps are proposed to mitigate the data/knowledge gaps and inform fisheries management. [ABSTRACT FROM AUTHOR]

Analysis of seasonal and interannual variations of the brightness temperature of the Yenisei, Pechora, and Khatanga estuaries is performed using the SMOS (Soil Moisture and Ocean Salinity) satellite MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) data for the period 2012–2020. It is shown that during the freezing period, with low river flow, Khatanga and Pechora bays are strongly influenced by the salty waters of the Laptev Sea and the Pechora Sea, respectively. In Yenisei Bay, the analysis reveals two characteristic areas for the winter period, delimited by a narrow strait between Sopochnaya Karga and Oshmarin capes. In the southern part of the bay, the water remains fresh or slightly salty, while in the northern part (north of the Sopochnaya Karga post), the water is always brackish, as this is the mixing zone of the fresh water of the Yenisei River and the salty water of the Kara Sea. The sea areas adjacent to the Yenisei and Pechora estuaries (the Kara Sea and Pechora Sea, respectively) are dynamic zones with brackish or salty water and constantly breaking ice under the influence of hydrological and climatic factors. The results show that SMOS MIRAS data can be used to estimate water salinity and movement of the transition zone under the ice in large bays and estuaries; to analyze the stability and dynamics of the Arctic sea-ice cover; and to determine the beginning of ice melt in large sea and freshwater areas. [ABSTRACT FROM AUTHOR]

Coastal waters off west Greenland are strongly influenced by the input of low salinity water from the Arctic and from meltwater from the Greenland Ice Sheet. Changes in freshwater content in the region can play an important role in stratification, circulation, and primary production; however, investigating salinity variability in the region is challenging because in situ observations are sparse. Here, we used satellite observations of sea surface salinity (SSS) from the Soil Moisture and Ocean Salinity mission produced by LOCEAN and by the Barcelona Expert Center (SMOS LOCEAN and SMOS BEC) and from the Soil Moisture Active Passive mission produced by the Jet Propulsion Laboratory (SMAP JPL) as well as by Remote Sensing Systems (SMAP RSS) to investigate how variability in a narrow coastal band off west Greenland is captured by these different products. Our analyses revealed that the various satellite SSS products capture the seasonal freshening off west Greenland from late spring to early fall. The magnitudes of the freshening and of coastal salinity gradients vary between the products however, being attenuated compared to historical in situ observations in most cases. The seasonal freshening off southwest Greenland is intensified in SMAP JPL and SMOS LOCEAN near the mouth of fjords characterized by large inputs of meltwater near the surface, which suggests an influence of meltwater from the Greenland Ice Sheet. Synoptic observations from 2012 following large ice sheet melting revealed good agreement with the spatial scale of freshening observed with in situ and SMOS LOCEAN data. Our analyses indicate that satellite SSS can capture the influence of meltwater input and associated freshwater plumes off coastal west Greenland, but those representations differ between products. [ABSTRACT FROM AUTHOR]

We investigate the Chukchi and the Beaufort seas in the Arctic Ocean, where salty and warm Pacific Water flows in through the Bering Strait and interacts with the sea ice, contributing to its summer melt. Thanks to in situ measurements recorded by two saildrones deployed during summer 2019 and to refined sea ice filtering in satellite L‐band radiometric data, we demonstrate the ability of satellite sea surface salinity (SSS) observed by Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive to capture SSS freshening induced by sea ice melt. We refer to these freshening events as meltwater lenses (MWL). The largest MWL observed by the saildrones during this period occupied a large part of the Chukchi shelf, with a SSS freshening reaching −5 practical salinity scale, persisting for up to 1 month. This MWL restricted the transfer of air‐sea momentum to the upper ocean, as illustrated by measured wind speed and vertical profiles of currents. With satellite‐based sea surface temperature, satellite SSS provides a monitoring of the different water masses encountered in the region during summer 2019. Using sea ice concentration and estimated Ekman transport, we analyze the spatial variability of sea surface properties after the sea ice edge retreat over the Chukchi and the Beaufort seas. The two MWL captured by the saildrones and the satellite measurements resulted from different dynamics. Over the Beaufort Sea, the MWL evolution followed the meridional sea ice retreat whereas, in the Chukchi Sea, a large persisting MWL was generated by advection and subsequent melting of a sea ice filament. Plain Language Summary: The Arctic Ocean is an area of large variations in salinity. Salinity is a main driver of ocean circulation as it determines (with seawater temperature) the seawater density. However, very little is known about salinity variations there, due to the paucity of measurements near ice and in river plumes where surface water is freshest. Here, we use surface salinity measurements from two autonomous vehicles, named saildrones, to show that satellite measurements can identify the evolution of freshwater lenses that result from sea ice melt. Over the Chukchi and the Beaufort seas, sea surface salinity exhibits large seasonal changes, partly because of the sea ice melting. In this region, water from the North Pacific Ocean enters the Arctic Ocean, resulting in large gradients of salinity and temperature. During summer 2019, the saildrones measured the surface salinity and temperature variability as the sea ice retreated northwards. By comparing these data with the measurements from satellites, we showed that satellites can detect these pools of fresh surface water in the Arctic Ocean, increasing the field of application of satellites to understand changes in conditions that determine the Arctic's role in climate change. Key Points: Saildrones and L‐band radiometers detect large sea surface salinity variability induced by sea ice over the Chukchi and the Beaufort SeaLow surface salinity due to sea ice melting decreases the vertical extent of momentum transfer, inhibiting it beyond 10 m depthMeltwater lenses may persist more than 1 month and reach a surface salinity 5 pss fresher than surrounding waters [ABSTRACT FROM AUTHOR]

Colloidal In2O3NPs-MWCNTs heterostructure (NPs) were successfully synthesized by a facile one-step Q-switched Nd: YAG laser ablation of indium target in CNTs suspension at room temperature. Raman spectroscopy and optical absorption analysis of the prepared samples confirmed the formation of In2O3NPs-MWCNTs heterostructure. Transmission electron microscope (TEM) investigation of In2O3-CNTs structure and pure In2O3 nanoparticles revealed the formation of spherical nanoparticles with average size of 66 nm and 19 nm, respectively. The heterojunction photodetectors were fabricated by drop casting of colloidal In2O3NPs-MWCNTs NPs onto a single crystal silicon wafer. I–V characteristics of the In2O3NPs/Si and In2O3NPs-MWCNTs/Si heterojunctions under both dark and light conditions revealed rectifying properties and good photo-response. The built-in voltage was determined from the C–V measurements which revealed an abrupt junction and their values of 1.05 V and 0.59 V for In2O3NPs-MWCNTs/Si and In2O3NPs/Si, respectively. In2O3NPs-MWCNTs/Si photodetector demonstrated the highest responsivity and quantum efficiency of 1.3 A/W and 3.5 × 102% at 450 nm, respectively. This In2O3-MWCNTs heterostructure-based photodetector with improved performance may open the door to effective Vis–NIR photodetection applications.Graphical Abstract: Colloidal In2O3NPs-MWCNTs heterostructure (NPs) were successfully synthesized by a facile one-step Q-switched Nd: YAG laser ablation of indium target in CNTs suspension at room temperature. Raman spectroscopy and optical absorption analysis of the prepared samples confirmed the formation of In2O3NPs-MWCNTs heterostructure. Transmission electron microscope (TEM) investigation of In2O3-CNTs structure and pure In2O3 nanoparticles revealed the formation of spherical nanoparticles with average size of 66 nm and 19 nm, respectively. The heterojunction photodetectors were fabricated by drop casting of colloidal In2O3NPs-MWCNTs NPs onto a single crystal silicon wafer. I–V characteristics of the In2O3NPs/Si and In2O3NPs-MWCNTs/Si heterojunctions under both dark and light conditions revealed rectifying properties and good photo-response. The built-in voltage was determined from the C–V measurements which revealed an abrupt junction and their values of 1.05 V and 0.59 V for In2O3NPs-MWCNTs/Si and In2O3NPs/Si, respectively. In2O3NPs-MWCNTs/Si photodetector demonstrated the highest responsivity and quantum efficiency of 1.3 A/W and 3.5 × 102% at 450 nm, respectively. This In2O3-MWCNTs heterostructure-based photodetector with improved performance may open the door to effective Vis–NIR photodetection applications. [ABSTRACT FROM AUTHOR]

To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

Validating Sea Surface Salinity (SSS) data has become a key component of the Soil Moisture Ocean Salinity (SMOS) satellite mission. In this study, the gridded SMOS SSS products are compared with in situ SSS data from analyzed products, a ship-based thermosalinograph and a tropical moored buoy array. The comparison was conducted at different spatial and temporal scales. A regional comparison in the Baltic Sea shows that SMOS slightly underestimates the mean SSS values. The influence of river discharge overrides the temperature in the Baltic Sea, bringing larger biases near river mouths in warm seasons. The global comparison with two Optimal Interpolated (OI) gridded in situ products shows consistent large-scale structures. Excluding regions with large SSS biases, the mean ΔSSS between monthly gridded SMOS data and OI in situ data is −0.01 PSU in most open sea areas between 60°S and 60°N, with a mean Root Mean Square Deviation (RMSD) of 0.2 PSU and a mean correlation coefficient of 0.50. An interannual tendency of mean ΔSSS shifting from negative to positive between satellite SSS and in situ SSS has been identified in tropical to mid-latitude seas, especially across the tropical eastern Pacific Ocean. A comparison with collocated buoy salinity shows that on weekly and interannual scales, the SMOS Level 3 (L3) product well captures the SSS variations at the locations of tropical moored buoy arrays and shows similar performance with in situ gridded products. Excluding suspicious buoys, the synergetic analysis of SMOS, SMAP and gridded in situ products is capable of identifying the erroneous data, implying that satellite SSS has the potential to act as a real-time 27 Quality Control (QC) for buoy data. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns of the time and length scales of SSS variability estimated from the three satellite missions. The temporal scales of SSS variability vary from more than 90 days in the tropics to ~15 days in the Southern Ocean. The very short temporal scales (close to the Nyquist period) in some parts of the ocean are probably due to the high level of noise in the satellite data or the high noise-to-signal ratio. The longest temporal scales are observed along the South Pacific Convergence Zone (SPCZ) and in the central and western tropical Pacific. These areas are also related to the strongest ENSO-related signal in SSS. The processes governing the SSS variability and distribution are also non-stationary, such that the scales determined over different observation periods may differ. Dominant spatial scales of SSS variability are generally the longest (up to 150 km) in the tropics and the shortest (<60 km) in the subpolar regions. The distribution of the dominant spatial scales is not simply latitudinal but exhibits a more complex spatial pattern. In the tropics, there is slight east-west and inter-hemispheric asymmetry observed in the Pacific but absent in the other two oceans. The analysis also reveals that the length scales of SSS variability are highly anisotropic in the tropics (the zonal scales are generally shorter than the meridional ones) and become more isotropic towards higher latitudes. Regional differences in the estimates of the scales from the three satellite SSS datasets may arise due to differences in the observation duration, spatial resolution and/or different level of noise. [ABSTRACT FROM AUTHOR]

Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm). [ABSTRACT FROM AUTHOR]

Nanoparticles of tin oxide (SnO2) doped with iron oxide (α-Fe2O3) and Europium oxide (Eu2O3) were synthesized through the solid-state synthesis method. The phase analysis confirms the tetragonal rutile phase for all samples using X-ray diffraction (XRD). The luminescence intensity and expansion of lattice depend on doping percentage, which validates the successful blending of Fe and Eu in SnO2 lattice. The lattice dislocation and strain are calculated using the Williamson–Hall (WH) parcel, which also supports the development of the grain boundary in the doped samples. An increase in a large number of recombination centers leads to quenching in optical spectra by using the photoluminescence (PL) spectrum, and the shift in the band gap estimated from UV–visible absorption spectra confirms the impact of the grain boundary. A vibrating sample magnetometer (VSM) under the physical properties measurement system (PPMS) confirms room temperature ferromagnetism RTFM, and a scanning electron microscope (SEM) showed clear images of grain boundaries. Conductivity studies show an increase in resistance to Fe/Eu doping. Among all the tailored samples, Sn(1−x)Fex/EuO2, for x = 0.04 wt. % and 0.02 wt. % proved as a potential candidate for magneto-opto-electronic, i.e., spintronics device applications. [ABSTRACT FROM AUTHOR]

Remote sensing soil moisture (SM) has a low spatial resolution. By contrast, to validate remote SM maps, point measurements gathered from scattered in situ stations are typically used. Therefore, a single representative SM value for the entire domain is required. The simplest approach is the arithmetic mean. Here, eight upscaling methods for in situ SM based in geostatistical interpolations and physical characteristics were tested and used to validate the Soil Moisture and Ocean Salinity mission observations. Comparisons showed that the simple mean performs well and is similar to the proposed upscaling methods, while overcoming the need for ancillary data. [ABSTRACT FROM AUTHOR]

Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters.

To compare the accuracy of satellite salinity data of level-3 Soil Moisture Active Passive V4.0 (SSMAP) and debiased v5 CATDS level-3 Soil Moisture and Ocean Salinity (SSMOS) before and after tropical cyclones (TCs) in the Bay of Bengal (BoB), this study used the sea surface salinity of Argo (SArgo) to assess SSMAP and SSMOS before and after the passage of 10 TCs from 2015 to 2019. The results indicate that the SSMAP and SSMOS agreed well with SArgo before and after 10 TCs. It can be seen that the correlation between SSMAP and SArgo (before TCs: SSMAP = 0.95SArgo + 1.52, R2 = 0.83; after TCs: SSMAP = 0.87SArgo + 4.34, R2 = 0.79) was obviously higher than that of SSMOS and SArgo (before TCs: SSMOS = 0.68SArgo + 10.38, R2 = 0.62; after TCs: SSMOS = 0.88SArgo + 3.98, R2 = 0.58). The root mean square error (RMSE) was also significantly higher between SSMOS and SArgo (before TCs: 0.84 psu; after TCs: 0.78 psu) than between SSMAP and SArgo (before TCs: 0.58 psu; after TCs: 0.47 psu). In addition, this study compared SSMAP and SSMOS during two TCs that swept in nearshore and offshore waters, and the results show good agreement between SSMAP and SArgo in the nearshore and offshore waters of BoB. In the BoB, both SSMAP and SSMOS can retrieve sea surface salinity well, and SSMAP is overall better than SSMOS, but the SMOS salinity product can fill the gap of SMAP from 2010 to 2015. [ABSTRACT FROM AUTHOR]

In the Arctic, the sea surface salinity (SSS) plays a key role in processes related to water mixing and sea ice. However, the lack of salinity observations causes large uncertainties in Arctic Ocean forecasts and reanalysis. Recently the Soil Moisture and Ocean Salinity (SMOS) satellite mission was used by the Barcelona Expert Centre to propose an Arctic SSS product. In this study, we evaluate the impact of assimilating this data in a coupled ocean-ice data assimilation system. Using the Ensemble Kalman filter from July to December 2016, two assimilation runs assimilated two successive versions of the SMOS SSS product, on top of a pre-existing reanalysis run. The runs were validated against independent in situ salinity profiles in the Arctic. The results show that the biases and the Root Mean Squared Differences (RMSD) of SSS are reduced by 10% to 50% depending on areas and put the latest product to its advantage. The time series of Freshwater Content (FWC) further show that its seasonal cycle can be adjusted by assimilation of the SSS products, which is encouraging for its use in a long-time reanalysis to monitor the Arctic water cycle. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle. [ABSTRACT FROM AUTHOR]

A large part of the surface of the Greenland Ice Sheet (GrIS) and the margins of Antarctica are melting every summer, affecting their surface mass balance. Wet/dry snow status has been detected for decades using the peaks of brightness temperature at 19 GHz, and more recently at L-band (1.4 GHz) using both the SMOS and SMAP missions. SMOS owns a longer time series than SMAP with data since 2010, but the 52.5°incidence bin in the Level 3 (L3) product from Centre Aval de Traitement des Données SMOS (CATDS) that was previously used to detect melt suffers from a coarse spatial resolution. For this reason, we developed a new SMOS enhanced resolution brightness temperature (T B) product building on the radiometer version of the Scatterometter Image Reconstruction (rSIR) algorithm. We also exploited the SMOS L1C observations near 40°incidence angle instead of 52.5°as the native spatial resolution of SMOS is better at low incidence. The new product is posted on a 12.5 km polar stereographic grid and covers all the GrIS and Antarctica for 2010–2024 with twice-daily morning and afternoon acquisitions. The effective spatial resolution was evaluated to ∼ 30 km, a 30% enhancement compared to the SMOS L3TB at 40°and almost a 50% enhancement compared to the SMOS L3TB at 52.5°. Then, we applied a melt detection algorithm to both the enhanced resolution product at 40°and the L3TB product at 52.5°which is used in the literature. The spatial resolution enhancement results not only in the detection of smaller melt regions but also in a widespread increase in the annual number of melt days. This increase is larger than 30 days per year in the GrIS percolation area and on multiple Antarctic ice shelves. This is primarily due to the mix of dry and wet snow regions near the ice shelves grounding line, resulting in lower brightness temperature peaks in the SMOS L3TB product due to a large power spread. These findings highlight the dependence of melt detection in particular, and geophysical applications in general, on the spatial resolution of passive microwave observations. This study provides a new open dataset suitable to monitor melt at the surface and at depth on the two main ice-sheets. • Resolution enhancement of SMOS Level 1C brightness temperatures on the ice sheets. • 30% increase in effective spatial resolution at fixed incidence against the L3 TB. • Application to melt detection on the Greenland and Antarctic ice sheets. • Widespread increase in the annual number of melt days compared with the Level 3 TB. • More accurate melt detection near the grounding line of Antarctic ice shelves. [ABSTRACT FROM AUTHOR]