Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is constructed by combining the X-Bragg model, the extended double Fresnel scattering model and the generalised volume scattering model (GVSM). Compared with traditional decomposition models, the proposed framework considers the depolarisation of dihedral scattering and the diverse vegetation contribution. Under the assumption that SM is invariant for the PolSAR observations at two different incidence angles and that vegetation scattering does not change between two consecutive measurements, analytical parameter solutions, including the dielectric constant of soil and crop stem, can be obtained by solving multivariable nonlinear equations. The proposed framework is applied to the time series of L-band uninhabited aerial vehicle synthetic aperture radar data acquired during the Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess retrieval performance by comparing the inversion results with in-situ measurements over bean, canola, corn, soybean, wheat and winter wheat areas and comparing the different performance of SM retrieval between the GVSM and Yamaguchi volume scattering models. Given that SM estimation is inherently influenced by crop phenology and empirical parameters which are introduced in the scattering models, we also investigate the influence of surface depolarisation angle and co-pol phase difference on SM estimation. Results show that the proposed retrieval framework provides an inversion accuracy of RMSE<6.0% and a correlation of R≥0.6 with an inversion rate larger than 90%. Over wheat and winter wheat fields, a correlation of 0.8 between SM estimates and measurements is observed when the surface scattering is dominant. Specifically, stem permittivity, which is retrieved synchronously with SM also shows a linear relationship with crop biomass and plant water content over bean, corn, soybean and wheat fields. We also find that a priori knowledge of surface depolarisation angle, co-pol phase difference and adaptive volume scattering could help to improve the performance of the proposed SM retrieval framework. However, the GVSM model is still not fully adaptive because the co-pol power ratio of volume scattering is potentially influenced by ground scattering.This work was supported by the National Natural Science Foundation of China [grant numbers 61971318, 41771377, 41901286, 42071295, 41901284, U2033216]; the China Postdoctoral Science Foundation [grant number 2018M642914]. This work was supported in part by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models

In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content generation and editing tool for various data modalities, making the study of their potential security risks very necessary and critical. Very recently, some pioneering works have shown the vulnerability of the diffusion model against backdoor attacks, calling for in-depth analysis and investigation of the security challenges of this popular and fundamental AI technique. In this paper, for the first time, we systematically explore the detectability of the poisoned noise input for the backdoored diffusion models, an important performance metric yet little explored in the existing works. Starting from the perspective of a defender, we first analyze the properties of the trigger pattern in the existing diffusion backdoor attacks, discovering the important role of distribution discrepancy in Trojan detection. Based on this finding, we propose a low-cost trigger detection mechanism that can effectively identify the poisoned input noise. We then take a further step to study the same problem from the attack side, proposing a backdoor attack strategy that can learn the unnoticeable trigger to evade our proposed detection scheme. Empirical evaluations across various diffusion models and datasets demonstrate the effectiveness of the proposed trigger detection and detection-evading attack strategy. For trigger detection, our distribution discrepancy-based solution can achieve a 100\% detection rate for the Trojan triggers used in the existing works. For evading trigger detection, our proposed stealthy trigger design approach performs end-to-end learning to make the distribution of poisoned noise input approach that of benign noise, enabling nearly 100\% detection pass rate with very high attack and benign performance for the backdoored diffusion models

Enhancement of stress tolerance in transgenic tobacco plants constitutively expressing AtIpk2β, an inositol polyphosphate 6-/3-kinase from Arabidopsis thaliana

Inositol phosphates (IPs) and their turnover products have been implicated to play important roles in stress signaling in eukaryotic cells. In higher plants genes encoding inositol polyphosphate kinases have been identified previously, but their physiological functions have not been fully resolved. Here we expressed Arabidopsis inositol polyphosphate 6-/3-kinase (AtIpk2β) in two heterologous systems, i.e. the yeast Saccharomycescerevisiae and in tobacco (Nicotiana tabacum), and tested the effect on abiotic stress tolerance. Expression of AtIpk2β rescued the salt-, osmotic- and temperature-sensitive growth defects of a yeast mutant strain (arg82Δ) that lacks inositol polyphosphate multikinase activity encoded by the ARG82/IPK2 gene. Transgenic tobacco plants constitutively expressing AtIpk2β under the control of the Cauliflower Mosaic Virus 35S promoter were generated and found to exhibit improved tolerance to diverse abiotic stresses when compared to wild type plants. Expression patterns of various stress responsive genes were enhanced, and the activities of anti-oxidative enzymes were elevated in transgenic plants, suggesting a possible involvement of AtIpk2β in plant stress responses

Contribution of Polarimetry and Multi-Incidence to Soil Moisture Estimation Over Agricultural Fields Based on Time Series of L-Band SAR Data

The alpha approximation method is known to be effective and simple for soil moisture retrieval from time series of synthetic aperture radar data. However, its accuracy is usually degraded by the scattering from vegetation, and it entails working with an underdetermined linear system when solving the unknown surface parameters. In this work, we study how the availability of fully polarimetric data and a diversity in incidence angles can help this method for soil moisture estimation. Results are obtained using data from the Soil Moisture Active Passive Validation Experiment 2012 campaign acquired by an air-borne L -band radar system. The assessment of the performance is based on in situ measurements over agricultural fields corresponding to five different crop types: bean, soybean, canola, corn, and wheat. The validation shows that, compared with the original method, the retrieval accuracy can be improved when the polarimetric decomposition is included in the approach. The combination of polarimetric decomposition and multi-incidence observations of enriched data provides the best performance, with a decrease in the final root-mean-square error between 0.4% and 5% with respect to single-pol and single-incidence data. Compared with HH, the results obtained for VV data present a higher accuracy for the overall crop types. The most noticeable improvement is achieved for corn, soybean and wheat, demonstrating the contribution of this extension of the original approach.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, in part by the State Agency of Research (AEI), in part by the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P, in part by the National Natural Science Foundation of China under Grant 61971318, Grant 41771377, Grant 41901286, and Grant 42071295, and in part by the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources under Grant 201905 and Grant 201906. The work of Hongtao Shi was supported by the China Scholarship Council (CSC) for 14 months study at the University of Alicante, Spain

AI VS. HUMANS: THE IMPACT OF DIFFERENT CONVERSATION AGENTS ON PRIVACY PERCEPTION AND PRIVACY DISCLOSURE

Developed by the artificial intelligence (AI) technology, AI conversation agents have been applied in more and more services in e-commerce contexts. Consumers presently interact with AI conversation agents and human agents for pre-purchase consulting service, which might influence their perceptions and attitudes toward privacy. Drawing upon communication privacy management theory, this study investigates how different conversation agents take effects on consumers’ perception of privacy concern and perceived benefits (informational support and emotional support), which in turn, influence consumers’ intention to disclose in e-commerce contexts. The expected scenario-based online survey will help to collect the data and analyze the research hypotheses. This study advances current theoretical knowledge in AI conversation agents and related privacy issues in e-commerce contexts. Also, we conclude the potential implications in practic

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient

Polarimetric Calibration and Quality Assessment of the GF-3 Satellite Images

The GaoFen-3 (GF-3) satellite is the first fully polarimetric synthetic aperture radar (SAR) satellite designed for civil use in China. The satellite operates in the C-band and has 12 imaging modes for various applications. Three fully polarimetric SAR (PolSAR) imaging modes are provided with a resolution of up to 8 m. Although polarimetric calibration (PolCAL) of the SAR system is periodically undertaken, there is still some residual distortion in the images. In order to assess the polarimetric accuracy of this satellite and improve the image quality, we analyzed the polarimetric distortion errors and performed a PolCAL experiment based on scattering properties and corner reflectors. The experiment indicates that the GF-3 images can meet the satellite’s polarimetric accuracy requirements, i.e., a channel imbalance of 0.5 dB in amplitude and ±10 degrees in phase and a crosstalk accuracy of −35 dB. However, some images still contain residual polarimetric distortion. The experiment also shows that the residual errors of the GF-3 standard images can be diminished after further PolCAL, with a channel imbalance of 0.26 dB in amplitude and ±0.2 degrees in phase and a crosstalk accuracy of −42 dB

Hydroalkylation of Unactivated Olefins with C(sp3)-H Com-pounds Enabled by NiH-Catalyzed Radical Relay

The hydroalkylation reaction of olefins with alkanes is a highly desirable synthetic transformation for the construction of C(sp3)-C(sp3) bonds. However, such transformation has proven to be challenging for unactivated olefins, particularly when the substrates are lack of directing groups or acidic C(sp3)-H bonds. Herein, we address this challenge by merging NiH-catalyzed radical relay strategy with a HAT (hydrogen atom transfer) process. In this catalytic system, a nucleophilic alkyl radical is generated from a C(sp3)-H compound in the presence of a HAT promotor, which couples with an alkyl metallic intermediate generated from the olefin substrate with a NiH catalyst to form the C(sp3)-C(sp3) bond. Notably, this approach represents the first case of transition-metal hydride-catalyzed hydroalkylation of unactivated olefins by employing C(sp3)-H compounds as the alkyl sources. Starting from easily available materials, the reaction not only demonstrates wide func-tional group compatibility but also provides hydroalkylation products with regiodivergency and excellent enantioselectivity through effective catalyst control under mild conditions

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32

A study on the willingness and influencing factors of novel coronavirus vaccination among medical personnel in North China

Aim To understand the awareness of the willingness to be vaccinated and influencing factors of the new coronavirus vaccine (neo-crown vaccine) among medical personnel in North China and to provide a theoretical basis and application guidelines for the feasibility of coronavirus vaccination by medical personnel to guide the public to actively be vaccinated by taking initiative and obtaining a coronavirus vaccination as soon as possible. Methods From April 2021 to June 2021, medical staff in North China were selected to complete an online questionnaire survey using Questionnaire Star to analyze the willingness rate to be vaccinated with the new coronavirus vaccine, and the influencing factors were analyzed using binary logistic regression. Results Among 621 respondents, 85.7% were willing to be vaccinated after the launch of the new vaccine. In the questionnaire, respondents were asked to answer questions such as “Do you think it is better to receive as few vaccines as possible at the same time?,” “If I get the new coronavirus vaccine, I may have serious side effects.,” “The new coronavirus vaccine is safe.,” “Specifically, for the new coronavirus vaccine, do you think it is safe?,” and “Specifically, for the new coronavirus vaccine, do you think it is easy to administer?.” These beliefs have an important influence on the vaccination of medical staff with the new coronavirus vaccine in Northern China (OR = 1.610,95% CI: 1.055 ~ 2.456; OR = 1.715,95% CI: 1.164 ~ 2.526; OR = 0.401, 95% CI: 0.212 ~ 0.760; OR = 0.352,95% CI: 0.147 ~ 0.843; OR = 3.688,95% CI: 1.281 ~ 10.502, respectively; All P values < .05). Conclusions Medical staff have a high willingness to be vaccinated with the new coronavirus vaccine, which plays a positive role in the publicity of the vaccine