The Chinese Ocean Salinity Satellite is designed to monitor global sea-surface salinity (SSS). One of the main payloads onboard the Chinese Ocean Salinity Satellite, named the Interferometric ...Microwave Radiometer (IMR), is a two-dimensional interferometric radiometer system with an L-band, Y-shaped antenna array. The comparison of two different array orientations is analyzed by an end-to-end simulation based on the configuration of the IMR. Simulation results of the different array orientations are presented and analyzed, including the brightness temperature (TB) images, the distribution of the incidence angles in the field of view, the TB radiometric resolutions, the spatial resolutions, the number of measurements in the Earth grid and the expected SSS accuracy. From the simulations we conclude that one of the array orientations has better performance for SSS inversion than the other one. The advantages mainly result in wider swath and better SSS accuracy at the edge of the swath, which then improve the accuracy of the monthly SSS after averaging. The differences of the Sun’s effects for two different array orientations are also presented. The analysis in this paper provides the guidance and reference for the in-orbit design of the array orientation for the IMR.
The Earth Exploration Satellite Service (EESS) for passive sensing has a primary frequency allocation in the 1400–1427 MHz band. All emissions unauthorized in this band are called RFI (Radio ...Frequency Interference). The SMOS (Soil Moisture and Ocean Salinity) mission is greatly perturbed by RFI impeding ocean salinity retrieval, especially in coastal areas such as the SCS (South China Sea), where the observed data has been discarded massively. At present, there is no way to eliminate the RFI impact on the retrieved salinity, other than by detecting and shutting down the emissions from the sources. However, it may be effective in a scientific sense if RFI can be quantified and applied to the salinity retrieval process. Therefore, this study proposes an RFI measuring method that can investigate contamination in both prominent and moderate respects, aroused either by on-site emissions or nearby continents. Based on the proposed method, two levels of hierarchical RFI maps of the SCS region, including the separated one and the merged one, are presented and discussed, indicating more severe contamination in northern and western SCS. Moreover, to verify the generalization of the method on open oceans far from continents, an area in the middle central Pacific is selected and tested. The result shows few or no RFI in this unattended region, which is consistent with the authors’ knowledge. This study presents the concept of the “RFI map” to describe the contamination, which will hopefully help researchers comprehend the RFI state intuitively and assist in ocean salinity retrieval statistically.
We validate Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) retrieved during August 2010 from the European Space Agency SMOS processing. Biases appear close to land and ice and ...between ascending and descending orbits; they are linked to image reconstruction issues and instrument calibration and remain under study. We validate the SMOS SSS in conditions where these biases appear to be small. We compare SMOS and ARGO SSS over four regions far from land and ice using only ascending orbits. Four modelings of the impact of the wind on the sea surface emissivity have been tested. Results suggest that the L-band brightness temperature is not linearly related to the wind speed at high winds as expected in the presence of emissive foam, but that the foam effect is less than previously modeled. Given the large noise on individual SMOS measurements, a precision suitable for oceanographic studies can only be achieved after averaging SMOS SSS. Over selected regions and after mean bias removal, the precision on SSS retrieved from ascending orbits and averaged over 100 km × 100 km and 10 days is between 0.3 and 0.5 pss far from land and sea ice borders. These results have been obtained with forward models not fitted to satellite L-band measurements, and image reconstruction and instrument calibration are expected to improve. Hence, we anticipate that deducing, from SMOS measurements, SSS maps at 200 km × 200 km, 10 days resolution with an accuracy of 0.2 pss at a global scale is not out of reach.
A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (Rrs) observed by the Chinese ocean color and temperature ...scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of Rrs565 and Rrs520/Rrs443, Rrs565/Rrs490, Rrs520/Rrs490, Rrs490/Rrs443, and Rrs670/Rrs565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m3, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.
This Special Issue gathers papers reporting research on various aspects of remote sensing of sea surface salinity (SSS) and the use of satellites SSS in oceanography. It includes contributions ...presenting improvements in empirical or theoretical radiative transfer models; mitigation techniques of external interference such as radio frequency interferences (RFI) and land contamination; comparisons and validation of remote sensing products with in situ observations; retrieval techniques for improved coastal SSS monitoring, high latitude SSS monitoring and assessment of ocean interactions with the cryosphere; and data fusion techniques combining SSS with sea surface temperature (SST). New instrument technology for the future of SSS remote sensing is also presented.
The freshness of information is critical for patient vital signs and physiological parameters in the healthcare system because changes in these parameters can indicate a patient's overall health ...status and guide treatment decisions. In this paper, we consider an edge device-aided smart healthcare system that relies on a resource management scheme. The medical center requires patient information, and edge nodes process the latest measurements received by each wearable device. Our goal is to find the optimal strategy to minimize the worst case of information freshness, i.e., the peak AoI age of information (PAoI). Firstly, we model the problem as a Markov Decision Process (MDP). Then, we design two separate Reinforcement Learning (RL)-based algorithms to find the optimal strategy that minimizes energy consumption and the average PAoI. To minimize energy consumption, we propose a pair of sleep mechanisms, including the <inline-formula> <tex-math notation="LaTeX">N </tex-math></inline-formula> policy and <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> wake-up policy, to improve the energy efficiency of each wearable device. Simulation results show that the proposed wake-up strategy and the proposed RL algorithm make a better trade-off between the average PAoI and power dissipation compared to the baseline schemes.
Severe convective weather is hugely destructive, causing significant loss of life and social and economic infrastructure. Based on the U-Net network with the attention mechanism, the recurrent ...convolution, and the residual module, a new model is proposed named ARRU-Net (Attention Recurrent Residual U-Net) for the recognition of severe convective clouds using the cloud image prediction sequence from FY-4A data. The characteristic parameters used to recognize severe convective clouds in this study were brightness temperature values TBB9, brightness temperature difference values TBB9−TBB12 and TBB12−TBB13, and texture features based on spectral characteristics. This method first input five satellite cloud images with a time interval of 30 min into the ARRU-Net model and predicted five satellite cloud images for the next 2.5 h. Then, severe convective clouds were segmented based on the predicted image sequence. The root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and correlation coefficient (R2) of the predicted results were 5.48 K, 35.52 dB, and 0.92, respectively. The results of the experiments showed that the average recognition accuracy and recall of the ARRU-Net model in the next five moments on the test set were 97.62% and 83.34%, respectively.
Synthetic Aperture Interferometric Radiometer (SAIR) as one of the most advanced instruments for Sea Surface Salinity (SSS) observation, has been in service on SMOS mission for years and is planned ...on the Chinese Ocean Salinity Satellite in the near future. However, a lot of Radio Frequency Interference (RFI) emissions are found in SMOS views, which contaminate the brightness temperature measurements of the SAIR instrument, and further impede the retrieval of SSS fields. Concerning SAIR’s operating mode, this study proposes an RFI mitigation method comprising two algorithms for co- and cross-polarization, respectively. First, RFI signatures are identified based on a series of thresholds defined by radiation theory, and then mitigated through a simple machine learning technique of Support Vector Regression (SVR), leveraging either SAIR’s multi-angle measurements or sea surface roughness descriptors, depending on the specific polarization mode. Finally, the outputs of all polarizations are merged and written back to the Level 1C brightness temperature product as the final result. Using the proposed method, the notable outliers arose from RFI contamination are attenuated, and the variation of standard deviations over nearby snapshots is smoothed, as expected on a homogeneous ocean. Furthermore, with the official L2OS software implementing the SSS retrieval procedure from the rewritten Level 1C brightness temperatures, the data re-gain of SSS fields is achieved in some places that are not attainable for the current SMOS Level 2 SSS products, with a reasonable error compared to WOA2009 SSS, confirming the validity of the proposed method. Hopefully, this work could provide a practical solution to current and future SAIR observing predicaments.
The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in November 2009, carries the first interferometric radiometer at L-band (1.4 GHz) in orbit. Over the open ocean and for moderate wind ...speeds (WSs), the SMOS brightness temperatures (TB) are at first order consistent with simulated TB of theoretical prelaunch models implemented in the European Space Agency Level 2 Ocean Salinity processor. However, we found large discrepancies between measurements and model simulations when WS is above 12 ms -1 . A new set of parameters for a sea wave spectrum and a foam coverage model that can be used for simulating L-band radiometer data over a large range of WS is proposed based on the deduced wind-induced components from the SMOS data. The quality of the SMOS retrieved sea surface salinity (SSS) with the new emissivity model is estimated by comparing it with the World Ocean Atlas 2005 climatological SSS and the Array for Real-Time Geostrophic Oceanography (ARGO) SSS.
The Soil Moisture and Ocean Salinity (SMOS) mission carries the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument. It is the first time that an interferometric radiometer is in ...orbit. The objective of this paper is to assess the quality of the brightness temperatures (TBs) derived from this novel instrument, as processed with the SMOS operational chain at the end of the SMOS commissioning phase. Extensive comparisons have been conducted between reconstructed TBs derived from MIRAS measurements (MIRAS TB) and TBs simulated using the default radiative transfer model implemented in the European Space Agency SMOS ocean salinity processor and the European Centre for Medium-Range Weather Forecast forcings. At first order, the North-South variability of MIRAS TB due to geophysical variations of temperature, salinity, and wind speed over the ocean is consistent with the simulated L-band signal, and the standard deviation of the MIRAS TB minus the model simulations is close to the theoretical radiometric resolution. On the other hand, biases of several Kelvins, that depend on the location in the field of view, are observed between averaged MIRAS TB and simulations. After these biases are removed, the North-South gradient of sea surface salinity is well sensed by MIRAS except at high wind speed.
PDF
