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Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change. [ABSTRACT FROM AUTHOR]

The present study focuses on the effects of vermicomposting on various soil properties, particularly emphasizing the dielectric characteristics of soil. Vermicomposting is known to significantly enhance soil fertility and improve its physical aspects such as aeration, porosity and water retention. In this analysis, a modified probe arrangement has been employed to determine how varying levels of vermicompost influence the soil's dielectric properties within the radio and microwave spectrum (10 MHz-14 GHz). A modified Dak-12 open-ended coaxial probe, paired with a vector network analyzer, was utilized for this purpose to overcome the drawbacks of standard arrangement. The results show that vermicompost strongly affects the soil's physical attributes (pH, bulk density, porosity) and also its chemical properties, along with its dielectric behavior. The study further explored the role of different factors such as the concentration of vermicompost, frequency and temperature on soil's dielectric response. To accurately and swiftly gauge these effects, Response Surface Methodology (RSM) has been employed to predict the dielectric properties (ε' and ε") of the soil. The high precision of these models, with R2 values of 0.9729 for ε' and 0.9487 for ε", underscores their effectiveness in evaluating the impact of vermicompost on various soil properties. [ABSTRACT FROM AUTHOR]

Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation. [ABSTRACT FROM AUTHOR]

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model's accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4). [ABSTRACT FROM AUTHOR]

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA's Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency's Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the "combined" European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient RALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (RALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10−16) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network. [ABSTRACT FROM AUTHOR]

Surface melt on ice shelves has been linked to hydrofracture and subsequent ice shelf breakup. Since the 1990s, scientists have been using microwave radiometers to detect melt on ice shelves and ice sheets by applying various statistical thresholding techniques to identify significant increases in brightness temperature that are associated with melt. In this study, instead of using a fixed threshold, we force the Snow Microwave Radiative Transfer model (SMRT) with outputs from the Community Firn Model (CFM) to create a dynamic, physics-based threshold for melt. In the process, we also combine our method with statistical thresholding techniques and produce microwave grain-size information. We run this "hybrid method" across the Larsen C ice shelf as well as 13 sites on the Antarctic Ice Sheet. Melt and non-melt days from the hybrid method and three statistical thresholding techniques match with the surface energy balance within 94 ± 1 %; the effect of melt on the passive microwaves is mostly binary and thus largely detectable by statistical thresholding techniques as well as physics-based techniques. Rather than always replacing statistical thresholding techniques with the hybrid method, we recommend using the hybrid method in studies where the melt volume or grain size is of interest. In this study, we show that the hybrid method can be used to (a) model dry-snow brightness temperatures of Antarctic snow and (b) derive a measure of grain size; therefore, it is an important step forwards towards using firn and radiative-transfer modeling to quantify melt rather than to simply detect melt days. [ABSTRACT FROM AUTHOR]

We have constructed a millimeter-wave free-space absorption spectrometer for high-resolution studies of jet-cooled molecules and weakly bound van der Waals complexes. The spectrometer employs a microwave frequency generator supplemented by active frequency multipliers and covers the spectral region from 50 to 170 GHz. A nozzle source is fixed on a rooftop mirror placed in the vacuum chamber in such a way that the millimeter-wave radiation propagates parallel to the molecular jet and makes two passes along it. Such arrangement of radiation and nozzle provides better sensitivity and higher spectral resolution compared to more common perpendicular configuration. Schottky diode detectors are used to measure absorption in the molecular gas flow. The observed linewidths are 30–40 kHz, and the accuracy of the line center determination is about 3–4 kHz. The presented spectra of the astrophysically relevant rare CO isotopologues, and the weakly bound NH3–H2 complexes demonstrate the potential of the newly built spectrometer. The line splittings arising from the hyperfine interactions of the nuclear spins of 13C for CO and 14N for NH3–H2 were resolved and analyzed. [ABSTRACT FROM AUTHOR]

High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China. Plain Language Summary: Reservoirs can supply water, control flood and provide electricity. Without high temporal frequency data on inundated area and water storage over a large spatial extent, we could not clearly understand how and to which degree numerous reservoirs have altered the natural water cycle and provided multiple benefits to people all across the world. The development of remote sensing technology has facilitated the long‐term monitoring of inundated areas and water levels of large quantities of reservoirs. However, high‐temporal‐resolution monitoring remains challenging. In this study, by developing a novel method which integrates active microwave remote sensing data with optical remote sensing observations, we largely improved the satellite‐based monthly inundated area and water storage change monitoring at almost all large reservoirs in China. The results reveal distinct seasonal patterns and interannual variations of reservoir water in different regions of China. Specifically, we found that reservoirs in Northeast China, the Yellow River basin, and Southwest China generally expanded, whereas those in Eastern and Northwest China generally shrank during 2017–2021. The climatic and anthropogenic impacts on the inter‐ and intra‐annual variations in reservoir water across different regions were also analyzed. Key Points: A new methodology for integrating synthetic aperture radar backscattering coefficients with optical remote sensing‐based inland surface water dynamics is developedMonthly inundated area and water storage changes of 721 large reservoirs in China during 2017–2021 are retrieved at improved accuracyImpacts of natural climate and human control on the inter‐ and intra‐annual variations in reservoir water across different regions in China are analyzed [ABSTRACT FROM AUTHOR]

Satellite‐retrieved vegetation optical depth (VOD) has provided extensive insights into global plant function (such as, carbon stocks, water stress, crop yields) because of VOD's ability to monitor plant water stress and biomass at near daily temporal frequency under all‐weather conditions. However, arguably, the greatest challenge with broadly applying VOD is its lack of validation partly because of VOD's simultaneous sensitivity to plant water status and biomass changes, as well as intensive methods required to measure these properties in‐situ. Here, inspired by the recent Yao et al. (2024), https://doi.org/10.1029/2023GL107121 article, I argue that VOD estimated from global navigation satellite systems (GNSS) and land surface models with plant hydraulic schemes are two emerging methods that show promise for more widely validating satellite‐based VOD. I encourage wider adoption of these approaches to validate and further advance satellite‐based VOD research. Plain Language Summary: Vegetation water content, or the amount of vegetation water mass over a given area, can be measured with satellites over large areas (36 km scales) globally at the microwave frequency. The satellite microwave index is called vegetation optical depth (VOD). Satellite VOD has provided new insights into the plant sciences such as into how grasses, crops, and forests respond to water stress. However, satellite VOD and relevant estimates of vegetation water content at 36 km scales are challenging to validate with ground‐based measurements. This is because vegetation water content changes with how saturated the plants are and how much dry plant biomass there is over a given area, both of which require labor‐intensive methods to measure. Recent technological advances are providing an opportunity to validate VOD more widely. First, field configurations using global navigation satellite systems (GNSS) can measure VOD using cost‐effective sensors that receive microwave signals from navigation satellites. Second, land surface models are now including methods to model vegetation water content and thus estimate VOD globally at the same spatial scales as satellite measurements. I recommend further development of these GNSS and model methods such that satellite VOD can be more comprehensively validated. Key Points: Satellite‐based vegetation optical depth (VOD) is sensitive to plant water status and biomass, but is challenging to validateGlobal navigation satellite systems (GNSS) and plant hydraulic models are emerging methods that can viably validate satellite VOD retrievals across larger spatial scalesI recommend establishing more GNSS sites and using models to validate satellite VOD across a broader range of global conditions [ABSTRACT FROM AUTHOR]

Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively. [ABSTRACT FROM AUTHOR]

The Arctic is one of the most important regions in the world's oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift. [ABSTRACT FROM AUTHOR]

As the Wind blows over the ocean surface, the generated waves are the primary factor affecting the microwave backscatter of the remote sensing radar. In addition, there are also some influencing factors, such as rainfall, which is the best-known atmospheric phenomenon. This paper proposes a wind and rain backscatter model whose angle of incidence is from 1 to 6 deg. The model is developed by considering the influence of ocean surface wind, atmospheric rain attenuation, rain volume backscatter and interactions between raindrops and ocean surface. The wind-induced component of the model adopted a quasi-specular theoretical model for the isotropic rough surface of Gaussian statistics. Rain attenuation is expressed as a coefficient of the model, and rain volume backscatter is expressed as a function of the rain volume backscattering coefficient, the thickness of precipitation layer and the incidence angle. Interactions between raindrops and ocean surface are complicated and deduced from the collocated dataset from the Precipitation Radar onboard the Tropical Rainfall Measuring Mission satellite and the European Centre for Medium-Range Weather Forecasts. Based on the collocated dataset, the result shows the proposed model can estimate reasonably accurate estimates surface backscatter within wind speeds range from 8 to 18 m s−1. [ABSTRACT FROM AUTHOR]

Soil fluoride is a critical determinant of soil fertility, human health and crop productivity. Soil fluoride can be increased by climatic conditions, irrigation water and anthropogenic activity, and it is important to control fluoride by understanding the complex relationships between atmospheric conditions and water systems. In this research, a detailed focus is on the hydrological and meteorological aspects of soil fluorides in semi-saturated and saturated soils to discuss the impact of irrigation, capillary rise and the combination of rainfall and anthropogenic activities such as fertilizer application on the soils in the dry spell and monsoon seasons of 2021 and 2022. A Sentinel-1 data can be used to estimate fluoride levels to the above soil conditions. In an effort to estimate fluoride levels in different hydro-meteorological scenarios, we have put forward a hypothesis that focuses on understanding the potential connections between hydro-meteorological factors (precipitation, groundwater levels, and temperature) and the levels of fluoride. The findings indicate that the extensive use of groundwater for irrigation leads to a rise in fluoride levels, posing a significant threat to crop health over time. Furthermore, the combined effects of irrigation and upheaval leaching on fluoride levels have shown strong statistical conformity (R2 > 0.85) with the relevant field-measured fluoride data for the year 2022. Importantly, areas affected by F upheaval are more sensitive to the sand and clay percentage in the soil because potential and dispersion behaviour enlarge the capillaries to decelerate the upward movement. A region-based discussion details the factors contributing to the increase of fluoride in soil helpful in taking remedial measures and mitigation plans. [ABSTRACT FROM AUTHOR]

Soil salinity at the crops' root zone is a significant environmental issue as it considerably affects agricultural productivity. This study was carried out to demonstrate the combined potential of microwave and multispectral remote sensing data products in identifying the accumulation of soil salinity both on the surface and in the root zone. The research was performed in the Vellore district, Tamil Nadu. The Landsat-8 imageries were employed to assess the soil salinity and moisture at the surface level in the dry and wet seasons using the Diagonal Soil Salinity Index and Modified Diagonal Soil Moisture Stress Index. Further, the soil moisture for the wet season was retrieved from the dielectric measurements of the Sentinel-1 data using Modified Dubois and Topp's Model. In the present study, Salinity Accumulation Index was proposed by integrating the resultant products of soil moisture and salinity retrieved from multispectral and microwave models. The results revealed that the lower (0.25–0.5) and higher range (0.5–1) of values of the proposed Salinity Accumulation Index were shown adequate statistical significance with in-situ salinity accumulation at the root zone (R2 = − 0.7) and surface level (R2 = 0.8), respectively. The trend analysis of the Normalized Differential Vegetation Index (2000–2019) with the results of Salinity Accumulation Index has demonstrated that a considerable proportion of the total agricultural area in the north-eastern part of the study region was majorly affected by the root zone salinity, while the southwestern part was detected with surface accumulated salinity. [ABSTRACT FROM AUTHOR]

• A phase error correction method (IFOA-JMEC) for InIRA images is proposed. • Ionospheric scintillation and atmospheric turbulence are main errors source for InIRA. • Correcting spatial-variant Error in range by dividing subblocks and interpolating. • Background atmosphere has little influence on InIRA height measurement accuracy. • Phase screen models for ionospheric scintillation and atmospheric turbulence. The interferometric imaging radar altimeter (InIRA) is a newly developed microwave remote sensing system in recent years. It is of great significance to analyze the main sources of its height measurement error through simulation, and to develop the corresponding image phase error correction methods. This can provide basis and reference for the subsequent correction of the real images phase error. In this study, according to the interaction principle between atmospheric interference and InIRA radar electromagnetic waves, the influence of background ionosphere and background troposphere on height measurement error is analyzed by introducing ionosphere and troposphere delays into echo signals respectively. The impact of ionospheric scintillation and atmospheric turbulence on the height measurement error is analyzed by building multi-layer phase screen models. Then according to the analysis results, a phase error correction method for InIRA images combined by the improved fruit fly optimization algorithm (IFOA) and the joint minimum entropy criterion (JMEC) is proposed. The image is divided into several sub-images with negligible spatial variation of phase error according to the principle of first range and then azimuth. IFOA is used to search the optimal phase compensation values iteratively to compensate the phase of all sub-images. When the joint entropy of the sub-images reaches the minimum value, the phase error correction of the whole InIRA image is completed. Experimental results show that the proposed method can correct the phase of InIRA images with inconspicuous features and polluted by weak ionospheric scintillation and atmospheric turbulence relatively quickly. After correction, the height measurement error can be reduced from decimeter level to centimeter level. The correction accuracy and efficiency of the proposed method are superior to those of the method combined by particle swarm optimization algorithm and joint minimum entropy criterion (PSO-JMEC), the method combined by genetic algorithm and joint minimum entropy criterion (GA-JMEC), the method combined by phase gradient autofocus and Map Drift (PGA-MD), and improved PGA-MD algorithm. [ABSTRACT FROM AUTHOR]

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms. [ABSTRACT FROM AUTHOR]

Multi-beam microwave antennas have attracted enormous attention owing to their wide range of applications in communication systems. Here, we propose a broadband metamaterial-based multi-beam Luneburg lens-antenna with low polarization sensitivity. The lens is constructed from additively manufactured spherical layers, where the effective permittivity of the constituting elements is obtained by adjusting the ratio of dielectric material to air. Flexible microstrip patch antennas operating at different frequencies are used as primary feeds illuminating the lens to validate the radiation features of the lens-antenna system. The proposed Luneburg lens-antenna achieves ±72° beam scanning angle over a broad frequency range spanning from 2 GHz to 8 GHz and presents a gain between 15.3 dBi and 22 dBi, suggesting potential applications in microwave- and millimeter-wave mobile communications, radar detection and remote sensing. [ABSTRACT FROM AUTHOR]

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the "zero trap" region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the "zero trap" region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. [ABSTRACT FROM AUTHOR]

GNSS-Reflectometry, a technique that harnesses the power of microwave remote sensing, is poised to revolutionize our ability to detect and monitor near-surface soil freeze/thaw processes. This technique's theoretical underpinnings are deeply rooted in the comprehensive explanation of the Zhang–Zhao dielectric constant model, which provides crucial insights into the behavior of frozen and thawed soils. The model elucidates how the dielectric properties of soil change as it transitions between frozen and thawed states, offering a scientific basis for understanding reflectivity variations. Furthermore, the theoretical framework includes a set of formulas that are instrumental in calculating reflectivity at Lower Right (LR) polarization and in deriving Dual-Polarization Differential Observables (DDMs). These calculations are pivotal for interpreting the signals captured by GNSS-R sensors, allowing for the detection of subtle changes in the soil's surface conditions. The evolution of GNSS-R as a tool for detecting freeze/thaw phenomena has been substantiated through qualitative analyses involving multiple satellite missions, such as SMAP-R, TDS-1, and CYGNSS. These analyses have provided empirical evidence of the technique's effectiveness, illustrating its capacity to capture the dynamics of soil freezing and thawing processes. In addition to these qualitative assessments, the application of a discriminant retrieval algorithm using data from CYGNSS and F3E GNOS-R has further solidified the technique's potential. This algorithm contributes to refining the accuracy of freeze/thaw detection by distinguishing between frozen and thawed soil states with greater precision. The deployment of space-borne GNSS-R for monitoring near-surface freeze/thaw cycles has yielded commendable results, exhibiting robust consistency and delivering relatively precise retrieval outcomes. These achievements stand as testaments to the technique's viability and its growing significance in the field of remote sensing. However, it is imperative to recognize and actively address certain limitations that have been highlighted in this review. These limitations serve as critical focal points for future research endeavors, directing the efforts toward enhancing the technique's overall performance and applicability. Addressing these challenges will be essential for leveraging the full potential of GNSS-R to advance our understanding and management of near-surface soil freeze/thaw processes. [ABSTRACT FROM AUTHOR]

The duration of ice-free periods in different parts of the Arctic Ocean plays a great role in processes in the climate system and defines the most comfortable sea ice conditions for economic activity. Based on satellite-derived sea ice concentration data acquired by passive microwave instruments, we identified the spatial distribution of the dates of sea ice retreat (DOR), dates of sea ice advance (DOA), and the resulting ice-free period duration (IFP) between these days for the Kara and Laptev seas during 1979–2022. The monthly decline in sea ice extent was detected from June to October in both seas, i.e., during the whole ice-free period. The annual mean sea ice extent during 2011–2021 decreased by 19.0% and 12.8% relative to the long-term average during 1981–2010 in the Kara and Laptev seas, respectively. The statistically significant (95% confidence level) positive IFP trends were detected for the majority of areas of the Kara and Laptev seas. Averaged IFP trends were estimated equal to +20.2 day/decade and +16.2 day/decade, respectively. The observed DOR tendency to earlier sea ice melting plays a greater role in the total IFP extension, as compared to later sea ice formation related to the DOA tendency. We reveal that regions of inflow of warm Atlantic waters to the Kara Sea demonstrate the largest long-term trends in DOA, DOR, and IFP associated with the decrease in ice coverage, that highlights the process of atlantification. Also, the Great Siberian Polynya in the Laptev Sea is the area of the largest long-term decreasing trend in DOR. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal "Remote Sensing" that focuses on monitoring terrestrial water resources using satellite sensors. The editorial acknowledges the increasing demand for water resources and the importance of accurately evaluating and tracking them. It discusses the advantages of using satellite technologies for monitoring water resources and highlights studies in the special issue that cover topics such as water body extraction, flood mapping, and coastal wetland dynamics monitoring. The editorial also mentions the use of specific satellite sensors, like satellite altimeters and satellite imagery, for acquiring surface water information. It addresses the challenges of acquiring 3D information about surface water and emphasizes the need to combine satellite observation with model simulation to improve monitoring and understanding of the water cycle. The authors suggest incorporating newly released satellite data, such as SWOT, to enhance the acquisition of information on inland areas, coastal areas, and oceans. [Extracted from the article]

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE) is a key variable in climate and hydrology studies. Yet, current SWE products mask out high-topography areas due to the coarse resolution of the satellite sensors used. The snow remote sensing community is hence pushing towards active-microwave approaches for global SWE monitoring. Designing a SWE retrieval algorithm is not trivial, as multiple combinations of snow microstructure representations and SWE can yield the same radar signal. Retrieval algorithm designs are converging towards forward modeling approaches using an educated first guess on the snowpack structure. Snow highly varies in space and time, especially in mountain environments where the complex topography affects atmospheric and snowpack state variables in numerous ways. In Canada, automatic weather stations are too sparse, and high-resolution numerical weather prediction systems have a maximal resolution of 2.5 km × 2.5 km, which is too coarse to capture snow spatial variability in a complex topography. In this study, we designed a subgridding framework for the Canadian High Resolution Deterministic Prediction System (HRDPS). The native 2.5 km × 2.5 km resolution forecast was subgridded to a 100 m × 100 m resolution and used as the input for snow modeling over two winters in Glacier National Park, British Columbia, Canada. Air temperature, relative humidity, precipitation, and wind speed were first parameterized regarding elevation using six automatic weather stations. We then used Alpine3D to spatialize atmospheric parameters and radiation input accounting for terrain reflections, and we performed the snow simulations. We evaluated modeled snowpack state variables relevant for microwave remote sensing against simulated profiles generated with automatic weather station data and compared them to simulated profiles driven by raw HRDPS data. The subgridding framework improves the optical grain size bias by 18 % on average and the modeled SWE by 16 % compared to simulations driven with raw HRDPS forecasts. This work could improve the snowpack radar backscattering modeling by up to 7 dB and serves as a basis for SWE retrieval algorithms using forward modeling in a Bayesian framework. [ABSTRACT FROM AUTHOR]

Exploring the Venusian lower atmosphere is crucial for studying the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a downward‐looking passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. A nonlinear iterative retrieval algorithm combining a radiative transfer (RT) model and an optimal‐estimation‐based inversion algorithm is established. With the RT model adapted to the Venusian atmosphere, simulations under different atmospheric conditions are performed to optimize the selection of frequency channels. The achievable altitude coverage, vertical resolution and corresponding expected precision of the temperature, SO2 and H2SO4(g) retrievals from the multi‐channel brightness temperature measurements are quantified via retrieval simulations. The temperature can be retrieved from the surface of Venus to an altitude of ∼61 km with a precision of 1–3.5 K and a vertical resolution of 6–15 km. A precision of 10%–35% is expected for SO2 in the ∼12–64 km altitude range and with a vertical resolution of 8–19 km. H2SO4(g) can be retrieved in the ∼36–54 km altitude range with a precision of 10%–30% and a vertical resolution of 6–13 km. Plain Language Summary: The hostile environment and thick sulfuric acid clouds make it difficult to measure the lower atmosphere of Venus. Although many attempts have been made, the existing measurements of the lower atmosphere have provided little information about the spatiotemporal distribution and variation in the temperature and sulfur compound profiles. This limits our understanding of the atmospheric circulation, surface‐atmosphere interactions, and origin and evolution of the Venusian atmosphere and climate. In this study, we investigate the theoretical capabilities of a passive microwave sounder placed in low Venus orbit to measure the temperature, sulfur dioxide (SO2) and gaseous sulfuric acid (H2SO4(g)) profiles. By model simulations, the frequency channel selection and performance simulation for the sounder are completed. Simulation results demonstrate that the sounder could provide the required high‐precision, high‐resolution and vertically resolved observations of the temperature, SO2 and H2SO4(g) in the Venusian lower atmosphere. Key Points: The ability of a microwave radiometer to measure the temperature, SO2 and H2SO4(g) profiles in the Venusian lower atmosphere is verifiedThe measurement channels of the radiometer are determined based on radiative transfer simulationsThe expected precision, vertical resolution and achievable altitude coverage are quantified via retrieval simulations [ABSTRACT FROM AUTHOR]

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50-100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010-2019). [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas. [ABSTRACT FROM AUTHOR]

The retrieval of continuous snow water equivalent (SWE) directly from passive microwave observations is hampered by ambiguity, which can potentially be mitigated by incorporating knowledge on snow hydrological processes. In this paper, we present a data assimilation (DA)-based SWE retrieval framework coupling the QCA-Mie scattering (DMRT-QMS) model (a dense medium radiative transfer (RT) microwave scattering model) and a one-dimensional column-based multiple-layer snow hydrology model. The snow hydrology model provides realistic estimates of the snowpack physical parameters required to drive the DMRT-QMS model. This paper devises a strategy to specify those internal parameters in the snow hydrology and RT models that lack observational records. The modeled snow depth is updated by assimilating brightness temperatures (Tbs) from the X, Ku, and Ka bands using an ensemble Kalman filter (EnKF). The updated snow depth is then used to predict the SWE. The proposed framework was tested using the European Space Agency's Nordic Snow Radar Experiment (ESA NoSREx) dataset for a snow field experiment from 2009 to 2012 in Sodankylä, Finland. The achieved SWE retrieval root mean square error of 34.31 mm meets the requirements of NASA and ESA snow missions and is about 70% less than the open-loop SWE. In summary, this paper introduces a novel SWE retrieval framework that leverages the combined strengths of a snow hydrology model and a radiative transfer model. This approach ensures physically realistic retrievals of snow depth and SWE. We investigated the impact of various factors on the framework's performance, including observation time intervals and combinations of microwave observation channels. Our results demonstrate that a one-week observation interval achieves acceptable retrieval accuracy. Furthermore, the use of multi-channel and multi-polarization Tbs is preferred for optimal SWE retrieval performance. [ABSTRACT FROM AUTHOR]

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as 'dielectric or conductive object + rough surface', 'dielectric or conductive floating object +rough' and it has certain generalization and reference, and it lays a foundation for solving the composite scattering coefficient of multiple medium targets in two-dimensional fractal sea surface. [ABSTRACT FROM AUTHOR]

Soil moisture is among the most essential variables in hydrology and earth science. Many satellite missions, such as AMSR-E/2, have been launched to observe it in broader spatial coverage to overcome the shortage of in situ observations. However, the satellite soil moisture products have been reported to comprise errors caused by the so-called "temperature effects" widely observed in dielectrically measured in situ volumetric soil water content (SWC). In this work, we confirmed the existence of these errors in AMSR2 soil moisture products. A new algorithm was developed to remove these errors using satellite data at ascending and descending overpasses. The application of this algorithm to both satellite and in situ data of SWC and soil temperature at the Mongolia site shows that the difference between SWC values at ascending and descending overpasses caused by temperature effects is effectively removed. We assess the impact of this removal method on satellite data by comparing it with in situ data, utilizing metrics such as the correlation coefficient and other widely adopted evaluation methods. It is shown that the difference between the original and corrected in situ SWC is much smaller than that between AMSR2 and in situ SWC, either corrected or not. The results indicate that the metric values between the corrected AMSR2 and in situ SWC, after removing apparent differences caused by temperature effects, slightly improved compared to those between the original AMSR2 and in situ SWC. Though these findings imply that the removed errors may not be the most dominant, considering the current significant difference between AMSR2 and in situ SWC, the removal makes the ascending and descending data have close characteristics. It may allow using data at both ascending and descending overpasses and double the temporal resolution of AMSR2 SWC data. [ABSTRACT FROM AUTHOR]

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature ( T B ) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath T B datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of T B signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks. [ABSTRACT FROM AUTHOR]

Photometric technology, characterized by its compact structure and relatively high stability, finds wide application in measuring airglow spectra. This instrumentation is anticipated to assume a pivotal role as the primary equipment for extensive network observations of middle and upper atmospheric temperatures in China, thereby providing crucial support for space environmental monitoring and atmospheric dynamic research. Nevertheless, susceptibility to various factors such as instrument inconsistency, variability in observation conditions, and alterations in the background atmospheric environment across different stations poses a challenge, potentially resulting in data inconsistencies in network observations. In response to these challenges, we propose a multiple-parameter iterative inversion (MPII) algorithm for temperature retrieval based on a mesospheric airglow spectrum photometer (MASP) developed by our research group. This algorithm accurately identifies the center of the image circle, corrects image distortion, and thereby obtains an accurate synthetic spectrum reflective of actual observations. It encompasses five adjustable parameters: sky background light, atmospheric temperature, filter temperature, optical system focal length, and degree of synthetic spectrum modulation. Compared to traditional methods, significant enhancements in the accuracy of the inverted temperature are achieved. To validate the effectiveness of the MPII algorithm, we conducted combined active and passive remote sensing synchronous measurements using MASP in conjunction with a sodium fluorescence Doppler lidar developed by the National Space Science Center. By utilizing the lidar temperature as a reference, atmospheric background radiation is mitigated from the MASP data, and the temperature is inverted using the MPII algorithm. Comparative analysis with the traditional method reveals that temperatures calculated by the MPII algorithm exhibit better consistency than those observed by the lidar. [ABSTRACT FROM AUTHOR]

The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Weather radars have been traditionally used to provide various convective-cloud characteristics. Here, we analyzed climatological convective-cell radar characteristics to obtain and assess the diurnal cycles of three convective-cell types – shallow, modest deep, and vigorous deep convective cells – that formed in the greater Houston area, using the National Weather Service radar from Houston, Texas, and a multi-cell identification and tracking algorithm. The examined dataset spans 4 years (2018–2021) and covers the warm-season months (June to September) in those years. The analysis showed clear diurnal cycles in cell initiation (CI) consistent with the sea breeze circulation and showed diurnal and normalized lifetime relationships in cell evolution parameters (e.g., maximum reflectivity, echo-top height, Geostationary Operational Environmental Satellite-16 (GOES-16) channel 13 brightness temperature, and the height of maximum reflectivity). The cell evolution is well represented by relationships between (1) the height and value of the maximum radar reflectivity, (2) the minimum GOES-16 channel 13 brightness temperature and the maximum vertically integrated liquid, (3) the maximum reflectivity and columnar-average reflectivity, and (4) the echo-top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. GOES-16 Aerosol Optical Depth values are also used as a proxy for cell initiation aerosol concentrations to investigate any potential relationships between initiation location and aerosol concentration. Overall, no significant relationships between initiation location and aerosol concentration were found for the three cell types investigated, but there are some minor differences in the pre-CI aerosol optical depth for vigorous deep convective cells. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product's design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains. [ABSTRACT FROM AUTHOR]

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran's I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. [ABSTRACT FROM AUTHOR]

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Abstract Satellite‐retrieved vegetation optical depth (VOD) has provided extensive insights into global plant function (such as, carbon stocks, water stress, crop yields) because of VOD's ability to monitor plant water stress and biomass at near daily temporal frequency under all‐weather conditions. However, arguably, the greatest challenge with broadly applying VOD is its lack of validation partly because of VOD's simultaneous sensitivity to plant water status and biomass changes, as well as intensive methods required to measure these properties in‐situ. Here, inspired by the recent Yao et al. (2024), https://doi.org/10.1029/2023GL107121 article, I argue that VOD estimated from global navigation satellite systems (GNSS) and land surface models with plant hydraulic schemes are two emerging methods that show promise for more widely validating satellite‐based VOD. I encourage wider adoption of these approaches to validate and further advance satellite‐based VOD research.

Abstract Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests.

Traditionally, the crystal video detector (CVD) has been used in a forward scatter (FS) radar to detect a moving target, with the requirement that some parameters of the target, such as its shape and locations, are available a priori. This work introduces a simple heuristic detector based on the variance changes in received signal amplitudes, eliminating the need for target parameters. The proposed detector is suitable for ground-based FS radars using opportunistic signals from broadband low Earth orbit (LEO) satellite communication systems, particularly for unmanned aerial vehicle (UAV) detection in a “sky fence” setup. Theoretical analysis of the receiver operating characteristics (ROC) indicates that although the CVD generally outperforms the heuristic detector, the performance gap is minimal with relatively small target-induced Doppler shifts compared with the signal bandwidth of the satellite communication system. Both detectors are effective for small target detection within the narrow beamwidth of antennas used in broadband LEO satellite communications. The proposed detector’s application in a simplistic sky fence scenario demonstrates its potential for UAV detection.

A new forward model (FM) was developed to characterize the influence of precipitation on L-band passive ocean surface measurements. The FM, which relates rain-induced brightness temperature (TB) variations to the rain rate and wind speed (WS), was established through a machine learning approach (referred to as the ML-FM). The soil moisture active passive (SMAP) data matched with integrated multisatellite retrievals for global precipitation measurement (IMERG) rain rate data and cross-calibrated multiplatform (CCMP) wind data were binned as a function of the rain rate, WS, and wind direction. The ML-FM was validated by comparing the simulated top-of-atmosphere (TOA) TB values with SMAP measurements. The results showed favorable agreement between the ML-FM outputs and SMAP data, with a root mean square error (RMSE) smaller than 0.55 K for both the horizontal and vertical polarizations. The validation results for ensuring more reasonable rainfall intensity distributions showed that the ML-FM returned stable results with a slightly reduced RMSE of ∼0.75 K for both the horizontal and vertical polarizations. Based on the ML-FM, we found that sea surface emission exhibited significant dependence on the rain rate for both polarizations. In addition, the ML-FM demonstrated signal saturation when the rain rate exceeded 45 mm/h, while precipitation slightly affected the directional characteristics of sea surface emission. These effects accounted for ∼0.3 K at a rain rate of 50 mm/h. Overall, our analyses demonstrated that the proposed ML-FM achieved superior performance in retrieving the TOA TB for both the vertical and horizontal polarizations with a higher accuracy than existing models.

The use of measurements from NASA's CYGNSS mission for the mapping of inland water bodies has been extensively demonstrated in past works. Despite this progress, the use of CYGNSS measurements for monitoring dynamic properties of water bodies, such as reservoir storage levels remains challenging given the need to combine observations over extended time periods or areas to improve water detection performance. This work explores the feasibility of an alternative sensing approach based on CYGNSS Level-1 products that has the potential to estimate reservoir water storage levels on intervals significantly finer compared to existing techniques. The method is explored using three example reservoirs with CYGNSS observations extending from October 2018 to December 2022. Retrieved water levels are found to be associated with relative water level root-mean-square-errors varying from 8–20% depending on the observed scenes' level of complexity. Correlations of up to $\approx\! \text{86}\%$ relative to in situ data are observed. The initial results presented in this work highlight the potential of spaceborne GNSS-R systems for reservoir water level monitoring.

In cold regions, the coexistence and interconversion of liquid water and ice in frozen soils have important implications for energy partitioning and surface runoff at the Earth's surface. Passive microwave remote sensing is crucial for the global monitoring of soil moisture (SM). However, current research on SM focuses mainly on unfrozen soil conditions. Limited studies have been conducted on variations in soil liquid water content throughout the freezing season. This article investigated the potential use of brightness temperature observations from the Soil Moisture Active Passive (SMAP) satellite for retrieving all-season liquid SM. The single-channel algorithm and the Zhang-Zhao dielectric model, which was specifically developed for freezing and thawing soils, achieved successful retrieval of liquid SM in both frozen and thawed soils, even when snow cover was present. The results indicate improved spatial coverage (during winter) and consistent spatial patterns in SM compared with the SMAP products. Validation at 17 SM networks suggests that the retrieved all-season liquid SM effectively captures the dynamic characteristics of each region with an average bias of 0.011 m3/m3, an average unbiased root mean square error of 0.056 m3/m3, and an average correlation coefficient of 0.76. The additional retrieval of unfrozen water content during the freezing season would enhance the monitoring and understanding of the hydrological cycle and energy balance in cold regions.

The range of salinity and temperature measurements used to develop models for the dielectric constant of sea water have in the past been limited to values appropriate for the open ocean. But there are important water bodies, such as the Great Salt Lake in Utah, with salinities much larger than those encountered in the open ocean. Extrapolating existing models to the values of salinity beyond the limits of the data used to create the model can result in unrealistic predictions in remote sensing applications. This can be prevented by using a model for conductivity based on the definition of salinity and ensuring that polynomials used to model the other unknown parameters result in bounded behavior at high salinity. New laboratory measurements with high salinity (up to 150 pss) are reported and used to test a model with these adjustments.

The launch of the CYGNSS constellation in 2016 represents NASA's first mission dedicated to the acquisition of GPS signals reflected off the earth’s surface. The major geophysical measurement objective is ocean surface winds with particular emphasis placed on the improved characterization of tropical cyclones. The conventional method for retrieving ocean surface winds using spaceborne Global Navigation Satellite System Reflectometry systems, of which CYGNSS is one example, is to measure the reflected power from the specular point on the ocean surface. Accurate measurement of surface power is complicated by calibration of the various power levels from the transmitting satellites, geometric effects, receiving system RF chain characteristics, among others. This article applies an alternative trailing edge slope (TES) method, which is more immune to end-to-end calibration uncertainties. It is nonetheless noted that because TES is a fundamentally shape-based measure of the waveform, it is susceptible to factors that may distort the measurements' slopes. This included measurements made with low SNR (on the order of 1.5 dB or less) due to low level of surface scattering or observations within the low gain portion of the receive antenna pattern. For this reason, in this work retrieval attempts are largely limited to those with a receive gain on the order of 10 dB or better. The TES method is based on the slope of the measured signal as a function of delay. Given the need for a delay space that extends beyond the $\approx$2.5–3.5 chips provided by CYGNSS's standard Level-1 measurements, its utility is explored using one of the constellation's special “full DDM” downlink modes. The TES method is shown to provide competitive retrievals including measurements from the surface, where the conventional method provides ocean wind speed estimates of lower accuracy. Tradeoffs using the TES method are discussed as well as simple improvements in the method are given. It is also noted that because with TES, measurements' slopes are the fundamental observable no calibration of measurements is required.

Snow depth (SD) plays a critical role in evaluating snow disasters, simulating hydrological processes and maintaining surface radiation balance etc. This study presents the first comprehensive evaluation of three SD products—SD-AMSR2, SD-Globsnow, and SD-SPM—derived from passive microwave remote sensing across the Mongolian Plateau. These products are developed through methods such as empirical microwave polarization difference, satellite and ground data integration, and machine learning with physical snow process models. The key findings included: 1) SD-SPM and SD-Globsnow had significantly lower values than SD-AMSR2, with SD-Globsnow showing less coverage in high-latitude areas. All products struggled with accuracy during deep snow and transitional seasons. 2) Validation against 66 stations showed that SD-SPM and SD-Globsnow were more accurate than SD-AMSR2, with SD-Globsnow performing best overall. SD-SPM was particularly stable and robust across all stations, excelling in May and September. 3) In snow identification, SD-SPM demonstrated the highest consistency of with fractional snow cover data, achieving over 80% agreement across most regions. 4) SD-SPM exhibited the strongest correlation with fractional snow cover data, especially in certain land cover types and timeframes. These results can provide insights for integrating multiple SD algorithms, aiding in snow disaster prediction and regional water resource management.

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.

Applying ICESat-2 and CryoSat-2 freeboards, the Arctic snow depth (ISCS) was obtained from October 2018 to April 2022. The results were assessed using Operation Ice Bridge (OIB) and Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) in situ observations and compared with frequently used remote sensing (AMSR-E/AMSR2), climatology (modified W99 snow climatology data; MW99), and modelled (Pan-Arctic Ice Ocean Modelling and Assimilation Systems; PIOMAS) snow products. The ISCS snow depth exhibited high correlations (r = 0.67) and small mean bias (0.01 ± 0.06 m) against OIB results. We found good correlations (statistically significant) between MOSAiC and all snow depth products, except for MW99. Mean and absolute biases between ISCS and MOSAiC in situ observations were 0.01 ± 0.06 and 0.04 ± 0.04 m, respectively. The ISCS revealed clear spatial distribution from the Canadian Arctic Archipelago to open sea areas. In contrast, the PIOMAS snow depth revealed weak autumn variation, whereas the MW99 snow depth yielded distinct seasonal variability but a weak interannual change. Snow depth in October showed large differences among the various products, from which the AMSR topped the snow depth in the first-year ice region, while MW99 had the highest values for the multiyear ice zone.

This study aims to reveal the relationships between understory coverage (principally, weed growth) beneath the canopies of Sumatran eucalyptus plantations and L-band backscatter data gathered by the PALSAR-2 Synthetic Aperture Radar (SAR) onboard the ALOS-2 satellite. Statistical analysis in conjunction with in situ measurements of forest structural parameters reveals that the SAR polarization ratio is significantly negatively correlated with understory coverage in forests taller than or equal to 10 m or above 2 years of age. Our field investigations confirm that a decrease in canopy coverage takes place some 2 years after transplanting, which in turn permits increased penetration (decreased attenuation) of microwave signals thereby enabling the SAR to detect changes in understory coverage. In addition, both growth and unmanaged remnants of the understory vegetation in older forests reduce evaporation from the ground, which in turn stabilizes soil-moisture levels throughout the year and allows co-polarized backscatter data (which might otherwise be dominated by soil-moisture changes) to contain significant information on the underlying vegetation. No correlation between the understory and SAR backscatter was recorded in younger forests undergoing programs of intensive weed management. This is presumably because strong canopy and trunk backscatter obscured the response from understory vegetation and because artificial changes were induced in vegetation water content as a result of the application of herbicides which in turn reduced the accuracy of some of our field measurements of understory coverage. This research nevertheless demonstrates an important potential application of active satellite microwave remote sensing. We show that satellite-based L-band SAR can be used as a tool to investigate coverage percentages of understory layers. This synoptic technique reduces the need for repeated field surveys across large areas of forest. It can enhance ecosystem assessment and improve understory coverage estimates in sparse forests and in various types of plantations.