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21 pages, 12628 KiB  
Article
Convection Parameters from Remote Sensing Observations over the Southern Great Plains
by Kylie Hoffman and Belay Demoz
Sensors 2025, 25(13), 4163; https://doi.org/10.3390/s25134163 - 4 Jul 2025
Viewed by 316
Abstract
Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN), commonly used measures of the instability and inhibition within a vertical column of the atmosphere, serve as a proxy for estimating convection potential and updraft strength for an air parcel. In operational forecasting, CAPE [...] Read more.
Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN), commonly used measures of the instability and inhibition within a vertical column of the atmosphere, serve as a proxy for estimating convection potential and updraft strength for an air parcel. In operational forecasting, CAPE and CIN are typically derived from radiosonde thermodynamic profiles, launched only twice daily, and supplemented by model-simulated equivalent values. This study uses remote sensing observations to derive CAPE and CIN from continuous data, expanding upon previous research by evaluating the performance of both passive and active profiling systems’ CAPE/CIN against in situ radiosonde CAPE/CIN. CAPE and CIN values are calculated from Atmospheric Emitted Radiance Interferometer (AERI), Microwave Radiometer (MWR), Raman LiDAR, and Differential Absorption LiDAR (DIAL) systems. Among passive sensors, results show significantly greater accuracy in CAPE and CIN from AERI than MWR. Incorporating water vapor profiles from active LiDAR systems further improves CAPE values when compared to radiosonde data, although the impact on CIN is less significant. Beyond the direct capability of calculating CAPE, this approach enables evaluation of the various relationships between the water vapor mixing ratio, CAPE, cloud development, and moisture transport. Full article
(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)
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19 pages, 4762 KiB  
Article
Parametric Representation of Tropical Cyclone Outer Radical Wind Profile Using Microwave Radiometer Data
by Yuan Gao, Weili Wang, Jian Sun and Yunhua Wang
Remote Sens. 2025, 17(9), 1564; https://doi.org/10.3390/rs17091564 - 28 Apr 2025
Viewed by 378
Abstract
The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial [...] Read more.
The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial wind profile models for the TC outer area whose distance from TC center is larger than the radius of maximum wind (Rm). A total of 196 TC cases observed by SMAP were collected between 2015 and 2020, and their intensities range from tropical storm to category 5. Based on the wind and radius data, the key model parameters α and β were fitted through the Rankine vortex model and the tangential wind profile (TWP) Gaussian model, respectively. α and β control the rate of change of the tangential wind speed with radius. Subsequently, for the parametric representation of α and β, we extracted some TC wind filed parameters, such as maximum wind speed (Um), Rm, the average wind speed at Rm (Uma), and the average radius of 17 m/s (R17) and examined the relationship between Uma and Um, the relationship between Rm and R17, the relationship between α, Um and Rm, and the relationship between β, Um and Rm. According to the results, the new radial wind profile models were proposed, i.e., SMAP Rankine Model-4 (SRM-4), SMAP Rankine Model-5 (SRM-5), and SMAP Gaussian Model-1 (SGM-1). A significant advantage of these models is that they can simulate average wind distribution through the conversion from Um to Uma. Finally, comparisons were made between the new models and existing SRM-1, SRM-2, and SRM-3, according to the Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements of 126 TC cases. The results demonstrate that the SRM-4 simulated the radial wind profile best overall, with the lowest root mean-square error (RMSE) of 5.57 m/s, due to replacing the parameter Um with Uma, using Rankine vortex for α parameterization and modeling with adequate data. Moreover, the models outperform in the Atlantic Ocean, with a RMSE of 5.37 m/s. The new models have the potential to make a contribution to the study of ocean surface dynamics and be used for forcing numerical models under TC conditions. Full article
(This article belongs to the Special Issue Observations of Atmospheric and Oceanic Processes by Remote Sensing)
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23 pages, 10230 KiB  
Article
Revisiting the Role of SMAP Soil Moisture Retrievals in WRF-Chem Dust Emission Simulations over the Western U.S.
by Pedro A. Jiménez y Muñoz, Rajesh Kumar, Cenlin He and Jared A. Lee
Remote Sens. 2025, 17(8), 1345; https://doi.org/10.3390/rs17081345 - 10 Apr 2025
Viewed by 508
Abstract
Having good replication of the soil moisture evolution is desirable to properly simulate the dust emissions and atmospheric dust load because soil moisture increases the cohesive forces of soil particles, modulating the wind erosion threshold above which emissions occur. To reduce errors, one [...] Read more.
Having good replication of the soil moisture evolution is desirable to properly simulate the dust emissions and atmospheric dust load because soil moisture increases the cohesive forces of soil particles, modulating the wind erosion threshold above which emissions occur. To reduce errors, one can use soil moisture retrievals from space-borne microwave radiometers. Here, we explore the potential of inserting soil moisture retrievals from the Soil Moisture Active Passive (SMAP) satellite into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to improve dust simulations. We focus our analysis on the contiguous U.S. due to the presence of important dust sources and good observational networks. Our analysis extends over the first year of SMAP retrievals (1 April 2015–31 March 2016) to cover the annual soil moisture variability and go beyond extreme events, such as dust storms, in order to provide a statistically robust characterization of the potential added value of the soil moisture retrievals. We focus on the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model from the Air Force Weather Agency (GOCART-AFWA) dust emission parameterization that represents soil moisture modulations of the wind erosion threshold with a parameterization developed by fitting observations. The dust emissions are overestimated by the GOCART-AFWA parameterization and result in an overestimation of the aerosol optical depth (AOD). Sensitivity experiments show that emissions reduced to 25% in the GOCART-AFWA simulations largely reduced the AOD bias over the Southwest and lead to better agreement with the standard WRF-Chem parameterization of dust emissions (GOCART) and with observations. Comparisons of GOCART-AFWA simulations with emissions reduced to 25% with and without SMAP soil moisture insertion show added value of the retrievals, albeit small, over the dust sources. These results highlight the importance of accurate dust emission parameterizations when evaluating the impact of remotely sensed soil moisture data on numerical weather prediction models. Full article
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25 pages, 7772 KiB  
Article
Intercomparison of Antarctic Sea-Ice Thickness Estimates from Satellite Altimetry and Assessment over the 2019 Data-Rich Year
by Magata Jesaya Mangatane and Marcello Vichi
Remote Sens. 2025, 17(7), 1180; https://doi.org/10.3390/rs17071180 - 26 Mar 2025
Viewed by 401
Abstract
Sea-ice thickness (SIT) from satellites is an essential climate variable for characterizing the ice-covered ocean and evaluating numerical models. Although satellite altimetry is a promising option to obtain sustainable circum-Antarctic SIT estimates, its application in the Antarctic remains challenging due to the scarcity [...] Read more.
Sea-ice thickness (SIT) from satellites is an essential climate variable for characterizing the ice-covered ocean and evaluating numerical models. Although satellite altimetry is a promising option to obtain sustainable circum-Antarctic SIT estimates, its application in the Antarctic remains challenging due to the scarcity of systematic in situ observations for validation, and the most recent intercomparison exercise covered the period 2004 to 2008. In this study, we compared three empirical methods (ERM, BERM, and OLM) and one lidar-only method (ZIF) to determine SIT from lidar freeboard observations, one method combining lidar and radar freeboard observations (FDM), and one that uses both lidar freeboard observations and an independent snow depth dataset from passive microwaves (SICC). We first compared the methods in 2019, which is the only data-rich year during the overlapping period from 2019 to 2023. While the methods agreed on the broad spatial patterns of SIT, they clustered in two groups that have significant magnitude differences, with SICC and FDM estimating thicker ice and the lidar-based methods producing the thinnest estimates. Based on the limited set of available data, we did not find any single best performing method, and we recommend using the methods in a complementary way and to establish a network of concerted and continued field measurements for method assessments. Full article
(This article belongs to the Special Issue Applications of Satellite Altimetry in Ocean Observation)
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21 pages, 6227 KiB  
Article
Evaluation of Satellite-Based Global Navigation Satellite System Reflectometry (GNSS-R) Soil Moisture Products in Complex Terrain: A Case Study of the Yunnan–Guizhou Plateau
by Yixiao Liu, Yong Wang, Jingcheng Lai, Yunjie Lin and Leyan Shi
Remote Sens. 2025, 17(5), 887; https://doi.org/10.3390/rs17050887 - 2 Mar 2025
Cited by 1 | Viewed by 1041
Abstract
Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with [...] Read more.
Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m3/m3, RMSE = 0.14 m3/m3). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m3/m3) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology. Full article
(This article belongs to the Special Issue Earth Observation Satellites for Soil Moisture Monitoring)
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18 pages, 5508 KiB  
Article
Preliminary Assessment of the Impact of the Copernicus Imaging Microwave Radiometer (CIMR) on the Copernicus Mediterranean Sea Surface Temperature L4 Analyses
by Mattia Sabatini, Andrea Pisano, Claudia Fanelli, Bruno Buongiorno Nardelli, Gian Luigi Liberti, Rosalia Santoleri, Craig Donlon and Daniele Ciani
Remote Sens. 2025, 17(3), 462; https://doi.org/10.3390/rs17030462 - 29 Jan 2025
Viewed by 1172
Abstract
This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see [...] Read more.
This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see through clouds. Passive microwave (PMW) radiometers, on the other hand, offer monitoring capabilities in almost all weather conditions but typically at lower spatial resolutions. The CIMR mission represents a notable advance in microwave remote sensing of SSTs, as it will ensure a ≤15 km spatial resolution in the recovered SST field. Using an observing system simulation experiment (OSSE), this study evaluates the effect of inserting synthetic CIMR observations into the Copernicus Mediterranean SST analysis system, which is based on an optimal interpolation (OI) algorithm. The OSSE was conducted using data for the year 2017, including daily SST and salinity outputs from a Mediterranean Sea model, hourly precipitation rates from the IMERG, and wind and cloud cover data from ERA5. The results suggest that the improved spatial resolution and accuracy of the CIMR could potentially improve SST retrievals in the Mediterranean Sea, offering better insights for climate and environmental monitoring in semi-closed basins. Including CIMR data in the OI algorithm reduced the mean error and root mean square error (RMSE) of the SST analysis, especially under conditions of low IR coverage. The greatest improvements were found to occur in July, corresponding to coastal upwelling and Atlantic inflow into the Alboran Sea. Improvements ranged from 16% to 29%, with an overall improvement of 26% for the full year of 2017. In conclusion, this preliminary study indicates that Copernicus Mediterranean Sea HR SST products could benefit from the inclusion of the CIMR in the current IR sensor constellation. Full article
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40 pages, 1192 KiB  
Review
Combining Passive Infrared and Microwave Satellite Observations to Investigate Cloud Microphysical Properties: A Review
by Mariassunta Viggiano, Domenico Cimini, Maria Pia De Natale, Francesco Di Paola, Donatello Gallucci, Salvatore Larosa, Davide Marro, Saverio Teodosio Nilo and Filomena Romano
Remote Sens. 2025, 17(2), 337; https://doi.org/10.3390/rs17020337 - 19 Jan 2025
Cited by 1 | Viewed by 7449
Abstract
Clouds play a key role in the Earth’s radiation budget, weather, and hydrological cycle, as well as the radiative and thermodynamic components of the climate system. Spaceborne observations are an essential tool to detect clouds, study cloud–radiation interactions, and explore their microphysical properties. [...] Read more.
Clouds play a key role in the Earth’s radiation budget, weather, and hydrological cycle, as well as the radiative and thermodynamic components of the climate system. Spaceborne observations are an essential tool to detect clouds, study cloud–radiation interactions, and explore their microphysical properties. Recent advancements in spatial, spectral, and temporal resolutions of satellite-borne measurements and the increasing variety of orbits and observing geometries offer the opportunity for more efficient and sophisticated retrieval procedures, leading to the more accurate estimation of cloud parameters. However, despite the availability of near-coincident observations of the same atmospheric state, the synergy between the whole set of acquired information is still largely underexplored. The use of synergy is often invoked to optimize the exploitation of the available information, but it is rarely implemented. Indeed, the strategy currently used in most cases is that retrievals are performed separately for each instrument and, only later, the retrieved products are combined. In this framework, therefore, there is a strong need to study and exploit the synergy potential of the instruments currently in orbit or that will be put in orbit in the next few years. This paper reviews the efforts already made in this direction, combining passive infrared and microwave to retrieve cloud microphysical properties. We provide readers with a framework to interpret the state of the art, highlighting the pros and cons of the various approaches currently used with a look to the most promising methodologies to be deployed to address the challenges of this field. Full article
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18 pages, 12913 KiB  
Article
Soil Organic Carbon Estimation and Transfer Framework in Agricultural Areas Based on Spatiotemporal Constraint Strategy Combined with Active and Passive Remote Sensing
by Jiaxin Qian, Jie Yang, Weidong Sun, Lingli Zhao, Lei Shi, Hongtao Shi, Lu Liao, Chaoya Dang and Qi Dou
Remote Sens. 2025, 17(2), 333; https://doi.org/10.3390/rs17020333 - 19 Jan 2025
Viewed by 1155
Abstract
Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active and passive remote sensing for SOC estimation modeling in agricultural areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polarization and C-band [...] Read more.
Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active and passive remote sensing for SOC estimation modeling in agricultural areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polarization and C-band dual-polarization), multi-spectrum (MS) data, and brightness temperature (TB) data. The performance of five advanced machine learning regression (MLR) models for SOC modeling was assessed, focusing on spatial interpolation accuracy and cross-spatial transfer accuracy, using two field observation datasets for modeling and validation. Results indicate that the SOC estimation accuracy when using MS data alone is comparable to that of using TB data alone, and both perform slightly better than SAR data. Radar cross-polarization ratio index, microwave polarization difference index, shortwave infrared reflectance, and soil parameters (elevation and soil moisture) demonstrate high correlation with the measured SOC. Incorporating temporal features, as opposed to single-phase features, allows each regression model to reach its upper limit of SOC estimation accuracy. The spatial interpolation accuracy of each MLR algorithm is satisfactory, with the Gaussian process regression (GPR) model demonstrating optimal modeling performance. When SAR, MS, or TB data are used individually in modeling, the estimation errors (RMSE) for SOC are 0.637 g/kg, 0.492 g/kg, and 0.229 g/kg for the SMAPVEX12 sampling campaign, and 0.706 g/kg, 0.454 g/kg, and 0.474 g/kg for the SMAPVEX16-MB sampling campaign, respectively. After incorporating soil moisture and topographic factors, the above RMSEs for SOC are further reduced by 57.8%, 35.6%, and 3.5% for the SMAPVEX12, and by 18.4%, 8.8%, and 3.4% for the SMAPVEX16-MB, respectively. However, cross-spatial transfer accuracy of the regression models remains limited (RMSE = 0.866–1.043 g/kg and 0.995–1.679 g/kg for different data sources). To address this, this study reduces uncertainties in SOC cross-spatial transfer by introducing terrain factors sensitive to SOC (RMSE = 0.457–0.516 g/kg and 0.799–1.198 g/kg for different data sources). The proposed SOC estimation and transfer framework, based on active and passive remote sensing data, provides guidance for high-resolution regional-scale SOC mapping and applications. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Low-Cost Soil Carbon Stock Estimation)
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32 pages, 11960 KiB  
Article
Estimation of 1 km Dawn–Dusk All-Sky Land Surface Temperature Using a Random Forest-Based Reanalysis and Thermal Infrared Remote Sensing Data Merging (RFRTM) Method
by Yaohai Dong, Xiaodong Zhang, Xiuqing Hu, Jian Shang and Feng Zhao
Sensors 2025, 25(2), 508; https://doi.org/10.3390/s25020508 - 16 Jan 2025
Viewed by 842
Abstract
All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)–passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution [...] Read more.
All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)–passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time. Therefore, a gap still remains in terms of all-sky LST data with a higher temporal resolution or at other times (e.g., dawn–dusk time). Under this background, this study merged the observations of the Medium Resolution Spectrum Imager (MERSI-LL) on board the dusk–dawn-orbit Fengyun (FY)-3E satellite and Global Land Data Assimilation System (GLDAS) data to estimate dawn–dusk 1 km all-sky LST using a random forest-based method (RFRTM). The results showed that the model had good robustness, with an STD of 0.62–0.86 K of the RFRTM LST, compared with the original MERSI-LL LST. Validation against in situ LST showed that the estimated LST had an accuracy of 1.34–3.71 K under all-sky conditions. In addition, compared with the dawn–dusk LST merged from MERSI-LL and the Special Sensor Microwave Imager/Sounder (SSMI/S), the RFRTM LST showed better performance in accuracy and image quality. This study’s findings are beneficial for filling the gap in all-sky LST at high spatiotemporal resolutions for associated applications. Full article
(This article belongs to the Section Environmental Sensing)
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23 pages, 3271 KiB  
Article
A Comparison of Physical-Based and Statistical-Based Radiative Transfer Models in Retrieving Atmospheric Temperature Profiles from the Microwave Temperature Sounder-II Onboard the Feng-Yun-3 Satellite
by Qiurui He, Xiao Guo, Ruiling Zhang, Jiaoyang Li, Lanjie Zhang, Junqi Jia and Xuhui Zhou
Atmosphere 2025, 16(1), 44; https://doi.org/10.3390/atmos16010044 - 2 Jan 2025
Viewed by 757
Abstract
The statistical retrieval of atmospheric parameters will be greatly affected by the accuracy of the simulated brightness temperatures (BTs) derived from the radiative transfer model. However, it is challenging to further improve a physical-based radiative transfer model (RTM) developed based on the physical [...] Read more.
The statistical retrieval of atmospheric parameters will be greatly affected by the accuracy of the simulated brightness temperatures (BTs) derived from the radiative transfer model. However, it is challenging to further improve a physical-based radiative transfer model (RTM) developed based on the physical mechanisms of wave transmission through the atmosphere. We develop a deep neural network-based RTM (DNN-based RTM) to calculate the simulated BTs for the Microwave Temperature Sounder-II onboard the Fengyun-3D satellite under different weather conditions. The DNN-based RTM is compared in detail with the physical-based RTM in retrieving the atmospheric temperature profiles by the statistical retrieval scheme. Compared to the physical-based RTM, the DNN-based RTM can obtain higher accuracy for simulated BTs and enables the statistical retrieval scheme to achieve higher accuracy in temperature profile retrieval in clear, cloudy, and rainy sky conditions. Due to its ability to simulate microwave observations more accurately, the DNN-based RTM is valuable for the theoretical study of microwave remote sensing and the application of passive microwave observations. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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23 pages, 7503 KiB  
Article
Circumferential Background Field Temperature Inversion Prediction and Correction Based on Ground-Based Microwave Remote Sensing Data
by Changzhe Wu, Yuxin Zhao, Peng Wu and Xiong Deng
J. Mar. Sci. Eng. 2024, 12(12), 2344; https://doi.org/10.3390/jmse12122344 - 20 Dec 2024
Viewed by 775
Abstract
Microwave radiometers are passive remote sensing devices that provide important observational data on the state of the oceanic and terrestrial atmosphere. Temperature retrieval accuracy is crucial for radiometer performance. However, inversions during strong convective weather or seasonal phenomena are short-lived and spatially limited, [...] Read more.
Microwave radiometers are passive remote sensing devices that provide important observational data on the state of the oceanic and terrestrial atmosphere. Temperature retrieval accuracy is crucial for radiometer performance. However, inversions during strong convective weather or seasonal phenomena are short-lived and spatially limited, making it challenging for neural network algorithms trained on historical data to invert accurately, leading to significant errors. This paper proposes a long short-term memory (LSTM) network forecast correction model based on the temperature inversion phenomenon to resolve these large temperature inversion errors. The proposed model leverages the seasonal periodicity of atmospheric temperature profiles in historical data to form a circumferential background field, enabling the prediction of expected background profiles for the forecast day based on temporal and spatial continuity. The atmospheric profiles obtained using the radiometer retrieval are compensated with the forecast temperature inversion vector on the forecast day to obtain the final data. In this study, the accuracy of the forecast correction model was verified utilizing meteorological records for the Taizhou area from 2013 to 2017. Using a hierarchical backpropagation network based on the residual module for comparison, which had a forecast accuracy error of 0.0675 K, the error of our new model was reduced by 34% under the temperature inversion phenomenon. Meanwhile, error fluctuations were reduced by 33% compared with the residual network algorithm, improving the retrieval results’ stability in the temperature inversion state. Our results provide insights to improve radiometer remote sensing accuracy. Full article
(This article belongs to the Section Marine Environmental Science)
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34 pages, 10549 KiB  
Review
Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
by Ruifang Guo, Xingwang Fan, Han Zhou and Yuanbo Liu
Remote Sens. 2024, 16(24), 4753; https://doi.org/10.3390/rs16244753 - 20 Dec 2024
Cited by 2 | Viewed by 1517
Abstract
Satellite remote sensing complements rain gauges and ground radars as the primary sources of precipitation data. While significant advancements have been made in spaceborne precipitation estimation since the 1960s, the emergence of multi-sensor precipitation estimation (MPE) in the early 1990s revolutionized global precipitation [...] Read more.
Satellite remote sensing complements rain gauges and ground radars as the primary sources of precipitation data. While significant advancements have been made in spaceborne precipitation estimation since the 1960s, the emergence of multi-sensor precipitation estimation (MPE) in the early 1990s revolutionized global precipitation data generation by integrating infrared and microwave observations. Among others, Global Precipitation Measurement (GPM) plays a crucial role in providing invaluable data sources for MPE by utilizing passive microwave sensors and geostationary infrared sensors. MPE represents the current state-of-the-art approach for generating high-quality, high-resolution global satellite precipitation products (SPPs), employing various methods such as cloud motion analysis, probability matching, adjustment ratios, regression techniques, neural networks, and weighted averaging. International collaborations, such as the International Precipitation Working Group and the Precipitation Virtual Constellation, have significantly contributed to enhancing our understanding of the uncertainties associated with MPEs and their corresponding SPPs. It has been observed that SPPs exhibit higher reliability over tropical oceans compared to mid- and high-latitudes, particularly during cold seasons or in regions with complex terrains. To further advance MPE research, future efforts should focus on improving accuracy for extremely low- and high-precipitation events, solid precipitation measurements, as well as orographic precipitation estimation. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)
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21 pages, 6412 KiB  
Article
Detection of Flight Target via Multistatic Radar Based on Geosynchronous Orbit Satellite Irradiation
by Jia Dong, Peng Liu, Bingnan Wang and Yaqiu Jin
Remote Sens. 2024, 16(23), 4582; https://doi.org/10.3390/rs16234582 - 6 Dec 2024
Viewed by 1225
Abstract
As a special microwave detection system, multistatic radar has obvious advantages in covert operation, anti-jamming, and anti-stealth due to its configuration of spatial diversity. As a high-orbit irradiation source, a geosynchronous orbit satellite (GEO) has the advantages of a low revisit period, large [...] Read more.
As a special microwave detection system, multistatic radar has obvious advantages in covert operation, anti-jamming, and anti-stealth due to its configuration of spatial diversity. As a high-orbit irradiation source, a geosynchronous orbit satellite (GEO) has the advantages of a low revisit period, large beam coverage area, and stable power of ground beam compared with traditional passive radar irradiation sources. This paper focuses on the key technologies of flight target detection in multistatic radar based on geosynchronous orbit satellite irradiation with one transmitter and multiple receivers. We carry out the following work: Firstly, we aim to address the problems of low signal-to-noise ratio (SNR) and range cell migration of high-speed cruise targets. The Radon–Fourier transform constant false alarm rate detector-range cell migration correction (RFT-CFAR-RCMC) is adopted to realize the coherent integration of echoes with range cell migration correction (RCM) and Doppler phase compensation. It significantly improves the SNR. Furthermore, we utilize the staggered PRF to solve the ambiguity and obtain multi-view data. Secondly, based on the aforementioned target multi-view detection data, the linear least square (LLS) multistatic positioning method combining bistatic range positioning (BR) and time difference of arrival positioning (TDOA) is used, which constructs the BR and TDOA measurement equations and linearizes by mathematical transformation. The measurement equations are solved by the LLS method, and the target positioning and velocity inversion are realized by the fusion of multistatic data. Finally, using target positioning data as observation values of radar, the Kalman filter (KF) is used to achieve flight trajectory tracking. Numerical simulation verifies the effectiveness of the proposed process. Full article
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23 pages, 7028 KiB  
Article
An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China
by Chuanxiang Yi, Xiaojun Li, Zanpin Xing, Xiaozhou Xin, Yifang Ren, Hongwei Zhou, Wenjun Zhou, Pei Zhang, Tong Wu and Jean-Pierre Wigneron
Remote Sens. 2024, 16(22), 4235; https://doi.org/10.3390/rs16224235 - 14 Nov 2024
Cited by 1 | Viewed by 1024
Abstract
Accurate surface soil moisture (SM) data are crucial for agricultural management in Jiangsu Province, one of the major agricultural regions in China. However, the seasonal performance of different SM products in Jiangsu is still unknown. To address this, this study aims to evaluate [...] Read more.
Accurate surface soil moisture (SM) data are crucial for agricultural management in Jiangsu Province, one of the major agricultural regions in China. However, the seasonal performance of different SM products in Jiangsu is still unknown. To address this, this study aims to evaluate the applicability of four L-band microwave remotely sensed SM products, namely, the Soil Moisture Active Passive Single-Channel Algorithm at Vertical Polarization Level 3 (SMAP SCA-V L3, hereafter SMAP-L3), SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), and SMAP-INRAE-BORDEAUX (SMAP-IB) in Jiangsu at the seasonal scale. In addition, the effects of dynamic environmental variables such as the leaf vegetation index (LAI), mean surface soil temperature (MSST), and mean surface soil wetness (MSSM) on the performance of the above products are investigated. The results indicate that all four SM products exhibit significant seasonal differences when evaluated against in situ observations between 2016 and 2022, with most products achieving their highest correlation (R) and unbiased root-mean-square difference (ubRMSD) scores during the autumn. Conversely, their performance significantly deteriorates in the summer, with ubRMSD values exceeding 0.06 m3/m3. SMOS-IC generally achieves better R values across all seasons but has limited temporal availability, while SMAP-IB typically has the lowest ubRMSD values, even reaching 0.03 m3/m3 during morning observation in the winter. Additionally, the sensitivity of different products’ skill metrics to environmental factors varies across seasons. For ubRMSD, SMAP-L3 shows a general increase with LAI across all four seasons, while SMAP-IB exhibits a notable increase as the soil becomes wetter in the summer. Conversely, wet conditions notably reduce the R values during autumn for most products. These findings are expected to offer valuable insights for the appropriate selection of products and the enhancement of SM retrieval algorithms. Full article
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27 pages, 7418 KiB  
Article
Assessment of CCMP in Capturing High Winds with Respect to Individual Satellite Datasets
by Pingping Rong and Hui Su
Remote Sens. 2024, 16(22), 4215; https://doi.org/10.3390/rs16224215 - 12 Nov 2024
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Abstract
High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations [...] Read more.
High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%). Full article
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