L-band Microwave Research Articles

An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China

An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China

Evaluating soil moisture retrieved from a portable L-band radiometer

Soil moisture (SM) is an essential climate variable for regulating water and energy balances at land–atmosphere interactions influencing the growth of plants and otherliving organisms. L-band ground-based portable microwave radiometers (1.4 GHz) is considered a viable technology to monitor the top few centimeters of SM. For the present study, a passive ground-based portable L-band microwave radiometer was deployed to measure horizontally and vertically polarized microwave brightness temperature (TB). The TB observations were then used to retrieve the SM with validation using in-situ observed SM. The variation of TB and SM with two different soil types was also evaluated to examine the dependence of soil texture in SM retrieval. The measurement results demonstrated significant spatial variability in brightness temperature (TB) with respect to varying soil moisture (SM). Horizontally (H) polarized TB was found to be more sensitive than Vertically (V) polarized TB. On the other hand, the sensitivity of V-polarization is more significant on bare soil with grass. H-polarized SM performs better than V-polarized SM on all the soil types with less bias and RMSE. Also, validation findings reveal RMSE values smaller than 0.04 m3 m−3, target accuracy achieved by satellite mission for SM retrieval.

Gelatin-Coated High-Sensitivity Microwave Sensor for Humidity-Sensing Applications.

In this paper, the humidity-sensing characteristics of gelatin were compared with those of poly(vinyl alcohol) (PVA) at L-band (1 ~ 2 GHz) microwave frequencies. A capacitive microwave sensor based on a defected ground structure with a modified interdigital capacitor (DGS-MIDC) in a microstrip transmission line operating at 1.5 GHz without any coating was used. Gelatin is a natural polymer based on protein sourced from animal collagen, whereas PVA is a high-sensitivity hydrophilic polymer that is widely used for humidity sensors and has a good film-forming property. Two DGS-MIDC-based microwave sensors coated with type A gelatin and PVA, respectively, with a thickness of 0.02 mm were fabricated. The percent relative frequency shift (PRFS) and percent relative magnitude shift (PRMS) based on the changes in the resonant frequency and magnitude level of the transmission coefficient for the microwave sensor were used to compare the humidity-sensing characteristics. The relative humidity (RH) was varied from 50% to 80% with a step of 10% at a fixed temperature of around 25 °C using a low-reflective temperature and humidity chamber manufactured with Styrofoam. The experiment's results show that the capacitive humidity sensitivity of the gelatin-coated microwave sensor in terms of the PRFS and PRMS was higher compared to that of the PVA-coated one. In particular, the sensitivity of the gelatin-coated microwave sensor at a low RH from 50% to 60% was much greater compared to that of the PVA-coated one. In addition, the relative permittivity of the fabricated microwave sensors coated with PVA and gelatin was extracted by using the measured PRFS and the equation was derived by curve-fitting the simulated results. The change in the extracted relative permittivity for the gelatin-coated microwave sensor was larger than that of the PVA-coated one for varying the RH.

CORS station for synergistic monitoring of multivariate surface parameters in expansive soils

Expansive soils cause frequent surface deformation due to their expansion and contraction, which is a serious engineering hazard, and long-term subsidence monitoring is a prerequisite for preventing and controlling expansive soil disasters. Currently, the conventional monitoring methods for the above issue include Interferometric Synthetic Aperture Radar (InSAR) technology, but InSAR is not suitable for uninterrupted monitoring of surface deformation and has low sensitivity. Meanwhile, it can’t obtain multiple surface environmental parameters around the station. The Global Navigation Satellite System (GNSS), a system that can directly acquire surface deformation, has been widely used in landslide disaster monitoring, and in recent years, this technology has also been applied to the field of expansive soil disaster monitoring. At the same time, GNSS can also provide a constant stream of L-band microwave signals to obtain ground environmental information such as precipitable rainfall and soil moisture around the station. In previous studies of expansive soil hazards, GNSS technology has been mainly used to provide surface deformation information without exploring its potential to invert ground environmental information around stations. This paper proposes a ground-based GNSS remote sensing integrated monitoring system that integrates expanding land surface parameters such as “precipitable rainfall, soil moisture, and three-dimensional deformation” and analyses the ability of ground-based GNSS to be used for integrated monitoring of expanding soil hazards by combining ten years of consecutive observational data from GNSS stations along the coastal area of Houston. The experimental results show that the GNSS is capable of providing highly accurate time-series characterization of deformation, and inelastic subsidence in recent years has resulted in a cumulative permanent elevation loss of 2 cm along the Houston coast. The correlation coefficient between soil moisture extracted by the fifth-generation European reanalysis data (ERA5) and soil moisture inverted by ground-based GNSS is 0.514. At the same time, the GNSS was also able to monitor the zenithal precipitable water vapor (PWV) and soil moisture changes around the GNSS station and further analyze the response relationship among the three parameters, which could comprehensively evaluate the stability of expansive soils, avoiding the unreliability of relying on a single piece of monitoring information to assess the stability of expansive soils. We hope to construct a more comprehensive ground-based GNSS remote sensing monitoring system to better monitor expansive soil hazards.

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

ABSTRACT The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Development of a Dynamically Re-Configurable Radio-Frequency Interference Detection System for L-Band Microwave Radiometers.

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver's bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system.

Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review

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.

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

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.

Demonstration of a Combined Active/Passive L-Band Microwave Remote Sensing Airborne Instrument

Demonstration of a Combined Active/Passive L-Band Microwave Remote Sensing Airborne Instrument

SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms

SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms

Precision Soil Moisture Monitoring With Passive Microwave L-Band UAS Mapping

Precision Soil Moisture Monitoring With Passive Microwave L-Band UAS Mapping

Land cover and management effects on ecosystem resistance to drought stress

Abstract. Drought events are projected to become more severe and frequent across many regions in the future, but their impacts will likely differ among ecosystems depending on their ability to maintain functioning during droughts, i.e., ecosystem resistance. Plant species have diverse strategies to cope with drought. As a result, divergent responses of different vegetation types for similar levels of drought severity have been observed. It remains unclear whether such divergence can be explained by different drought duration; co-occurring compounding effects, e.g., heat stress or memory effects; management practices; etc. Here, we provide a global synthesis of vegetation resistance to drought and heat using different proxies for vegetation condition, namely the vegetation optical depth (L-VOD) data from the ESA Soil Moisture and Ocean Salinity (SMOS) passive L-band microwave mission and enhanced vegetation index (EVI) and kernel normalized difference vegetation index (kNDVI) from NASA MODIS. Due to its longer wavelength, L-VOD has the advantage over more commonly used vegetation indices (such as kNDVI, EVI) in that it provides different information on vegetation structure and biomass and suffers from less saturation over dense forests. We apply a linear model accounting for drought and temperature effects to characterize ecosystem resistance by their sensitivity to drought duration and temperature anomalies. We analyze how ecosystem resistance varies with land cover across the globe and investigate the potential effects of forest management and crop irrigation. We compare estimates of ecosystem resistance to drought and heat as retrieved from L-VOD, kNDVI, and EVI products. We find that regions with higher forest fraction show stronger ecosystem resistance to extreme droughts than cropland for all three vegetation proxies. L-VOD indicates that primary forests tend to be more resistant to drought events than secondary forests when controlling for the differences in background climate, but this cannot be detected in EVI and kNDVI. The difference is possibly related to EVI and kNDVI saturation in dense forests. In tropical primary evergreen broadleaf forests, old-growth trees tend to be more resistant to drought than young trees from L-VOD and kNDVI. Irrigation increases the drought resistance of cropland substantially. Forest harvest decreases the drought resistance of forests. Our results suggest that ecosystem resistance can be better monitored using L-VOD in dense forests and highlight the role of forest cover, forest management, and irrigation in determining ecosystem resistance to droughts.

Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice

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

Assessment of 9 km SMAP soil moisture: Evidence of narrowing the gap between satellite retrievals and model-based reanalysis

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (∼10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(±0.15) for ERA5-Land vs. 0.60(±0.17) for SMAP-IB9 and 0.50(±0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(±0.18) for SMAP-IB9 vs. 0.66(±0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(±0.02) m3/m3 for both SMAP-E and ERA5-Land) and R (0.59(±0.16) for SMAP-IB9 vs. 0.61(±0.15) for ERA5-Land), respectively.

Evaluation of L-band GPS signal attenuation to multiple vegetations using ground-based measurements

ABSTRACT Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth’s surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth’s surface. However, surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since plants, including branches and leaves, attenuate GNSS signals. Additionally, the evaluation of signal attenuation caused by the plant canopy is quite difficult. In this paper, we study the attenuations of received L1- and L2-band GPS signals to the vegetation leaf area index (LAI) for different types of plants. The relationship between the attenuation of the GPS signal-to-noise ratio (SNR) (both above and below the canopy) and the LAI is established through field experiments. The results show that the mean SNR received in the L2 band is lower than that in the L1 band for each satellite but with a larger standard deviation (SD). The sensitivities of L1- and L2-band signals to the LAI are revealed, revealing greater sensitivity and a relatively good Pearson correlation coefficient (R) for lower elevation angles and vegetation biomass. In addition, the sensitivity and R of L2-band signals to the LAI are lower than those of L1-band signals. This study is significantly valuable for improving the quantitative representation of error estimates for GNSS-R SM retrieval. The established model can be employed in GNSS land applications and aid in solving signal surface-scattering problems in which accurate signal estimates are important.

Design and validation of a dual-band circular polarization patch antenna and stripline combiner for the FSSCat mission

The FMPL-2 payload on board the 3Cat-5/A 6 Unit CubeSat, part of the FSSCat CubeSat mission, includes a dual L-Band Microwave Radiometer and a Global Navigation Satellite System Reflectometer, in one instrument, implemented in a Software Defined Radio. One of the design challenges of this payload was its Nadir looking Antenna, which had to be directive (> 12 dB), dual-band at 1400–1427 MHz and 1575.42 MHz, left-hand circularly polarized, and with important envelope restrictions, notably with a low profile. After a trade-off analysis, the best design solution appeared to be an array of six elements each of them being a stacked dual-band patch antenna, with diagonal feed to create the circular polarization, and a six to one stripline combiner. The design process of the elementary antennas first includes a theoretical analysis, to obtain the approximate dimensions. Then, by means of numerical simulations, prototyping, and adjusting the results in the simulations, the manufacturing errors and dielectric constant tolerances, to which patch antennas are very sensitive, can be characterized. A similar approach is taken with the combiner. This article includes the theoretical analysis, simulations, and prototype results, including the Flight Model assembly and characterization.

Earth observation approach for targeting stratiform deposit of manganese in central India

The stratiform manganese deposits, hosted within the Sausar Group of rocks, in Madhya Pradesh state of India, has undergone polyphase deformation and metamorphism. The region is an enterprising geosite to understand the lithological and tectonic control on manganese mineralization. We have carried out a conjugate remote sensing based analysis using Visible, Near Infrared and Short Wave Infrared bands (VIS-NIR-SWIR) of optical imagery from multiple sensors supplemented with analysis of L-band microwave data over the Balaghat district of Madhya Pradesh to delineate prospective areas for manganese exploration, supported by the lab and field-based evidence.The enhanced image products (i.e.band ratios, principal components) and spectral anomaly map derived from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2B multispectral bands are used to delineate the lithological contrast of the host rock and indicate key locales of mineralization. The geological structures interpreted using the merged product of Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR −1), Landsat 8 PC bands (2 & 3) and the processed Horizontal Gradient Magnitude (HGM) from advanced satellite gravity model (EIGEN-6C4), illustrates the structural control on the mineralization. The petrological details derived from the host rock samples shows microstructures like micro-folding, foliations, quartz grain elongation etc., thus representing the microscale deformations within the rock units occurring in the area.The lab-based spectral signatures of manganese-bearing host rock was resampled to image spectra and spectral anomaly map using ConstrainedEnergyMinimization(CEM), which shows good correlation with the existing mine locations.The information from remote sensing and lab analysis has been integrated thematically using the knowledge-based Weighted Sum Method (WSM) with existing mine locations as the training dataset. The derived mineral prospect map indicates several new areas of manganese prospectivity which can serve as a guide for detailed future explorations in the region .

Beyond river discharge gauging: hydrologic predictions using remote sensing alone

This study suggests a radical approach to hydrologic predictions in ungauged basins, addressing the long standing challenge of issuing predictions when in-situ river discharge does not exist. A simple but powerful rationale for measuring and modeling river discharge is proposed, using coupled advances in hydrologic modeling and satellite remote sensing. Our approach presents a Surrogate River discharge driven Model (SRM) that infers Surrogate River discharge (SR) from remotely sensed microwave signals with the ability to mimic river discharge in varying topographies and vegetation cover, which is then used to calibrate a hydrological model enabling physical realism in the resulting river discharge profile by adding an estimated mean of river discharge via the Budyko framework. The strength of SRM comes from the fact that it only uses remotely sensed data in prediction. The approach is demonstrated for 130 catchments in the Murray Darling Basin (MDB) in Australia, a region of high economic and environmental importance. The newly proposed SR (SRL, representing L-band microwave) boosts the Nash-Sutcliffe Efficiency (NSE) of modeled flow, showing a mean NSE of 0.54, with 70% of catchments exceeding NSE 0.4. We conclude that SRM effectively predicts high-flow and low-flow events related to flood and drought. Overall, this new approach will significantly improve catchment simulation capacity, enhancing water security and flood forecasting capability not only in the MDB but also worldwide.

Soil and vegetation water content identify the main terrestrial ecosystem changes.

Environmental change is a consequence of many interrelated factors. How vegetation responds to natural and human activity still needs to be well established, quantified and understood. Recent satellite missions providing hydrologic and ecological indicators enable better monitoring of Earth system changes, yet there is no automatic way to address this issue directly from observations. Here, we develop an observation-based methodology to capture evidence of changes in global terrestrial ecosystems and attribute these changes to natural or anthropogenic activity. We use the longest time record of global microwave L-band soil moisture and vegetation optical depth as satellite data and build spatially explicit maps of change in soil and vegetation water content and biomass reflecting large ecosystem changes during the last decade, 2010-20. Regions of prominent trends (from [Formula: see text] to 9% per year) are observed, especially in humid and semi-arid climates. We further combine such trends with land cover change maps, vegetation greenness and precipitation variability to assess their relationship with major documented ecosystem changes. Several regions emerge from our results. They cluster changes according to human activity drivers, including deforestation (Amazon, Central Africa) and wildfires (East Australia), artificial reforestation (South-East China), abandonment of farm fields (Central Russia) and climate shifts related to changes in precipitation variability (East Africa, North America and Central Argentina). Using the high sensitivity of soil and vegetation water content to ecosystem changes, microwave satellite observations enable us to quantify and attribute global vegetation responses to climate or anthropogenic activities as a direct measure of environmental changes and the mechanisms driving them.

Impact of Rain Effects on L-Band Passive Microwave Satellite Observations Over the Ocean

L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good accuracy for rain effect correction.