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BackgroundMany app-based interventions targeting women with breast cancer have been developed and tested for effectiveness. However, information regarding the evaluation of the usage of these interventions is scarce. A better understanding of usage data is important to determine how women use apps and how these interventions affect health outcomes. ObjectiveThis study aimed to examine the usage duration and login frequency of an app-based intervention, the Breast Cancer e-Support (BCS) program, and to investigate the association between usage data and participants’ demographic and medical characteristics. MethodsThis study is a secondary data analysis of a randomized controlled trial assessing the effectiveness of the BCS program. The BCS program contains four modules: Learning Forum, Discussion Forum, Ask-the-Expert Forum, and Your Story Forum. A total of 57 women in the intervention group accessed the BCS program during their 12-week chemotherapy. The app’s background system tracked the usage duration and login frequency for each forum and the entire BCS program. ResultsThe total usage duration per participant ranged from 0 to 9371 minutes, and the login frequency per participant ranged from 0 to 774 times. The Discussion Forum and the Learning Forum were the most frequently used modules. The general linear model showed that age, education, family monthly income, and employment were associated with BCS usage duration and/or login frequency. Age (F1,45=10.09, P=.003, B=115.34, 95% CI 42.22-188.47) and education level (F1,45=7.22, P=.01, B=1949.63, 95% CI 487.76-3411.50) were positively associated with the usage duration of the entire BCS program. Family monthly income was positively associated with the usage duration of the Learning Forum (F1,45=11.85, P=.001, B=1488.55, 95% CI 617.58-2359.51) and the login frequency of the entire BCS program (F1,45=4.47, P=.04, B=113.68, 95% CI 5.33-222.03). Employment was negatively associated with the usage duration of the Ask-the-expert Forum (F1,45=4.50, P=.04, B=–971.87, 95% CI –1894.66 to –49.07) and the Your Story Forum (F1,45=5.36, P=.03, B=–640.71, 95% CI –1198.30 to –83.11) and positively associated with the login frequency of the entire BCS program (F1,45=10.86, P=.002, B=192.88, 95% CI 75.01-310.74). No statistical differences were found between BCS usage data and cancer stage, BMI, comorbidity, types of surgery, or cycles of chemotherapy. ConclusionsOverall, this study found considerable variability in the usage of app-based interventions. When health care professionals incorporate app-based interventions into their routine care for women with breast cancer, the learning and discussion functions of apps should be strengthened to promote engagement. Additionally, characteristics of women with breast cancer, such as age, level of education, income, and employment status, should be taken in consideration to develop tailored apps that address their particular needs and therefore improve their engagement with the app. Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12616000639426; http://www.ANZCTR.org.au/ACTRN12616000639426.aspx

In this paper, we will conclude the results of Bufeng-1 (BF-1) A/B data processing, calibration workflow, and validation of the calibrated sea surface winds, land surface soil moisture, and sea surface height measurements. Since 2019, the BF-1 mission has operated in-orbit for over 4 years. The Earth reflected delay Doppler maps (DDMs) are continuously collected to perform global sea surface and land observations. At the same time, the intermediate frequency (IF) raw data are also obtained for 12 seconds every pass in diagnostic mode. To begin with, a brief description of the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technique will be provided in the introduction. Next, we will present the overview of Chinese BF-1 mission and the data specifications used in our research. In the next section, the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications. Then, the status of Chinese Beidou System (BDS) Equivalent Isotropic Radiated Power (EIRP) acquisition programme is then introduced. Furthermore, the latest sea surface height (SSH) measurements results including two modes (group delay and carrier phase) and wind speed derivation based on machine learning (ML) method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University (DTU) mean sea surface (MSS) products and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications. Finally, the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works. [ABSTRACT FROM AUTHOR]

The performance of a semantic segmentation model for remote sensing (RS) images pre-trained on an annotated dataset greatly decreases when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods face two problems when performing RS image translation: (1) ignoring the scale discrepancy between two RS datasets, which greatly affects the accuracy performance of scale-invariant objects; (2) ignoring the characteristic of real-to-real translation of RS images, which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS image translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDualGAN. At the end of the paper, a thorough discussion is conducted to provide a reasonable explanation for the improvement of ResiDualGAN. Our source code is also available. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) is an approach to minimizing the domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images, which improves the performance of the downstream tasks, for example, cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this letter, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL) and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks. Source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG. [ABSTRACT FROM AUTHOR]

Suspended sediment concentration (SSC) is an important indicator of water quality that affects the biological processes of river ecosystems and the evolution of floodplains and river channels. The in situ SSC measurements are costly, laborious and spatially discontinuous, while the spaceborne SSC overcome these drawbacks and becomes an effective supplement for in situ observation. However, the spaceborne SSC observations of rivers are more challenging than those of lakes and reservoirs due to their narrow widths and the broad range of SSCs, among other factors. We developed a novel SSC retrieval method that is suitable for the rivers. Water was classified as clear or turbid based on the Forel–Ule index, and optimal SSC models were constructed based on the spectral responses to SSCs in cases of different turbidity. The estimated SSC had a strong correspondence with in situ measurements, with a root mean squared error (RMSE) of 24.87 mg/L and a mean relative error (MRE) of 51.91%. Satellite-derived SSC showed good consistency with SSCs obtained from gauging stations (r2 > 0.79). We studied the spatiotemporal variation in SSC in the Yangtze main stream from 2017 to 2021. It increased considerably from May to October each year, with the peak generally occurring in July or August (ca. 200–300 mg/L in a normal year and 800–1000 mg/L in a flood year), while it remained stable and decreased to around 50 mg/L from November to April of the following year. It was high in the east and low in the west, with local maxima in Chongqing (ca. 80–150 mg/L) and in the lower Dongting Lake reaches (ca. 80–100 mg/L) and a local minima in the downstream of the Three Gorges Dam (ca. 1–20 mg/L). Case studies in the Yibin reach and Three Gorges Reservoir determined that local variation in SSCs is due to special hydrodynamic conditions and anthropogenic activities. The procedure applied to process Sentinel-2 imagery and the novel SSC retrieval method we developed supplement the deficiencies in river SSC retrieval. [ABSTRACT FROM AUTHOR]

Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g., the depth code) derived from deep neural networks has been employed in the visual-only or visual-inertial simultaneous localization and mapping (SLAM) systems, which achieve promising performances on both camera motion and local dense geometry estimations from monocular images. However, the existing visual-inertial SLAM systems combined with depth codes are either built on a filter-based SLAM framework, which can only update poses and maps in a relatively small local time window, or based on a loosely-coupled framework, while the prior geometric constraints from the depth estimation network have not been employed for boosting the state estimation. To well address these drawbacks, we propose DiT-SLAM, a novel real-time Dense visual-inertial SLAM with implicit depth representation and Tightly-coupled graph optimization. Most importantly, the poses, sparse maps, and low-dimensional depth codes are optimized with the tightly-coupled graph by considering the visual, inertial, and depth residuals simultaneously. Meanwhile, we propose a light-weight monocular depth estimation and completion network, which is combined with attention mechanisms and the conditional variational auto-encoder (CVAE) to predict the uncertainty-aware dense depth maps from more low-dimensional codes. Furthermore, a robust point sampling strategy introducing the spatial distribution of 2D feature points is also proposed to provide geometric constraints in the tightly-coupled optimization, especially for textureless or featureless cases in indoor environments. We evaluate our system on open benchmarks. The proposed methods achieve better performances on both the dense depth estimation and the trajectory estimation compared to the baseline and other systems. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a critical problem for many remote sensing (RS) image applications. Benefiting from large-scale pixel-level labeled data and the continuous evolution of deep neural network architectures, the performance of semantic segmentation approaches has been constantly improved. However, deploying a well-trained model on unseen and diverse testing environments remains a major challenge: a large gap between data distributions in train and test domains results in severe performance loss, while manual dense labeling is costly and not scalable. To this end, we proposed an unsupervised domain adaptation framework for RS image semantic segmentation that is both practical and effective. The framework is supported by the consistency principle, including the cycle consistency in the input space and self-supervised consistency in the training stage. Specifically, we introduce cycle-consistent generative adversarial networks to reduce the discrepancy between source and target distributions by translating one into the other. The translated source data then drive a pipeline of supervised semantic segmentation model training. We enforce consistency of model predictions across target image transformations in order to provide self-supervision for the unlabeled target data. Experiments and extensive ablation studies demonstrate the effectiveness of the proposed approach on two challenging benchmarks, on which we achieve up to 9.95% and 7.53% improvements in accuracy over the state-of-the-art methods, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel change detection method for very-high-resolution images based on deep learning. In the method, an ensemble CNN change detection framework is proposed. Different from other deep learning change detection methods, samples of changed and unchanged regions of two very-high-resolution images acquired at different times are fed into two CNN. The discriminative deep metric learning based on dissimilarity degree is used to adjust discriminative distance metric of two CNN output layers quantitatively, under which the distance of unchanged samples becomes smaller and that of changed samples becomes higher, respectively. During its training procedure, cost module function based on dissimilarity degree of samples is used to train the ensemble CNN and high-level and abstract features of changed and unchanged pair of samples are driven to learn by the proposed framework. After training, the discriminative distance of unchanged samples becomes smaller and that of changed samples becomes larger. The proposed method justifies the changed and unchanged area of original images and change detection results can be obtained. Experiments on real datasets and theoretical analysis validate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The reconstruction of an existing railway is important for railway reformation or double-track design. Obtaining the curve parameters of the railway and the location of the main stake accurately and rapidly is the key issue for existing railway reconstruction. A new method based on point cloud data is proposed in this paper. The issue of reconstruction was transformed into an optimization problem by constructing the objective function and introducing the constraint. With consideration of the slope of the curves' chord, the robust local weighted moving average method was used for de-noising. The time complexity was reduced greatly after separating the curve unit. The proposed method can obtain the coordinates of the main stake and the parameters of the railway by particle swarm optimization using a full direction search, combining the design requirements and geometric relations of the railway. Finally, some experiments on the design data and measured data were conducted to verify the validity of the proposed method. The results also show that the proposed method is very effective and useful for existing railway reconstruction. [ABSTRACT FROM AUTHOR]

The feasibility of using China’s Beidou Navigation Satellite System (BDS) signal-to-noise ratio (SNR) data for estimating soil moisture is demonstrated in this letter. Previous studies for the Global Navigation Satellite System (GNSS) multipath for estimating soil moisture have concentrated on the Global Positioning System (GPS) SNR data. The SNR data recorded by BDS receivers can also be strongly impacted by the relative permittivity, which however, has not been comprehensively investigated. This letter used a commercially available geodetic-quality BDS/GPS receiver to collect BDS SNR data on both B1 and B2 frequencies. Two methods were investigated to demonstrate the relationship between BDS SNR metrics and soil moisture. One was the phase method proposed by Larson et al., and the other was an interference model proposed by Peng et al. The results show that both B1 and B2 frequencies perform well to reflect the moisture fluctuations, particularly for reflecting big precipitation events. Comparisons between BDS B1 and B2 with GPS L2C and L5 demonstrate the results from these two constellations are comparable. BDS could be a new data source for producing global high-temporal soil moisture products using GNSS-based approaches. [ABSTRACT FROM PUBLISHER]

Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture. [ABSTRACT FROM AUTHOR]

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha). [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK's TechdemoSat-1 (TDS-1) and the US's Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs. R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m−3, and ubRMSE = 0.060 m3m−3 for TDS-1; R = 0.798, MAE = 0.040 m3m−3, and ubRMSE = 0.062 m3m−3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m−3, and ubRMSE = 0.056 m3m−3 for TDS-1; R = 0.724, MAE = 0.052 m3m−3, and ubRMSE = 0.053 m3m−3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision. [ABSTRACT FROM AUTHOR]

Hydrological models are usually calibrated against observed streamflow (Qobs), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ETRS) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ETRS in the hydrological calibration of a widely used land surface model coupled with a source–sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Qobs and ETRS-based model calibrations, respectively, through comprehensive sensitivity tests. The ETRS-based model calibration results in a mean Kling–Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Qobs, the ETRS calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ETRS calibration runs. This study confirms that with reasonable precipitation input, the ETRS-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ETRS for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ETRS observations continue to improve. [ABSTRACT FROM AUTHOR]

In the original article publication, the affiliation of co-author Yang Hong was accidentally omitted from the manuscript. The missing affiliation is given. [ABSTRACT FROM AUTHOR]

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications. [ABSTRACT FROM AUTHOR]

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSS) techniques, such as GPS, can be used to accurately record vertical crustal movements induced by seasonal terrestrial water storage (TWS) variations. Conversely, the TWS data could be inverted from GPS-observed vertical displacement based on the well-known elastic loading theory through the Tikhonov regularization (TR) or the Helmert variance component estimation (HVCE). To complement a potential non-uniform spatial distribution of GPS sites and to improve the quality of inversion procedure, herein we proposed in this study a novel approach for the TWS inversion by jointly supplementing GPS vertical crustal displacements with minimum usage of external TWS-derived displacements serving as pseudo GPS sites, such as from satellite gravimetry (e.g., Gravity Recovery and Climate Experiment, GRACE) or from hydrological models (e.g., Global Land Data Assimilation System, GLDAS), to constrain the inversion. In addition, Akaike's Bayesian Information Criterion (ABIC) was employed during the inversion, while comparing with TR and HVCE to demonstrate the feasibility of our approach. Despite the deterioration of the model fitness, our results revealed that the introduction of GRACE or GLDAS data as constraints during the joint inversion effectively reduced the uncertainty and bias by 42% and 41% on average, respectively, with significant improvements in the spatial boundary of our study area. In general, the ABIC with GRACE or GLDAS data constraints displayed an optimal performance in terms of model fitness and inversion performance, compared to those of other GPS-inferred TWS methodologies reported in published studies. [ABSTRACT FROM AUTHOR]

Spaceborne global navigation satellite system reflectometry (GNSS-R) data collected by the UK TechDemoSat-1 (TDS-1) satellite is applied to retrieve global lake levels for the first time. Lake levels of 351 global lakes (area greater than 500 km2 and elevation lower than 3000 m each) are estimated using TDS-1 Level 1b data over 2015–2017. Strong correlations (overall R2 greater than 0.95) are observed among lake levels derived from TDS-1 and other altimetry satellites such as CryoSat-2, Jason, and Envisat (the latter two are collected by Hydroweb), although with large root-mean-square error (RMSE) (tens of meters) mainly due to the fact that TDS-1 is not dedicated for altimetry measuring purpose. Examples of the Caspian Sea and the Poyang Lake show consistent spatial and temporal variations between TDS-1 and other data sources. The results in this paper provide supportive information for further application of GNSS-R constellations to measure altimetry of inland water bodies. [ABSTRACT FROM AUTHOR]

NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, is a small satellite constellation designed to measure the ocean surface wind speed in hurricanes and tropical cyclones. To explore its additional capabilities for applications on the land surface, this study investigated the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017. The results showed that despite the lack of quantitative evaluation, the CYGNSS-derived surface reflectivity (SR) and flood inundation area was qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and Soil Moisture Active Passive (SMAP)/Soil Moisture and Ocean Salinity (SMOS)-derived total brightness temperature at circular polarization ( T b C ). The results provide supporting evidence for further designation of Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) constellations to monitor land surface hydrology. [ABSTRACT FROM AUTHOR]

GNSS-R (Global Navigation Satellite System-Reflectometry) has been demonstrated to be a new and powerful tool to sense soil moisture in recent years. Multi-antenna pattern and single-antenna pattern have been proposed regarding how to receive and process reflected signals. Great efforts have been made concerning ground-based and air-borne observations. Meanwhile, a number of satellite-based missions have also been implemented. For the in-depth study of soil moisture remote sensing by the technique of GNSS-R, regardless of the extraction methods of the reflected signals or the types of the observation platform, three key issues have to be determined: The specular reflection point, the spatial resolution and the detection depth in the soil. However, in current literatures, there are no comprehensive explanations of the above three key issues. This paper conducts theoretical analysis and formula derivation, aiming to systematically and quantitatively determine the extent of soil moisture being detected in three dimensions from the above-mentioned aspects. To further explain how the three factors behave in the specific application, the results of two application scenarios are shown: (1) a ground-based GPS measurement in Marshall, Colorado, US from the Plate Boundary Observatory, corresponding to single-antenna pattern. The relative location of the specular reflection points, the average area of the First Fresnel Ellipse Clusters and the sensing depth of the time-series soil moisture are analyzed, and (2) an aviation experiment conducted in Zhengzhou to retrieve soil moisture content, corresponding to the multi-antenna pattern. The spatial distribution of soil moisture estimation with a certain resolution based on the flight tracks and the relevant sensing depth are manifested. For remote sensing using GNSS reflected signals, BeiDou is different from GPS mainly in the carrier frequency. Therefore, the results of this study can provide references for China’s future development of the BeiDou-R technique. [ABSTRACT FROM AUTHOR]

The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial components. The method analyzes the normal vector distribution matrix to segment feature points from a 3D point cloud. The point cloud is then divided into different patches by applying the region growing method on the basis of the distance constraint and according to the initial results. Subsequently, each patch is fitted with an implicit expression equation, and the proposed method is combined with the random sample consensus (RANSAC) algorithm and parameter fitting to extract and optimize the feature surface. The proposed method is experimentally validated on three industrial components. The threshold setting in the algorithm is discussed in terms of algorithm principles and model features. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components. [ABSTRACT FROM AUTHOR]

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer's application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions., Competing Interests: The authors declare no conflict of interest.

Wi-Fi indoor positioning algorithms experience large positioning error and low stability when continuously positioning terminals that are on the move. This paper proposes a novel indoor continuous positioning algorithm that is on the move, fusing sensors and Wi-Fi on smartphones. The main innovative points include an improved Wi-Fi positioning algorithm and a novel positioning fusion algorithm named the Trust Chain Positioning Fusion (TCPF) algorithm. The improved Wi-Fi positioning algorithm was designed based on the properties of Wi-Fi signals on the move, which are found in a novel "quasi-dynamic" Wi-Fi signal experiment. The TCPF algorithm is proposed to realize the "process-level" fusion of Wi-Fi and Pedestrians Dead Reckoning (PDR) positioning, including three parts: trusted point determination, trust state and positioning fusion algorithm. An experiment is carried out for verification in a typical indoor environment, and the average positioning error on the move is 1.36 m, a decrease of 28.8% compared to an existing algorithm. The results show that the proposed algorithm can effectively reduce the influence caused by the unstable Wi-Fi signals, and improve the accuracy and stability of indoor continuous positioning on the move.