In this paper, a scheme is presented for detecting sea ice from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDM). Less spreading along delay and Doppler axes were observed in the DDMs of sea ice relative to those of seawater. This enables us to distinguish sea ice from seawater through studying the values of various DDM observables, which describe the extent of DDM spreading. The area associated with a DDM that results in an observable below or above a threshold value will be classified as covered by sea ice and seawater, respectively. In particular, this study applies an adaptive incoherent summation to each DDM with efforts to increase signal-to-noise ratio and avoid the averaging between DDMs collected over surfaces of different types. Accordingly, an adaptive threshold is employed for the derived observable based on the incoherent summation interval for its corresponding DDM. The proposed sea ice detection method is tested with five different DDM observables. Through comparing DDM observable-based detection results with ground-truth sea ice data, the feasibility of this method is validated with an accuracy of up to 99.73% based on the pixel number observable.

In this letter, a scheme that uses convolutional neural networks (CNNs) is proposed for sea ice detection and sea ice concentration (SIC) prediction from TechDemoSat-1 Global Navigation Satellite System Reflectometry delay-Doppler maps (DDMs). Specifically, a classification-orientated CNN was designed for sea ice detection and a regression-based one for SIC estimation. Here, DDM images were used as input, and SIC data from Nimbus-7 Scanning Multi-Channel Microwave Radiometer and Defense Meteorological Satellite Program Special Sensor Microwave Imager-Special Sensor Microwave Imager/Sounder sensors were modified as targeted output. In the experimental phase, the CNN output resulted from inputting full-size DDM data (128-by-20 pixels) showed better accuracy than that of the existing NN-based method. Besides, both CNNs and NNs with further processed input data (40-by-20 pixels, and with a fixed position in each image) were evaluated and the performance of both networks was enhanced. It was found that when DDM data are adequately preprocessed, CNNs and NNs share similar accuracy; otherwise the former outperforms the latter. Further conclusion was thus drawn that CNNs were more tolerant to the data format changes than NNs.

In this paper, a neural networks (NN) based scheme is presented for detecting sea ice and retrieving sea ice concentration (SIC) from global navigation satellite system reflectometry delay-Doppler maps (DDMs). Here, a multilayer perceptron neural network with back-propagation learning is adopted. In practice, two NN were separately developed for sea ice detection and concentration retrieval purposes. In the training phase, DDM pixels were employed as an input. The SIC data obtained by Nimbus-7 SMMR and DMSP SSM/I-SSMIS sensors were used as the target data, which were also regarded as ground-truth data in this paper. After the training process using a dataset collected around February 4, 2015, these networks were used to produce corresponding detection and concentration estimation for other four sets of DDM data, which were collected around February 12, 2015, February 20, 2015, March 16, 2015, and April 17, 2015, respectively. Results show high accuracy in sea ice detection and concentration estimation with DDMs using the proposed scheme. On average, the accuracy for sea ice detection is about 98.4%. In terms of estimated SIC, the mean absolute error is less than 9%, whereas the correlation coefficient is as high as 0.93 compared with the reference data. It was also found that low sea state and wind speed could lead to an overestimation of SIC for partially ice-covered region.

Knowledge of sea ice is critical for offshore oil and gas exploration, global shipping industries, and climate change studies. During recent decades, Global Navigation Satellite System-Reflectometry (GNSS-R) has evolved as an efficient tool for sea ice remote sensing. In particular, thanks to the availability of the TechDemoSat-1 (TDS-1) data over high-latitude regions, remote sensing of sea ice based on spaceborne GNSS-R has been rapidly growing. The goal of this paper is to provide a review of the state-of-the-art methods for sea ice remote sensing offered by the GNSS-R technique. In this review, the fundamentals of these applications are described, and their performances are evaluated. Specifically, recent progress in sea ice sensing using TDS-1 data is highlighted including sea ice detection, sea ice concentration estimation, sea ice type classification, sea ice thickness retrieval, and sea ice altimetry. In addition, studies of sea ice sensing using airborne and ground-based data are also noted. Lastly, applications based on various platforms along with remaining challenges are summarized and possible future trends are explored. In this review, concepts, research methods, and experimental techniques of GNSS-R-based sea ice sensing are delivered, and this can benefit the scientific community by providing insights into this topic to further advance this field or transfer the relevant knowledge and practice to other studies.

Abstract—In this paper, an effective schematic is developed for estimating sea ice thickness (SIT) from the reflectivity (􀀀) produced with TechDemoSat-1 (TDS-1) Global Navigation Satellite System-Reflectometry (GNSS-R) data. Here, 􀀀 is formulated as the product of 1) the propagation loss due to SIT and 2) the reflection coefficient of underlying seawater. The effect of surface roughness on 􀀀 is neglected when only considering signals of coherent reflection. In practice, 􀀀 at the specular point is first generated using TDS-1 data. Afterwards, SIT is calculated from TDS-1 􀀀 based on the proposed reflectivity model, and verified with two sets of reference SIT data; one is obtained by the Soil Moisture Ocean Salinity (SMOS) satellite, and the other is the combined SMOS/Soil Moisture Active Passive (SMAP) measurements. This analysis is performed on the data with sea ice thickness less than 1 m. Through comparison, good consistency between the derived TDS-1 SIT and the reference SIT is obtained, with a correlation coefficient (r) of 0.84 and a root-mean-square difference (RMSD) of 9.39 cm with SMOS, and an r of 0.67 and an RMSD of 9.49 cm with SMOS/SMAP, which demonstrates the applicability of the developed model and the utility of TDS-1 data for SIT estimation. In addition, this method is proved to be useful for improving existing sea ice detection accuracy.

Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this article, the global SM is estimated using machine learning (ML) regression aided by a preclassification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without preclassification is compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the preclassification strategy. Then, the optimal XGBoost predicted model with root-mean-square error (RMSE) of 0.052 cm ³ /cm ³ is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86 and an RMSE value of 0.056 cm ³ /cm ³ are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against in situ SM measurements from 301 stations. Similar low-median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the in situ networks is 0.049 cm ³ /cm ³ . The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this article reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data.