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Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors.

The reflection of Global Navigation Satellite System (GNSS) signals, known as GNSS reflectometry (GNSS-R), has made significant progress in monitoring ocean and land surface geophysical parameters. The main purpose of this investigation is to analyze the impact of antenna gain, land surface properties, and coherent integration time on the signal-to-noise ratio (SNR) of multi-GNSS reflected signals using complex waveform (CW) products generated by the Cyclone Global Navigation Satellite System (CYGNSS) constellation recording multi-GNSS raw intermediate frequency (IF) data. For different GNSS constellations, CYGNSS antenna gain of 6–9 dB can achieve an SNR of 0 dB or more over most pan-tropical regions. The normalized SNR (NSNR) calculated by the calibration procedure is used for such analysis. The results show that the coherent reflection caused the largest NSNR by the inland water body, and NSNR from the GPS reflected signal is about 2 dB and 4 dB larger than Galileo and BDS, respectively, whereas the dense vegetation caused a strong attenuation of the reflected signal and resulted in the smallest NSNR. Different GNSS systems have similar sensitivity to roughness coefficient (RC). In contrast, different GNSS constellations exhibit different sensitivities for moderate soil moisture (SM), the NSNR increases for GPS, BDS, and Galileo are estimated by linear fitting to be 4.85 dB, 7.67 dB, and 8.73 dB, respectively. NSNR gradually increases with above-ground biomass (AGB) when AGB is less than 100 Mg/ha, which seems to be the threshold for signals to penetrate the sparse vegetation and sense SM under the vegetation. However, when AGB is greater than 100 Mg/ha, the opposite is true. Furthermore, NSNR can be improved by longer coherent integration time, the average improvement in NSNR is about 1.5 dB with 2 ms coherent integration time and approximately 3 dB with 4 ms coherent integration time.

Liangjiecheng Huang, Weiqiang Li, Zikai Xu, Hongli Sun, Danfeng Ai, Yinfeng Hu, Shiqi Wang, Yu Li, Yanyan Zhou Department of Psychology, Ningbo University, Ningbo, Zhejiang, People’s Republic of ChinaCorrespondence: Yu Li; Yanyan Zhou, Department of Psychology, Ningbo University, No. 616 Fenghua Road, Ningbo, Zhejiang, 315211, People’s Republic of China, Email liyu@nbu.edu.cn; zhouyanyan@nbu.edu.cnBackground: Bystander intervention can protect victims from harm in cyberbullying. Previous studies have found that the severity of cyberbullying incidents is one of the important factors affecting decisions to intervene. However, little is known about the mediating and moderating mechanisms underlying this effect.Purpose: The current study explored the effect of the severity of cyberbullying incidents on bystander intention to intervene on social network sites (SNSs) among college students (Experiment 1), the mediating role of feelings of responsibility (Experiment 2) and the moderating role of empathy (Experiment 3).Patients and Methods: We presented cyberbullying incidents with different levels of severity through scenarios including fictive Weibo news reports and comments. Participants were exposed to a fictive cyberbullying incident and asked to complete a questionnaire including measures of the variables of interest.Results: Our results showed that the severity of incidents positively affected bystander intention to intervene through the mediation of feelings of responsibility. Empathy moderated the effect of incident severity on bystander intention to intervene.Conclusion: The results of the current study help to understand the behavior of bystanders in cyberbullying and they provide a practical reference for intervention in cyberbullying incidents.Keywords: cyberbullying, bystander, incident severity, intention to intervene, social network sites

In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with soil moisture active passive (SMAP) data and found a certain deviation in the CYGNSS “young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the “ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide a valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS.