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Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [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 measurement of global precipitation is important for quantifying and understanding the Earth's systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a comprehensive suite of observing systems, including many sensors that are capable of precipitation retrievals. While much progress has been made in developing and implementing precipitation retrieval schemes, many techniques have concentrated upon retrievals over regions with well-defined precipitation systems, such as the tropics. At higher latitudes, such retrieval schemes are less successful in providing accurate and consistent precipitation estimates, especially due to the large diversity of precipitation regimes. Furthermore, the increasing dominance of snowfall at higher latitudes imposes a number of challenges that require further, urgent work. This paper reviews the state of the current observations and retrieval schemes, highlighting the key factors that need to be addressed to improve the estimation and measurement of precipitation at mid-to-high latitudes. [ABSTRACT FROM AUTHOR]

Over arid areas, observations of brightness temperatures by passive microwave radiometers are affected by the variation of the emitting depth with wavelengths. When this variation is unaccounted for, it limits the assimilation of passive microwaves over deserts in Numerical Weather Prediction models and it causes large errors in passive microwave retrievals of land surface temperatures. The emitting depths, along with the corresponding emissivities, are estimated from 10 to 89 GHz, using the non‐Sun‐synchronous observations of the Global Precipitation Mission Microwave Imager to reconstruct the monthly diurnal cycle of brightness temperature. The soil temperature profile is modeled using a two‐term Fourier decomposition based on the ERA5 surface temperature. The combination of the observation and the modeled temperature allows for an estimation of the microwave effective emitting depth. The emitting depth is estimated to be up to 25 cm at 36 GHz, resulting in large differences between the surface temperature and the effective emitting temperature. The variation of emitting depth with frequency is parameterized, and a companion data set provides the necessary parameters to calculate the emitting depth for arid areas between 10 and 89 GHz, globally. The benefit of this parameterization is quantified, with an application to the modeling of observations from the Special Sensor Microwave Imager Sounder over arid areas. Plain Language Summary: Discrepancies have been observed between passive microwave radiometer retrievals of Land Surface Temperature (LST) and the Infrared retrievals of LST in arid areas. This difference has been explained by the combination of two phenomena: a deeper emitting depth for microwave radiation and the variation of temperature with depth and time in the soil. This paper describes the step to obtain a map that allows quantification of this phenomenon for all global arid areas. The map is derived from a combination of observations by the Global Precipitation Measurement (GPM) Microwave Imager sensor and modeling of the microwave signal based on reanalysis data. This will enable the scientific community to take this effect into account in all retrievals relying on passive microwave sensors in arid areas. Key Points: Emissivity and soil temperature affect the passive microwave brightness temperaturesThe effective soil temperature depends on the emitting depth, driven by the dielectric propertiesA map of dielectric permittivity is created to help improve retrievals based on passive microwave observations [ABSTRACT FROM AUTHOR]

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory's microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the "truth", this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR's precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development. [ABSTRACT FROM AUTHOR]

This paper describes the initial results of precipitation estimates from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter-wave Sounder (TMS) using the Precipitation Retrieval and Profiling Scheme (PRPS). The TROPICS mission consists of a Pathfinder CubeSat and a constellation of six CubeSats, providing a low-cost solution to the frequent sampling of precipitation systems across the Tropics. The TMS instrument is a 12-channel cross-track scanning radiometer operating at frequencies of 91.655 to 204.8 GHz, providing similar resolutions to current passive microwave sounding instruments. These retrievals showcase the potential of the TMS instrument for precipitation retrievals. The PRPS has been modified for use with the TMS using a database based upon observations from current sounding sensors. The results shown here represent the initial postlaunch version of the retrieval scheme, as analyzed for the Pathfinder CubeSat launched on 30 June 2021. In terms of monthly precipitation estimates, the results fall within the mission specifications and are similar in performance to retrievals from other sounding instruments. At the instantaneous scale, the results are very promising. [ABSTRACT FROM AUTHOR]

Both the European Space Agency's soil moisture and ocean salinity (SMOS) mission and the National Aeronautics and Space Administration's soil moisture active passive (SMAP) mission employ L-band (1.413 GHz) radiometers to observe brightness temperatures at ∼40-km spatial resolution to subsequently derive global soil moisture every two to three days with a target accuracy of 0.04 m3/m3. However, the man-made structures that dominate urban areas in many of the SMOS and SMAP radiometers pixels may confound the interpretation of their radiometric observations if not taken into account, and thus, degrade the soil moisture retrieval accuracy. This paper investigates the effect that urban areas are expected to have on the SMOS and SMAP soil moisture retrieval accuracy using experimental data from the Australian airborne field campaigns performed over the past six years. Taking the total radiometric error budgets for the SMOS (3.95 K) and the SMAP (1.3 K) missions as conservative benchmarks for radiometric “error” that can be tolerated to achieve the 0.04 m3/m3 target accuracy, urban fraction thresholds of 6.6% and 2.2% were obtained for the SMOS and SMAP pixels, respectively, under warm dry (soil moisture < 0.15 m3/m3) conditions, increasing to 16.8% and 5.2% under cold and/or wet conditions. These results have been extrapolated globally, assuming that the microwave behavior of the cities analyzed here is representative of those elsewhere, to identify the SMOS and SMAP pixels that are expected to be adversely affected by urban areas if not explicitly taken into account in retrieval algorithms. [ABSTRACT FROM AUTHOR]

Salinity is a key parameter that affects the surface, deep circulations, and heat transport of oceans. Sea surface salinity (SSS) represents the salinity at the ocean surface and impacts atmosphere – ocean interactions and vertical ocean circulation. To monitor SSS, three passive microwave radiometers with an L-band (1.4 GHz) have been launched since 2009. The scientific need for SSS retrieval and estimation has grown in recent years; however, the operational retrieval of SSS via satellite remote sensing still faces significant challenges. This study provides a review of satellite-based SSS retrieval methods and guidelines to encourage future research. This paper introduces satellite-derived SSS research trends and summarizes the representative SSS satellite sensors and their retrieval methods. The limitations and challenges of satellite-derived SSS are then discussed. The errors from the retrieval algorithms, discrepancies in the spatio-temporal scales of in situ and remote sensing, and limitations of the satellite-derived SSS are then detailed. Finally, our paper provides suggestions for the future directions of SSS remote sensing in five ways: mitigation of measurement errors, improvement of currently available SSS products, enhancement of the usage of in situ data, reconstruction of three-dimensional salinity information, and synergetic uses of multi-satellite missions.

The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The retrieval algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS retrieval algorithm converges to realistic soil moisture retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other. [ABSTRACT FROM AUTHOR]

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m3· m-3, respectively, which generally meet their mission requirement of 0.04 m3· m-3. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m3· m-3. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m3 · m-3 though it underestimates in situ measurements with a bias of 0.059 m3 · m-3, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles. [ABSTRACT FROM AUTHOR]

In this paper, sea-ice thickness values are calculated along the Labrador coast using data from two sensors representative of those available for operational data assimilation. Data from the first sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), are used to calculate the ice thickness using a heat balance equation. Relationships between the MODIS ice thickness and polarization ratio from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calculate the thickness of thin ice (less than 0.2 m) from the AMSR-E data. This is done for each frequency on the AMSR-E sensor in the range of 6.9-36.5 GHz. Through comparison with data from ice charts, it is found that the errors are lowest for thickness values calculated from low-frequency AMSR-E data. The accuracies of the ice thickness from MODIS, AMSR-E, operational ice charts, and two moored upward looking sonars are further assessed using the triple collocation method. It is found that the error associated with ice thickness from AMSR-E is the lowest and the error associated with ice thickness from MODIS is the highest. While the MODIS data represent the small-scale variability of the sea-ice thickness better than the AMSR-E data, the MODIS data can produce spurious values of ice thickness due to unmasked clouds. To use ice thickness from MODIS in an automated algorithm, quality control would need to be applied to the MODIS data to remove unmasked clouds which lead to spurious values of thick ice. The errors calculated for the ice thickness from AMSR-E, which are calculated based on a relationship calibrated with MODIS ice thickness from a clear-sky day, indicate that these data would be useful for operational data assimilation. [ABSTRACT FROM AUTHOR]

As an important component of the Earth system, precipitation plays a vital role in regional and global water cycles. Based on Microwave Humidity and Temperature Sounder (MWHTS) onboard FY-3D satellite, four machine learning models, random forest regression (RFR), support vector machine (SVM), multilayer perceptron (MLP), and gradient boosting regression tree (GBRT), are implemented to retrieve precipitation rate, and verified with Integrated Multi-satellite Retrievals for GPM (IMERG). This paper determines the optimal hyperparameters of the machine models and proposes three linear combinations of MWHTS channels (183.31 ± 1.0–183.31 ± 3.0 GHz, 183.31 ± 1.0–183.31 ± 7.0 GHz, and 183.31 ± 3.0–183.31 ± 7.0 GHz), which can better characterize precipitation of different intensities. With the inclusion of three linear combinations, the performances of all four machine learning models are significantly improved. It is concluded that the RFR and GBRT have the best retrieval accuracy. Over ocean, the MSE, MAE, and R 2 values of precipitation estimates using RFR are 1.75 mm/h, 0.44 mm/h, and 0.80, respectively, and are 1.80 mm/h, 0.45 mm/h, and 0.78 for GBRT. Simultaneously, this paper analyzes the retrieval results from the perspective of the different rain rates and temporal matching difference between MWHTS and IMERG data. The RFR and GBRT also maintain the best retrieval accuracy under the condition of Gaussian noise, indicating the relatively strong robustness and antinoise performance of ensemble learning models for precipitation retrieval. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE) is critical information for hydrological studies over northern latitudes. This information is needed to adequately simulate spring runoff in order to optimize hydropower generation. In this paper, a new geophysical inversion methodology for monitoring SWE evolution on a daily basis is presented. The methodology is based on a modified version of the Helsinki University of Technology (HUT) snow emission model and assimilates in situ data as ground truth. Previous studies used the mean snow grain size (ø) as an optimization parameter to retrieve SWE with the HUT model. Therefore, a priori information on ø was needed to estimate SWE, but this information is rarely measured in the field. In the HUT model, the mean snow grain size is used to compute the total extinction coefficient (κe) of the snowpack. We modified the model to eliminate the required a priori knowledge on mean snow grain size and used the total extinction coefficient as an optimization parameter instead. The methodology was applied in a subarctic territory in northern Québec (Canada) using snow measurements acquired during the 2001 to 2006 winters (January 1 to March 31). A metric based on the brightness temperature difference between the special sensor microwave/imager 19- and 37-GHz (vertical polarization) was used for SWE retrieval. In the monitoring process, the result obtained from the previous day inversion was used to initialize the current day SWE retrieval. When in situ measurements are available, they are assimilated and used to calibrate the model. The results obtained with this methodology show an improvement in the experimental context considered (high SWE values). For all winters considered in this paper, the results described in this paper show that SWE was estimated with a root-mean-square error (rmse) of less than 19% after the first process of in situ data assimilation and with an rmse of 14 % for nondense forested areas and for SWE values below 300 mm. [ABSTRACT FROM AUTHOR]

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over- or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper were assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7, 18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement mission planned jointly by the United States, Japanese, and European space agencies envisions providing global rainfall products from a constellation of passive microwave (PM) satellite sensors at time scales ranging from 3-6 h. In this paper, a sensitivity analysis was carried out to understand the implication of satellite PM rainfall retrieval and sampling errors on flood prediction uncertainty for medium-sized (∼ 100 km[SUUP2]) watersheds. The 3-h rainfall sampling gave comparable flood prediction uncertainties with respect to the hourly sampling, typically used in runoff modeling, for a major flood event in Northern Italy. The runoff prediction error, though, was magnified up to a factor of 3 when rainfall estimates were derived from 6-h PM sampling intervals. The systematic and random error components in PM retrieval are shown to interact with PM sampling introducing added uncertainty in runoff simulation. The temporal correlation in the PM retrieval error was found to have a negligible effect in runoff prediction. It is shown that merging rain retrievals from hourly infrared (IR) and PM observations generally reduces flood prediction uncertainty. The error reduction varied between 50% (0%) and 80% (50%) for the 6-h (3-h) PM sampling scenarios, depending on the relative magnitudes of PM and IR retrieval errors. Findings from this paper are potentially useful for the design, planning, and application assessment of satellite remote sensing in flood and flash flood forecasting. [ABSTRACT FROM AUTHOR]

Passive microwave sensors have better capability of penetrating forest layers to obtain more information from forest canopy and ground surface. For forest management, it is useful to study passive microwave signals from forests. Passive microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests. The observed brightness temperature of a mixed forest can be approximated by a linear combination of the needleleaf and broadleaf brightness temperatures weighted by their respective abundances. For a mixed forest observed by an N-band microwave radiometer with horizontal and vertical polarizations, there are 2 N observed brightness temperatures. It is desirable to infer 4 N $+$ 2 unknowns: 2 N broadleaf brightness temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf abundance, and 1 needleleaf abundance. This is a challenging underdetermined problem. In this paper, we devise a novel method that combines microwave unmixing with video segmentation for inferring broadleaf and needleleaf brightness temperatures and abundances from mixed forests. We propose an improved Otsu method for video segmentation to infer broadleaf and needleleaf abundances. The brightness temperatures of needleleaf and broadleaf trees can then be solved by the nonnegative least squares solution. For our mixed forest unmixing problem, it turns out that the ordinary least squares solution yields the desired positive brightness temperatures. The experimental results demonstrate that the proposed method is able to unmix broadleaf and needleleaf brightness temperatures and abundances well. The absolute differences between the reconstructed and observed brightness temperatures of the mixed forest are well within 1 K. [ABSTRACT FROM AUTHOR]

Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly‐known microstructural properties that vary strongly in time and space. A consequence is that surface‐sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an "empirical state" method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical‐empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This "empirical state" approach could be used to solve many other problems of earth system data assimilation. Plain Language Summary: One way to learn about the earth system would be through machine learning, but typical "supervised learning" already requires good knowledge of the geophysical variables of interest. Quite often this "geophysical state" is not well known and the main observations are from satellites, which measure earth‐emitted radiation. In many cases the links between this observed radiation and the geophysical state are poorly known. This work illustrates a new method which allows both the geophysical state and its links to the satellite observations to be learned at the same time. The specific application is finding the sea ice concentration, which is a difficult problem because there is poor knowledge of the properties of sea ice, such as the air bubbles within it, which can strongly affect the radiation measured by satellites, and poor knowledge of exactly how this affects the satellite observations. The new approach solves this double problem by making daily maps of the sea ice and its properties, along with learning a new model to simulate the satellite measured radiation from the sea ice properties. Similar approaches could improve knowledge of both models and geophysical state in other areas of earth system science. Key Points: A new hybrid of machine learning and data assimilation can infer an unknown state and an unknown model simultaneouslyThis empirical state method could help other chicken and egg earth system problems that are not suitable for supervised machine learningThis work finds a new sea ice surface emissivity model and makes global all‐season maps of sea ice concentration using inverse methods [ABSTRACT FROM AUTHOR]

Ice water path (IWP) is an important parameter to characterize tropical cyclones. The FY-3B satellite, with multiple passive microwave sensors onboard, offers a unique opportunity to monitor the variation of cloud IWP during the evolution of tropical cyclones. In this paper, by using the combined simultaneous measurements of the MicroWave atmospheric Humidity Sounder and MicroWave Radiometer Imager on FY-3B satellite, an improved IWP algorithm for tropical clouds is developed. The new algorithm seeks to better estimate ice-free background brightness temperature at 150 GHz using simultaneous observations at low microwave frequencies. This approach improves IWP retrieval accuracy particularly for high IWP clouds that often associated with tropical cyclones. The current algorithm was applied to FY-3B observations of two typhoons with different strengths, and the characteristics of IWP variation at the storms' different evolution stages were investigated. The results showed that IWP tends to vary ahead of the storm intensification or decay, suggesting that IWP can be potentially used to predict the change of storm intensity. [ABSTRACT FROM PUBLISHER]

Abstract: This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use. [ABSTRACT FROM AUTHOR]

Passive microwave data of a snow-covered land surface received by a passive microwave radiometer is composed of microwave radiation from the snow cover and the underlying surface. The western Jilin Province of China is an important area with seasonal snow. Here, the land cover types mainly include characteristically saline-alkaline farmland and grassland. The main aim of this paper is to obtain more accurate snow depth (SD) data in the western Jilin Province of China. First, a multifrequency passive microwave unmixing method is proposed for the study area. Second, based on Fengyun-3B microwave radiation imagery (MWRI) data and advanced microwave scanning radiometer 2 (AMSR2) data, the SD retrieval results are evaluated using four methods: using MWRI data with Foster's algorithm and with the standard MWRI algorithm, and using the AMSR2 data with Foster's algorithm and with the standard AMSR2 algorithm. The experimental results demonstrate that using MWRI data with the standard MWRI algorithm yields the highest accuracy for SD retrieval, with average bias and root-mean-square-error (RMSE) values of approximately 6.9 and 7.5 cm, respectively. Finally, we combined the unmixing method with the optimal SD retrieval method, and obtained more accurate SD data for the study area. Using MWRI data with the standard MWRI algorithm, the average bias and RMSE improved by approximately 9.7% and 7% when using the unmixed pixels compared with using the original mixed pixels. [ABSTRACT FROM AUTHOR]

Passive microwave sensors have the capability to provide information on snow depth (SD), which is critically important for hydrological modeling and water resource management. However, the different algorithms used to produce SD products lead to discrepancies in the data. To determine which products might be most suitable for Northern China, this paper assesses the accuracy of the existing snow depth products in the period of 2002-2011. By comparing three daily snow depth products, including NSIDC, WESTDC and ESA Globsnow, with snow cover product and meteorological stations data, the accuracies of the different SD products are analyzed for different snow class and forest cover fraction. The results show that comparison between snow cover derived from snow depth of NSIDC, ESA GlobSnow and WESTDC with snow cover product shows that accuracy of WESTDC and ESA GlobSnow in snow cover detecting can reach 0.70. Compared to meteorological stations data below 20cm, NSIDC consistently overestimate, WESTDC and ESA Globsnow underestimate, furthermore the product from WESTDC is superior to the others. The three products have the same tendency of significant undervaluation over 20cm. The WESTDC is superior to the ESA Globsnow and NSIDC in non-forest regions, whereas the ESA GlobSnow estimate is superior to the WESTDC and NSIDC in forest regions. As for the prairie and alpine snow, WESTDC has smaller bias and RMSE, meanwhile Globsnow has advantages in the snow depth retrieval in tundra and taiga snow. Therefore, we should choose the more suitable snow depth products according to different needs. [ABSTRACT FROM AUTHOR]

The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau–omega model from 1) the soil moisture active passive (SMAP) algorithm theoretical basis document (ATBD) for global condition and 2) calibrated parameters from the National Airborne Field Experiment (NAFE’05) for Australian conditions, with special focus on the vegetation parameter b and roughness parameter HR. This study uses airborne L-band data and field observations from the SMAP experiments conducted in south-eastern Australia. Results show that the accuracy with the proposed parameterizations from SMAP ATBD was satisfactory at 100-m spatial resolution for maize (0.07 m3/m3) and pasture (0.07 m3/m3 ), while it decreased to 0.19 m3/m3 for wheat. Calibrated parameters from the NAFE’05 did not provide better results, with the accuracy of wheat degrading to 0.23 m3/m3. After a comprehensive site-specific calibration and validation at 100-m spatial resolution, this result was improved to 0.10 m3/m3. Further calibration and validation were performed at 1-km resolution against intensive ground sampling and at 3-km against in situ monitoring stations. Results showed an accuracy over grassland and cropland of 0.04 m3/m3 and 0.05 m3/m3 , respectively. This study also suggests that the parameters from SMAP ATBD show an underestimation of soil moisture, with the roughness parameter HR being too low for south-eastern Australian condition. Therefore, a new set of b and HR parameters for ten different land cover types was proposed in this study. [ABSTRACT FROM PUBLISHER]

Models and observations show that the Arctic is experiencing the most rapid changes in global near-surface air temperature. We developed novel EASE-grid Level 3 (L3) land surface temperature (LST) products from Level 2 (L2) AATSR and MODIS data to provide weekly, monthly and annual LST means over the pan-Arctic region at various grid resolutions (1-25 km) for the past decade (2000-2010). In this paper, we provide: (1) a review of previous validation of MODIS/AATSR L2; (2) a description of the processing chain of L3 products; (3) an assessment of the 25 km products uncertainty, and; (4) a quantification of the bias introduced by over-representing clear-sky days in MODIS L3 products. In addition, we generated uncertainty maps by comparing L3 products with LST from passive microwave sensors (AMSR-E and SSM/I) and the North American Regional Reanalysis (NARR). Results show a close correspondence between MODIS and AATSR monthly products with a mean-difference (MD) of -1.1 K. Comparing L3 products with NARR indicates a close agreement in summer and a systematic bias in winter, which is entirely negative with respect to MODIS L3 (MD: -3.6, Min: -6.8, Max: -1 K). Comparing monthly averaged MODIS L3 to NARR clear-sky to quantify over-representing clear-sky days indicates a decrease of winter and an increase of summer difference compared to NARR all-sky. Finally, we provide suggestions to improve LST retrieval over Arctic regions. [ABSTRACT FROM AUTHOR]

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter. [ABSTRACT FROM AUTHOR]

Simulated sea ice was grown in an outdoor tank during the early winter seasons of 2001/2002 and 2005/2006. Microwave radiation was sampled every 5 min from the following three channels: 19, 37, and 85 GHz. Surface physical conditions were measured or observed to help in the interpretation of the radiometric behavior. This paper reports on results related to the following objectives: 1) linking the observed radiation to surface properties and processes; 2) classifying thin ice into emissivity-based surface types, and 3) assessing thin-ice parameter retrieval algorithms. This paper shows that ice of less than 4-cm thickness exhibits cycles of a sharp decrease of microwave emission caused by surface wetness followed by a gradual increase as the surface refreezes. This ice is particularly linked to meteorological conditions. Snow accumulation on relatively thick ice (> 20 cm) affects only the radiation from the 85-GHz channel. Thin-ice surfaces can be grouped into two radiometrically distinguished categories - the first includes slushy and wet surfaces and the second includes wet snow, dry snow, and dry bare-ice surfaces. Radiation from the second category is higher. The radiation from a refrozen slush surface appears to fall between these two categories. The variability of emissivity increases as the radiation frequency increases, particularly for the horizontal polarization channels. Existing algorithms of ice thickness, snow depth, and ice concentration were examined against the current data to study their sensitivity to variations of surface conditions. Limitations on their applications have been established. [ABSTRACT FROM PUBLISHER]

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-rn resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-rn "coarse-scale" passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): 1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and 2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave soil moisture. [ABSTRACT FROM AUTHOR]

Snow plays a crucial role in the global water cycle, providing water to over 20% of the world's population and serving as a vital component for flora, fauna, and climate regulation. Changes in snow patterns due to global warming have far-reaching impacts on water management, agriculture, and other economic sectors such as winter tourism. Additionally, they have implications for environmental stability, prompting migration and cultural shifts in snow-dependent communities. Accurate information on snow and its variables is, thus, essential for both scientific understanding and societal planning. This review explores the potential of remote sensing in monitoring snow water equivalent (SWE) on a large scale, analyzing 164 selected publications from 2000 to 2023. Categorized by methodology and content, the analysis reveals a growing interest in the topic, with a concentration of research in North America and China. Methodologically, there is a shift from passive microwave (PMW) inversion algorithms to artificial intelligence (AI), particularly the Random Forest (RF) and neural network (NN) approaches. A majority of studies integrate PMW data with auxiliary information, focusing thematically on remote sensing and snow research, with limited incorporation into broader environmental contexts. Long-term studies (>30 years) suggest a general decrease in SWE in the Northern Hemisphere, though regional and seasonal variations exist. Finally, the review suggests potential future SWE research directions such as addressing PMW data issues, downsampling for detailed analyses, conducting interdisciplinary studies, and incorporating forecasting to enable more widespread applications. [ABSTRACT FROM AUTHOR]

Passive microwave (PM) observations have been used to monitor ice retreat in the Arctic. However, various PM sea ice concentration (SIC) algorithms are prone to underestimate ice fraction during summer. We evaluated the accuracy of 2002–2019 low SICs in the Central Arctic Ocean of four PM products from the University of Bremen, the National Snow and Ice Data Center (NSIDC), and the Ocean and Sea Ice Satellite Application Facility (OSI SAF), and two reanalysis data sets from the fifth generation of European ReAnalysis (ERA5) and the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). Three reference fields were used: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) true‐color composites, (b) MODIS sea ice extent, and (c) multi‐product ensemble (MPE‐SIC) comprising the median of collocated SIC estimates. Our results indicate SICs derived from the Advanced Microwave Scanning Radiometer ‐ Earth Observing System (AMSR‐E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) high frequency channels have the best accuracy. Reanalysis SICs indicate almost identical patterns as their remote sensing inputs. The assessment shows that the Bremen (+1.06%) and NSIDC (+0.99%) SICs are higher than the median field, while the OSI‐401 (−6.65%) and OSI‐408 (−4.64%) have negative mean deviations. The mean error of MODIS‐derived SIC (−0.80%) is smaller than PM SICs. These small mean values belie wide distributions of values. The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96. Key Points: We compared Arctic sea ice concentration (SIC) using passive microwave (PM), optical, and reanalysis data sets for the summers of 2002–2019The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96The average differences between multi‐product ensemble (MPE‐SIC) and PM SIC range from −6.65% to +1.09% [ABSTRACT FROM AUTHOR]

Information on Arctic sea-ice motion in summer is critical for operational sea-ice monitoring and prediction and commercial navigation. Affected by the melting of the sea-ice surface, the accuracy and spatial coverage of the sea-ice velocity (SIV) retrieved by brightness temperature (TB) during the melting period are always lower than in winter. Our research indicates that the lack of ice--water discrimination in summer for the Advanced Microwave Scanning Radiometer 2 (AMSR2) 36 GHz TB is the main reason for the poor quality of its SIV results. We propose a new SIV retrieval scheme based on the polarization difference (P) of the 36 GHz TB. The 36-P SIV has a higher spatial coverage than that derived from 18-H, 36-H and 36-V SIVs and a higher maximum cross-correlation coefficient than 36 GH TB-derived SIVs. Compared with buoy and Synthetic Aperture Radar SIVs, the 36-P SIV's error is close to that of 18-H SIV and significantly lower than that of 36 TB SIV. Therefore, our results indicate that 36-P is more competitive than the 36 GHz TB in SIV retrieval in summer. Further analysis shows that our proposed scheme is compatible with the two current mainstream SIV tracking algorithms, demonstrating its great potential for future summer SIV retrieval. [ABSTRACT FROM AUTHOR]

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating the canopy, plays a crucial role in global-scale biomass monitoring and climate change research. However, the spatial gridding of existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at a regional scale. High-resolution active-microwave proxies and optical vegetation indices can potentially be used to disaggregate coarse-resolution VOD, but it is unclear which proxy is optimal. In this paper, the Normalized Difference Vegetation Index (NDVI) and active-microwave proxies (VH, VV, and cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed with VOD products at various frequencies (L-/C-/X-VOD) across the contiguous United States (U.S.). The results showed that VH (R = 0.80) and NDVI (R = 0.77) exhibit a high spatial correlation with L-VOD products. For temporal correlation, NDVI had the highest overall performances with all VOD products, but good correlations were also achieved with CR and, to a lesser extent, VH. Further comparisons of the performance between Brightness Temperature (TB) and VOD revealed that while TB displayed a strong temporal correlation with active-microwave proxies, its spatial correlations with such proxies were low. In contrast, VOD had good correlations both temporally and spatially with active-microwave proxies (e.g., VH). These evidences suggested that the downscaling of VOD using the combination of VH and other proxies could be an alternative promising method to estimate high-resolution VOD.

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).

As an important component of the Earth system, precipitation plays a vital role in regional and global water cycles. Based on Microwave Humidity and Temperature Sounder (MWHTS) onboard FY-3D satellite, four machine learning models, random forest regression (RFR), support vector machine (SVM), multilayer perceptron (MLP), and gradient boosting regression tree (GBRT), are implemented to retrieve precipitation rate, and verified with Integrated Multi-satellite Retrievals for GPM (IMERG). This paper determines the optimal hyperparameters of the machine models and proposes three linear combinations of MWHTS channels (183.31 ± 1.0–183.31 ± 3.0 GHz, 183.31 ± 1.0–183.31 ± 7.0 GHz, and 183.31 ± 3.0–183.31 ± 7.0 GHz), which can better characterize precipitation of different intensities. With the inclusion of three linear combinations, the performances of all four machine learning models are significantly improved. It is concluded that the RFR and GBRT have the best retrieval accuracy. Over ocean, the MSE, MAE, and R2 values of precipitation estimates using RFR are 1.75 mm/h, 0.44 mm/h, and 0.80, respectively, and are 1.80 mm/h, 0.45 mm/h, and 0.78 for GBRT. Simultaneously, this paper analyzes the retrieval results from the perspective of the different rain rates and temporal matching difference between MWHTS and IMERG data. The RFR and GBRT also maintain the best retrieval accuracy under the condition of Gaussian noise, indicating the relatively strong robustness and antinoise performance of ensemble learning models for precipitation retrieval.

The landscape freeze/thaw (F/T) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and soil moisture and ocean salinity (SMOS) L-band passive microwave measurements using a polarization ratio (PR)-based algorithm for landscape F/T monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: 1) tundra; 2) boreal forest; and 3) prairies. The reference data include in situ measurements of soil temperature (\mathbf T\mathbf{soil}) and air temperature (\mathbf T\mathbf{air}), and moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between L-band F/T detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using \mathbfT\mathbf{air} as the reference. Aquarius radiometer 2 (incidence angle of 39.6°) data give the best accuracies (90.8%), while for SMOS, using the Aquarius temporal characteristics, the best results (87.8% of accuracy) are obtained at higher incidence angles (\mathbf55^\circ\!\!-\!\!\mathbf60^\circ ). The F/T algorithm identifies both freeze onset and end with a delay of about 1 week in tundra and 2 weeks in forest and prairies, when compared to \mathbfT\mathbf{air}. The analysis shows a stronger F/T signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the PR) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar F/T patterns over Canada. This study shows the potential of using L-band spaceborne observations for F/T monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth. [ABSTRACT FROM PUBLISHER]

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity mission has been launched by the European Space Agency (ESA) in November 2009. It is the worldwide first satellite dedicated to retrieve soil moisture information at the global scale, with a high temporal resolution, and from spaceborne L-band radiometry. This novel technique requires careful calibration, validation, and an in-depth understanding of the acquired data and the underlying processes. In this light, a measurement campaign was undertaken recently in the river catchment of the upper Danube in southern Germany. In May and June 2010, airborne thermal infrared and L-band passive microwave data were collected together with spatially distributed in situ measurements. Two airborne radiometers, EMIRAD and HUT-2D, were used during the campaigns providing two complementary sets of measurements at incidence angles from 0° to 40° and with ground resolutions from roughly 400 m to 2 km. The contemporaneous distributed ground measurements include surface soil moisture, a detailed land cover map, vegetation height, phenology, and biomass. Furthermore, several ground stations provide continuous measurements of soil moisture and soil temperature as well as of meteorological parameters such as air temperature and humidity, precipitation, wind speed, and radiation. All data have undergone thorough postprocessing and quality checking. Their values and trends fit well among each other and with the theoretically expected behavior. The aim of this paper is to present these data which may contribute to potential further studies of soil moisture, brightness temperature, and their spatial variability. The presented data are available to the scientific community upon request to ESA. [ABSTRACT FROM AUTHOR]

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve. [ABSTRACT FROM AUTHOR]

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 \m^3/\m^3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation. [ABSTRACT FROM AUTHOR]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-rn and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb. edu.au. [ABSTRACT FROM AUTHOR]

Soil moisture retrieval models from the Soil Moisture and Ocean Salinity (SMOS) mission, which is an L-band microwave interferometer, are based on multiangular measurements and make use of the emissivity angular signature. Mountainous areas modify local incidence angles, implying significant impacts on brightness temperatures and, consequently, on soil moisture retrievals. The purpose of this paper is to establish a criterion in quantifying the relevance of topographic impacts at the SMOS scale (∼40 km). The goal is thus to define a method of flagging the pixels according to the relative impact of topography on the brightness temperature. The proposed method uses the variogram of digital elevation model images. As a result, a map of the pixels to be flagged is produced to ensure that no soil moisture retrievals are carried out on pixels that are affected by strong topographic effects. As validation, a model was also used to simulate differences between brightness temperature variations between mountainous areas and flat surfaces. [ABSTRACT FROM AUTHOR]

The mapping of ice type concentrations in the Arctic is important for commercial operations and for climate-related research. Algorithms based on moderate-resolution passive microwave sensors for mapping first-year ice and multiyear ice concentrations suffer from a number of known problems. In this paper, it has been shown that QuikSCAT scatterometer data can add complimentary information to that available from passive microwave, which can assist in separating different ice classes. Specifically, we identify a class of ice that exhibits a passive microwave signature which is characteristic of first-year ice, but has a scatterometer signature which is typical of multiyear ice. We track the evolution and distribution of this new ice class throughout the Arctic during the winter season of 2003-2004 and compare the results against the U.S. National Ice Center (NIC) ice charts. It has been found that the new ice class is predominantly multiyear ice and is especially prevalent in the Fram Strait and the high Arctic regions north of the islands Franz Josef Land and Severnaya Zemlya. A simple algorithm has been proposed that enables a passive microwave-based partial ice concentration algorithm (for example, the NT algorithm based on Special Sensor Microwave/Imager data) to be adapted using QuikSCAT scatterometer data, so that the new ice class is corrected from the first-year ice class to the multiyear ice category. The algorithm performance is measured against the NIC ice charts. We provide a discussion regarding the possible physical causes of the effects that have been observed and described. [ABSTRACT FROM AUTHOR]

A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave- Scanning Radiometer—Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Qp, model, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Qp, at different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface emissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronomiques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the avail- able experimental data at the incidence angles of 500 and 60°. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50° and 550W [ABSTRACT FROM AUTHOR]

This paper presents the algorithms and analysis results for delineating snow zones using active and passive microwave satellite remote sensing data. With a high-resolution Radarsat synthetic aperture radar (SAR) image mosaic, dry snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A competing region growing and merging algorithm is used to initially segment the SAR images into a series of homogeneous regions. Based on the backscatter characteristics and texture property, these image regions are classified into different snow zones. The higher level of knowledge about the areal size of and adjacency relationship between snow zones is incorporated into the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathematical morphology operations and a line-tracing algorithm are designed to extract a vector line representation of snow-zone boundaries. With the daily passive microwave Special Sensor Microwave/Imager (SSM/I) data, dry and melt snow zones were derived using a multiscale wavelet-transform-based method. The analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary nature and comparative advantages of frequently repeated passive microwave data and spatially detailed radar imagery for detecting and characterizing snow zones were demonstrated. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of heterogeneity at the land surface on geophysical parameters retrieved from multiangle microwave brightness temperature data, such as would be obtained from the Soil Moisture and Ocean Salinity (SMOS) mission. Synthetic brightness temperature data were created using the Common Land (land surface) Model, coupled with a microwave emission model and set within the framework of the North American Land Data Assimilation System (NLDAS). Soil moisture, vegetation optical depth, and effective physical temperature were retrieved using a multiobjective calibration routine similar to the proposed SMOS retrieval algorithm for a typical on-axis range of look angles. The impact of heterogeneity both in the near-surface profiles of soil moisture and temperature and in the land cover on the accuracy of the retrievals was examined. There are significant errors in the retrieved parameters over regions with steep gradients in the near-surface soil moisture profile. These errors are approximately proportional to the difference in the soil water content between the top (at 0.7 cm) and second layer (at 2.7 cm) of the land surface model. The errors resulting from heterogeneity in the land cover are smaller and increase nonlinearly with increasing land-surface heterogeneity (represented by the standard deviation of the optical depth within the pixel). The most likely use of retrieved soil moisture is through assimilation into an LDAS for improved initiation of weather and climate models. Given that information on the soil moisture profile is already available within the LDAS, the error in the retrieved soil moisture as a result of the near-surface profile can be corrected for. The potential errors as a result of land-surface heterogeneity can also be assessed for use in the assimilation process. [ABSTRACT FROM AUTHOR]

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤ 100   kg   m − 2 ) and low values of cloud liquid water path (LWP 100–150 g   m − 2 ), the scattering of light snowfall (intensities ≤ 0.5   mm   h − 1 ) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg   m − 2 and the LWP is greater than 100–150 g   m − 2 , the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg   m − 2 and LWP is lower than 100–150 g   m − 2 , the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain. [ABSTRACT FROM AUTHOR]

The European Space Agency (ESA's) Soil Moisture and Ocean Salinity (SMOS) satellite mission was launched in November 2009. SMOS carries the first L-band (1.4 GHz) 2-D synthetic aperture microwave radiometer that produces multi-angular dual polarized (or fully polarized) brightness temperature. The objective of SMOS mission is to provide global surface soil moisture maps over the land surfaces with an accuracy of 0.04 m3m 3. The SMOS soil moisture retrieval algorithm was developed, which processes Level 1C products (multi-angular brightness temperatures) to Level 2 SM products (soil moisture maps). This algorithm is based on the comparison between the brightness temperatures from SMOS and the simulated brightness temperatures data (simulated TB) using L-MEB model. Thus, the evaluation of SMOS brightness temperatures is a necessary step before using of Level 2 Soil Moisture products. Therefore, the objective of this research is to evaluate the horizontal and vertical full polarized brightness temperatures data (TBh, TBv) from the SMOS MIR_SCLF1C products at the five meteorological stations in the west and southwest of Iran. Evaluation of SMOS brightness temperature data (SMOS TB) was done through a comparison between the SMOS TB and simulated TB from the L-MEB model. The SMOS MIR_SCLF1C (Level 1C Full Polarization Land Science measurements) products, which were provided through the ESA, contains the multi-angular brightness temperatures at the top of the atmosphere in the antenna polarization reference frame. In this study, the MIR_SCLF1C products, version 505 for the period 2012-2013 were evaluated. The ESA's SMOS Matlab codes on Linux was used to reading and deriving TB, Incidence angles, Geometric and Faraday rotations and other required data from MIR_SCLF1C products. The L-MEB (L-band Microwave Emission of the Biosphere) model is the radiative transfer model, which has been specifically developed to simulate the L-band microwave emission (brightness temperature) over land surfaces. In this research, the simulation of TB (TBh, TBv) at the five meteorological stations was carried out using L-MEB model (MATLAB function) and ground-based measurements. The model was simulated TB at the Earth's surface reference. Therefore, SMOS TB data was projected from the antenna reference frame to the Earth's surface reference frame using an algorithm provided by the CESBIO (Centre d'Etudes Spatiales de la BIOsphére) team. Four statistical metrics and Taylor diagram were used for the evaluation of results; the Root Mean Squared Difference (RMSD), the centered Root Mean Square Difference (cRMSD), the Mean Bias Error or bias and the correlation coefficient (R). The Taylor diagrams are used to represent three statistical metrics (R, cRMSD and standard deviation) on two dimensional plots to graphically describing how closely SMOS TB matches simulated TB. Based on the research algorithm, the Evaluation model for the SMOS brightness temperatures data (TBh, TBv) was obtained. The Evaluation model was run for five metrological stations and simulated TB data from L-MEB model and SMOS BT from the MIR_SCLF1C product was saved as the output of the model to evaluation. The results of the comparison between the SMOS TBh, TBv data and simulated TBh, TBv show that SMOS TB have an underestimation at Ahvaz, Sararod, Sarableh stations, whereas an overestimation of the SMOS BT was detected at Darab, Ekbatan stations. According to RMSD results, the SMOS TBh data at Ahvaz, Sarableh stations and the SMOS TBv data at Ahvaz, Darab, Sarableh stations have the highest accuracy. The Taylor diagrams shows the strong correlation (RTBh = 0.8-0.9 and RTBv =0.81-0.93) between the SMOS TB and simulated TB data at all stations. Besides, the lowest value of the cRMSD of the SMOS TB data was obtained at Ahvaz (TBh =5.34, TBv = 5.67 K) and Darab stations (TBh =8.54, TBv = 5.9 K). In addition, these diagrams indicate that the standard deviation of SMOS TBh data at Sarableh, Ahvaz, Sararod stations and SMOS TBv data at Sarableh, Darab, Ahvaz stations are closer to the simulated TB data than other stations. Overall, the findings of this paper give valuable information about the uncertainties and errors of SMOS brightness temperatures data (MIR_SCLF1C) in the study area. Therefore, this research could be as a reference for using the SMOS soil moisture products (Level 2 Soil Moisture) in hydrology and meteorology studies in Iran. [ABSTRACT FROM AUTHOR]

Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of them are not fully symmetrical, have a shallow warm-core structure, and a weak frontal activity. Finding a clear definition and potential classification of Medicanes based on their initiation and intensification processes, understanding the role of convection, and identifying the evolution to a TLC phase are all current research topics. In this study, passive microwave (PMW) measurements and products are used to characterize warm core (WC) and deep convection (DC) for six Medicanes that occurred between 2014 and 2021. A well-established methodology for tropical cyclones, based on PMW temperature sounding channels, is used to identify the WC while PMW diagnostic tools and products (e.g., cloud-top height (CTH) and ice water path (IWP)), combined with lightning data, are used for DC detection and characterization. The application of this methodology to Medicanes highlights the possibility to describe their WC depth, intensity, and symmetry and to identify the cyclone center. We also analyze to what extent the occurrence and characteristics of the WC are related to the Medicane's intensity and DC development. The results show that Medicanes reaching full TLC phase are associated with deep and symmetric WCs, and that asymmetric DC features in the proximity of the center, and in higher CTH and IWP values, with scarce lighting activity. Medicanes that never develop to a fully TLC structure are associated with a shallower WC, weaker and more sparse DC activity, and lower CTHs and IWP values. Ultimately, this study illustrates the potential of PMW radiometry in providing insights into dynamic and thermodynamic processes associated with Medicanes' WC characteristics and evolution to TLCs, thus contributing to the ongoing discussion about Medicanes' definition. [ABSTRACT FROM AUTHOR]

A polynya is an area of open water or reduced concentration of sea ice surrounded by either concentrated sea ice or land ice. They are often seen as sites of intense ocean–atmosphere heat exchange and as ice production factories. Given their importance, it is crucial to quantify the accuracy of satellite-derived polynya information. Polynyas in their early evolution phase are generally narrow and occur at scales likely too fine to be detected by widely used passive microwave (PMW) radiometric sensors. We derived 40 m scale polynya information over the western Ross Sea from high-resolution Synthetic Aperture Radar (SAR) Sentinel-1 C-band data and examined discrepancies with larger-scale estimates. We utilized two automated algorithms, supervised (a rule-based approach) and unsupervised (a combination of texture analysis with k-means clustering), to accurately identify the polynya areas. We generated data for validation using Sentinel-1 data at instances where polynyas can be visually delineated. Results from PMW sensors (NSIDC and AMSR2) and SAR-based algorithms (rule-based and texture-based) are compared with manually delineated polynya areas obtained through Sentinel-1. Analysis using PMW sensors revealed that NSIDC overestimates larger polynyas and underestimates smaller polynyas compared to AMSR2. We were more accurately able to identify polynya presence and area using Sentinel-1 SAR observations, especially in clear cases and cases when PMW data miscalculates the polynya's presence. Of our SAR-based algorithms, the rule-based approach was more accurate than the texture-based approach at identifying clear polynyas when validated against manually delineated regions. Altogether, we emphasize the need for finer spatio-temporal resolution data for polynya studies. [ABSTRACT FROM AUTHOR]

Satellite‐based precipitation estimations provide frequent, large‐scale measurements. Deep learning has recently shown significant potential for improving estimation accuracy. Most studies have employed a two‐stage framework, which is a sequential architecture of a rain/no‐rain binary classification task followed by a rain rate regression task. This study proposes a novel precipitation retrieval framework in which these two tasks are simultaneously trained using multi‐task learning approach (MTL). Furthermore, a novel network architecture and loss function were designed to reap the benefits of MTL. The proposed two‐task model successfully achieved a better performance than the conventional single‐task model possibly due to efficient knowledge transfer between tasks. Furthermore, the product intercomparison showed that our product outperformed existing products in rain rate retrieval and also yielded better skills in the rain/no‐rain retrieval task. Plain Language Summary: Satellite‐based observation can provide frequent large‐scale precipitation measurements. Recently, machine learning techniques have been widely used in satellite precipitation estimates. This study introduces a novel deep learning (DL) method using multi‐task approach. The proposed method enables the simultaneous learning of rain/no‐rain classification and rain rate estimates. The experiment determined that our method achieved a better result than the conventional DL. Furthermore, a comparison between existing products demonstrated that our method provided a better rain rate estimate and comparable rain/no‐rain classification. Key Points: Multi‐task learning was devised to infer precipitation intensity and rain/no‐rain classification simultaneouslySimultaneous learning demonstrated a better performance than the conventional single task learningRetrieval based on the proposed algorithm outperformed existing satellite precipitation products [ABSTRACT FROM AUTHOR]

This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.