Satellite Passive Microwave Observations Research Articles

Passive microwave Arctic sea ice melt onset dates from the advanced horizontal range algorithm 1979–2022

The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979–2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.

Evaluation of Improvement Schemes for FY-3B Passive Microwave Soil-Moisture Estimates Retrieved Using the Land Parameter Retrieval Model

Satellite observations have provided global and regional soil-moisture estimates in the last four decades. However, the accuracy of these observations largely depends on reducing uncertainties in the retrieval algorithms. In this study, we address two challenges affecting the quality of soil-moisture estimates from a widely used soil-moisture-retrieval model, the land parameter retrieval model (LPRM). We studied two improvement schemes that were aimed at reducing uncertainties in open water signals (the LPRMv6_OWF) and vegetation signals (the LPRMv6_Veg), as well as a scheme to reduce their combined impacts (the LPRMv6_OWFVeg) on LPRM-retrieved soil moisture using the FengYun-3B (FY-3B) satellite observations. To assess the impacts of the improvement schemes, we utilized in situ soil moisture from the Jiangsu and Jiangxi provinces in China. We found that the retrievals (Rs) of the LPRMv6_Veg and the LPRMv6_OWFVeg were mainly in the range of 0.2 to 0.5 in Jiangsu and Jiangxi, with increases of 0.1 compared to those of the LPRMv6. The standard deviation (SD) of the LPRMv6_OWFVeg increased in Jiangsu, while the R of the LPRMv6_OWF increased in Jiangsu by 0.05–0.1 compared to that of the LPRMv6, but the SD tended to become worse. In Jiangxi, there was an increase of 0.1 in R. The results show that each of these algorithms improved the accuracy of soil-moisture inversion to some extent, compared to the original algorithm, with the LPRMv6_OWFVeg showing the greatest improvement, followed by the LPRMv6_Veg. The accuracy of both the LPRMv6_OWF and the LPRMv6_OWFVeg decreased to some extent when the open-water fraction (OWF) was greater than 0.2. Full areal extent analyses based on triple collocation showed significant improvements in correlations and minimized errors across different vegetation scenarios over the entire region of China in both the LPRMv6_OWF and the LPRMv6_Veg. However, reduced qualities were found in arid regions in northern China because of the nonlinear relationships between land-surface temperature, vegetation, and soil moisture in the LPRM. These results highlight important lessons for developing comprehensive improvement schemes for soil-moisture retrievals from passive microwave satellite observations.

Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts

Satellite passive microwave (PMW) observations are important data sources for obtaining land surface temperature (LST) especially for cloudy conditions. Considering the generally poorer spatial representativeness of PMW observations, fusion of PMW-based and thermal infrared-(TIR-) based observations have been considered as an important approach to generate all-weather LST with appropriate resolution. However, the existing penetration difference between PMW-based and TIR-based observations has been an obstacle against this objective. This is especially the case for desert regions, where the penetration depth of PMW observations is larger than under other conditions and the relationship between TIR-based and PMW-based signals are thus more complicated. In this regard, this study develops an easy and high-efficiency model suitable for transforming PMW-based subsurface LST to TIR-like skin surface temperature in four regions across the world characterized by typical arid climate. The developed model employs microwave-derived surface soil moisture (SSM) and vegetation transmissivity (VT) as major inputs to quantify that “resampling depth difference” between TIR-based and PMW-based observations. Results show that the model can significantly reduce that difference (RMSE) from >10 K to around 2–5 K in the typical desert regions. The developed model is beneficial to obtaining high-resolution all-weather LST for the vast desert regions across the world in subsequent studies.

Challenges in the Description of Sea Ice for a Kilometer-Scale Weather Forecasting System

Abstract Simulation of atmosphere–ocean–ice interactions in coupled Earth modeling systems with kilometer-scale resolution is a new challenge in operational numerical weather prediction. This study presents an assessment of sensitivity experiments performed with different sea ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea ice detection of the higher-resolution retrievals based on synthetic aperture radar instruments. We perform sensitivity experiments with sea ice concentration fields of 1) the global ECMWF-IFS forecast system, 2) a newly developed multisensor product processed through a coupled sea ice–ocean forecasting system, and 3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100) km scales in the northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and toward the Fram Strait. These differences have a direct impact on the modeled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2-m air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multisensor product; however, south of Svalbard this product has a negative impact. The different sea ice products are resulting in changes of the surface turbulent heat flux of up to 400 W m−2, which in turn results in T2M variations of up to 5°C. Over a 2-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea ice. Significance Statement Weather forecasting in polar regions requires an accurate description of sea ice properties due to the very important atmosphere–ocean–ice interactions. With the increasing resolution of weather forecasting systems, there is also a need to advance the resolution of the sea ice characteristics in the models. This is, however, not straightforward due to various issues in the sea ice satellite products. This study explores new products and approaches to integrate high-resolution sea ice in a weather prediction system. We find that the model is sensitive to the choice of the sea ice product and that it is still challenging to provide an accurate sea ice field on a kilometer-scale resolution.

Quantifying Antarctic‐Wide Ice‐Shelf Surface Melt Volume Using Microwave and Firn Model Data: 1980 to 2021

AbstractAntarctic ice‐shelf stability is threatened by surface melt, which has been implicated in several ice‐shelf collapse events over recent decades. Here, we first analyze cumulative days of wet snow/ice status (“melt days”) for melt seasons from 1980 to 2021 over Antarctica's ice shelves using passive and active microwave satellite observations. As these observations do not directly reveal meltwater volumes, we calculate these using the physics‐based multi‐layer snow model SNOWPACK, driven by the global climate‐reanalysis model Modern‐Era Retrospective analysis for Research and Applications Version 2. We find a strong non‐linear relationship between melt days and meltwater production volume. SNOWPACK's calculation of melt days shows agreement with observations of both cumulative days, and spatial and interannual variability. Highest melt rates are found on the Peninsula ice shelves, particularly in the 1992/1993 and 1994/1995 austral summers. Over all ice shelves, SNOWPACK calculates a small, but significant, decreasing trend in both annual melt days and meltwater production volume over the 41 years.

Estimation of summer pan-Arctic ice draft from satellite passive microwave observations

Pan-Arctic observations of summer sea ice thickness have been a challenging problem despite their global significance in weather/climate analysis and prediction. To solve this problem, this study developed a method for estimating the pan-Arctic ice draft (depth of sea ice below sea surface level) using spaceborne passive microwave-measured brightness temperatures (TBs). This study found that the time series of TBs is highly correlated with the corresponding time series of ice draft (D)s measured by three upward-looking sonar (ULS) sites over the Beaufort Sea during the ice melting season. Based on this finding, a D estimation equation was derived by relating satellite-measured TBs at microwave frequencies to Ds. Pan-Arctic D distributions from June to August obtained from this study describe well the general thinning process of Arctic sea ice during the summer of 2021. The validation of estimated Ds with other ULS measurements, which were not used for training data, showed good agreement between them suggesting the robustness of the developed estimation method over the Arctic basin. In addition, validation was made against ice mass balance buoy-measured sea ice thickness, and result shows a good agreement between buoy-measured ice thickness and estimated ice thickness converted from the D. Meanwhile, relatively high uncertainty in D estimation above 2 m shown in the result and validation suggests the potential for future improvement of D estimation in the early melting period. As the proposed method can provide summer pan-Arctic D distributions on a daily basis, the D from this study can be applied for generating the initial field for the sea ice and global climate models through data assimilation.

Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz

AbstractArctic sea ice thickness (SIT) has been mostly retrieved from lidar and radar altimeters since the 2000s. However, the repeatability of altimeters and their spatial coverage limit SIT estimates spatially and temporally. On the other hand, the passive microwave (PMW) radiometers have daily basin‐scale coverage of the Arctic. In this study, we propose a SIT retrieval from PMW observations, derived from a statistical inversion technique. It is based on the evidence of high correlations between PMW observations and existing altimetric satellite‐derived SIT, especially at 36 GHz. Lidar ICESat‐2 SIT products are used to train a neural network with multiple combinations of brightness temperatures between 1.4 and 36 GHz as inputs over the 2018–2019 time period. The PMW retrieved SIT can mimic the lidar SIT product over the full winter over the Arctic, with a correlation of 0.85, and a root mean square difference (RMSD) of 0.54 cm. Results are also compared with the SIT product CS2SMOS (CryoSat‐2 and Soil Moisture Ocean Salinity merged product), with SIT from a coupled ice/ocean reanalysis model and with the Operation IceBridge QuickLook airborne SIT measurements. The PMW SIT retrieval with all frequencies from 1.4 to 36 GHz shows a correlation of 0.72 and a RMSD of 57 cm when compared to OIB‐QL measurements, for large SIT (mostly above 3 m), under multi‐year ice environments. The PMW SIT retrieval using only 18 and 36 GHz has similar performances and could allow the calculation of long time series, these microwave frequencies being available from satellites since the 1980s.

Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth

Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however hampered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For example, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements; (2) to test different model structures to estimate LFMC from VOD; and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal sampling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.

Sensitivity of Passive Microwave Satellite Observations to Snow Density and Grain Size Over Arctic Sea Ice

Sensitivity of Passive Microwave Satellite Observations to Snow Density and Grain Size Over Arctic Sea Ice

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.

Arctic sea ice coverage from 43 years of satellite passive-microwave observations

Satellite passive-microwave instrumentation has allowed the monitoring of Arctic sea ice over the past 43 years, and this monitoring has revealed and quantified major changes occurring in Arctic sea ice coverage. The 43-year 1979–2021 record shows considerable interannual variability but also a long-term downward trend in Arctic sea ice that is clear from many vantage points: A linear-least-square trend of −54,300 ± 2,700 km2/year for yearly average sea ice extents; statistically significant negative trends for each of the 12 calendar months; negative trends for each of nine regions into which the Arctic sea ice cover is divided; the fact that for all 12 calendar months the highest monthly average sea ice extent came in the first 8 years of the record and the lowest monthly average sea ice extent came in the last 10 years of the record; and a prominent shortening of the sea ice season throughout much of the marginal ice zone, with the length of the sea ice season in some locations decreasing by over 100 days and some locations previously experiencing months-long sea ice seasons now typically no longer having a sea ice season at all. The overall, Arctic-wide trend value of the yearly average sea ice extents since 1979 has consistently had a negative magnitude exceeding two standard deviations of the trend line slope since 1990 and has remained in the narrow range of −53,000 km2/yr to −55,500 km2/yr since 2011.

The Estimation of the Total Freeboard of Arctic Sea Ice in Winter Using Passive Microwave Satellite Measurements

Abstract A method for estimating the total freeboard hf of the sea ice in the Arctic basin was developed in this study. To utilize the dielectric properties of microwave measurements on the sea ice freeboard, we adopted the spectral difference between the microwave frequencies (e.g., the gradient ratio). Satellite lidar altimetry data were utilized as a reference, and two pairs of gradient ratios [GR(36.5, 18.7) and GR(10.7, 6.9)] and an optional brightness temperature for first-year ice [that is, TBH(6.9)] were converted from passive microwave sensors into hf using the multiple linear regression equation. Using this method, we estimated hf without direct altimetry measurements. The developed method was evaluated using Operation IceBridge data and the relationship between the regressed hf and Operation IceBridge hf had a correlation coefficient R of 0.761 and a nearly unbiased (approximately −0.4 cm) pattern. Because passive microwave measurements are taken in the Arctic daily, the approach presented in this study has the potential to enable daily hf estimations for the Arctic. Significance Statement Arctic sea ice is one of the most critical indicators when monitoring climate change. Precise and continuous observations of sea ice thickness are essential to understand Arctic sea ice. This study attempts to estimate sea ice thickness using passive microwave satellite data. Passive microwave satellite observations are advantageous because of their wide spatial coverage and long-term records. Therefore, the suggested method in this study can be used for filling in gaps in coverage between sea ice thickness estimates from L-band radiometry and radar/lidar altimetry. The total freeboard is proportional to the thickness of sea ice, which is converted into thickness using the hydrostatic equation. The estimated total freeboard during two winter periods (2018/19 and 2019/20) demonstrates a plausible geographical distribution over the Arctic and indicates good agreement with airborne measurements.

Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm.The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model.The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations.

A Satellite-Observed Substantial Decrease in Multiyear Ice Area Export through the Fram Strait over the Last Decade

Revealing the changes in the Fram Strait (FS) multiyear ice (MYI) export is crucial due to their climate relevance in the context of the loss rate of MYI being faster than that of the total ice in the Arctic. Here, we estimated winter (October–April) MYI area export through the FS over the last 2 decades by using updated MYI concentration data retrieved from active and passive microwave satellite observations. We divided the period into two regimes relative to the ice index: D1 (2002/03–2010/11) and D2 (2012/13–2019/20). The observed variations of winter MYI exports D2 were compared with those of the previous decade D1. The results show that the MYI area exports display strong interannual variability. A significant decrease in MYI export for the periods between D1 and D2 is noted. On average, the wintertime MYI area exports declined sharply by 22% from 3.82 × 105 km2 in D1 to 3.00 × 105 km2 in D2. In addition, the percentage of MYI in the total sea ice outflow through the FS (PCM) also decreased distinctly from 72% in D1 to 59% in D2. Statistics show that weekly sea ice drift across the strait can explain 76% of the MYI area export variability. Furthermore, the dominant atmospheric drivers contributing to the decline in MYI area export during D2 were examined. In the last decade (D2), the strengthened low pressure in the North Atlantic sector, combined with an eastward shift in the axis of dipole anomaly (DA), resulted in reduced MYI advection from the Beaufort Sea and Siberian Coast toward the FS. Moreover, weakened cyclonic activity south of the FS also contributed to the reduction in MYI export during D2.

Diurnal and Seasonal Variations of Passive and Active Microwave Satellite Observations Over Tropical Forests

AbstractDocumenting the large scale variability of tropical forest structure and function is needed for improved understanding of the carbon and water cycles. The seasonal and diurnal cycles of passive and active microwave satellite observations are jointly analyzed for the first time, using the Global Precipitation Mission (GPM). Collocated backscattering coefficients from the GPM Dual‐frequency Precipitation Radar (DPR) and emissivities derived from the GPM Microwave Imager (GMI) are studied for 4 yr over the Tropics, at the available multiple frequencies, angles, and polarizations. Our analysis confirms the diurnal patterns already observed with radars, with a maximum backscatter in the early morning attributed to the water xylem refill at night. At the same time, early in the morning, we evidence a minimum emissivity over most of tropical forests: the backscatter and emissivity diurnal cycles tend to be in phase opposition, as well as their seasonal cycles. However, during the dry season in the south eastern Amazonian forest where precipitation can be low for several months, the emissivity diurnal cycle changes, implying a different behavior of the forest in this area, related to the state of the canopy.

Evaluation of GPROF V05 Precipitation Retrievals under Different Cloud Regimes

AbstractPrecipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard Profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

Understanding the Non‐Linear Response of Summer Evapotranspiration to Clouds in a Temperate Forest Under the Impact of Vegetation Water Content

AbstractUnderstanding the cloud impact on forest evapotranspiration (ET) is crucial for studying the interaction of vegetation‐cloud‐atmosphere. Combining long‐term (2003–2010) satellite passive microwave observations and in‐situ measurements, a non‐linear response of canopy‐scale ET to cloud increase was found at a temperate forest in Northeast China. As cloud increased, an initial enhancement (4%–10%) in ET occurred under less cloudy sky, while a significant reduction (>20%) in ET occurred under more cloudy sky. The phenomenon existed under both high and low vegetation water content (VWC) indicated by satellite microwave emissivity difference vegetation index (EDVI). Analysis showed that this was the combined effect from the enhancement (5%–30%) in evaporative fraction (EF) and the reduction (5%–50%) in net radiation under cloud increase. Decoupling analysis based on coefficients (ρ) of path analysis model showed that enhanced EF (ρ > 0.61) rather than radiation (ρ < 0.47) dominated the ET enhancement under less cloudy sky, while the control of reduced radiation became stronger (ρ > 0.63) and could not be compensated by increased EF (ρ < 0.48) under more cloudy sky. EF enhancement under clouds was strongly correlated with the decline in canopy resistance (Rs) which was dominated by vapor pressure deficit (VPD). Higher VWC increased ET via reducing Rs and enlarging EF. This positive effect of VWC was more noticeable under less cloudy sky. Associated mechanisms could be related to the dynamic controls of plant physiology and environmental conditions induced by VWC and clouds. This study highlighted the dynamic effect of clouds and VWC on forest ET and improved our knowledge of vegetation‐cloud interactions.

Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations

A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27-1.77 K. Validation based on in situ LST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46-4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.

Technical note: A sensitivity analysis from 1 to 40 GHz for observing the Arctic Ocean with the Copernicus Imaging Microwave Radiometer

Abstract. The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority missions for the expansion of the Copernicus program within the European Space Agency (ESA). It is designed to respond to the European Union Arctic policy. Its channels, incidence angle, precision, and spatial resolutions have been selected to observe the Arctic Ocean with the recommendations expressed by the user communities. In this note, we present the sensitivity analysis that has led to the choice of the CIMR channels. The famous figure from Wilheit (1979), describing the frequency sensitivity of passive microwave satellite observations to ocean parameters, has been extensively used for channel selection of microwave radiometer frequencies on board oceanic satellite missions. Here, we propose to update this sensitivity analysis, using state-of-the-art radiative transfer simulations for different geophysical conditions (Arctic, mid-latitude, tropics). We used the Radiative Transfer Model (RTM) from Meissner and Wentz (2012) for the ocean surface, the Round Robin Data Package of the ESA Climate Change Initiative (Pedersen et al., 2019) for the sea ice, and the RTM from Rosenkranz (2017) for the atmosphere. The sensitivities of the brightness temperatures (TBs) observed by CIMR as a function of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), ocean wind speed (OWS), total column water vapor (TCWV), and total column liquid water (TCLW) are presented as a function of frequency between 1 and 40 GHz. The analysis underlines the difficulty to reach the user requirements with single-channel retrieval, especially under cold ocean conditions. With simultaneous measurements between 1.4 and 36 GHz onboard CIMR, applying multi-channel algorithms will be facilitated, to provide the user community with the required ocean and ice information under arctic environments.

An evaluation of the synergy of satellite passive microwave observations between 1.4 and 36 GHz, for vegetation characterization over the Tropics

Satellite passive microwave observations from 1.4 to 36 GHz already showed sensitivity to vegetation parameters, primarily through the calculations of the Vegetation Optical Depth (VOD) at individual window frequencies, separately. Here we evaluate the synergy of this frequency range for vegetation characterization, through the estimation of two vegetation parameters, its foliage and the photosynthesis activity as described by the Normalized Difference Vegetation Index (NDVI), and its woody components and carbon stock as described by the Above Ground Carbon (AGC), using different combinations of channels in the considered frequency range. Neural network retrievals are trained on these two vegetation parameters (NDVI and AGC), for several microwave channel combinations, including the future Copernicus Imaging Microwave Radiometer (CIMR) that will observe simultaneously in window channels from 1.4 to 36 GHz, for the first time. This methodology avoids the use of any assumptions in the complex interaction between the surface (vegetation and soil) and the radiation, as well as any ancillary observations, to propose a genuine and objective evaluation of the information content of the passive microwave frequencies for vegetation characterization. Our analysis quantifies the synergy of the microwave frequencies from 1.4 to 36 GHz, and shows the expected potential of the CIMR instrument for the monitoring of vegetation parameters in tropical environments, as compared to current instruments. It also confirms that the 1.4 GHz observations have a high sensitivity to AGC, as compared to the other single frequencies up to 36 GHz, at least under tropical environments.