Synthetic Aperture Radar (SAR) data, characterized by high spatial resolution and all-weather observation capabilities, holds tremendous potential for application in soil moisture monitoring and crop growth assessment. Currently, the Water Cloud Model (WCM) is widely applied in SAR-based soil moisture inversion. However, the traditional WCM’s reliance on optical vegetation indices, which cannot be synchronized with SAR data, and the incomplete polarization scattering information contained in existing SAR vegetation indices, adversely effect the accuracy of SAR soil moisture retravel. Thus, this paper proposed a novel dual-polarization SAR vegetation index to soil moisture retravel in croplands. Firstly, the method establishes a Polarization Scattering Contrast Parameter (mcp) by combining SAR data covariance elements with backscattering information. Subsequently, based mcp, the Polarization Scattering Correlation Contrast parameter (Rcp) is defined, incorporating the degree of polarization, to integrate both scattering contrast and polarization state characteristics. Then, we proposed a novel dual-polarization SAR vegetation index (DRVIs) based Rcp. Finally, the surface soil moisture retravel in cropland area was realized using DRVIs and multiparameter WCM. This study conducted experimental in the winter wheat and summer corn covered areas of the Eastern Henan Plain, China. The experimental results indicate that during the wheat growing season, the correlation coefficient between the surface soil moisture products and estimated results reached 0.72. And during the corn growing season, the correlation coefficient was approximately 0.64. The research will contribute to expending the application of SAR data in vegetation growth monitoring and soil moisture retrieval.

Surface soil moisture (SSM) is an essential component of surface ecosystems. However, the existing microwave SSM retrieval methods are beleaguered with issues such as insufficient decoupling of terrestrial scattering characteristics and excessive reliance on empirical parameters within the models. This paper proposes a novel generalized SAR backscattering model (GSBM) for time-series retrieval of SSM, considering different land-cover influences. Initially, the GSBM is introduced, categorizing land cover into built-up areas, water bodies, vegetation-covered areas, and soils. Subsequently, we establish a unique SAR Water Cloud Model (SWCM) with the dual-polarization SAR vegetation index (DRVIs). A high-quality soil backscatter coefficient is obtained by employing the SWCM to eliminate vegetation's influence. Ultimately, the dry and wet reference values of soil backscatter are calculated to retrieve the relative SSM time series. Based on Sentinel-1 data, we select the Golmud area for a three-year spatiotemporal monitoring of SSM. Experimental results show that our method improves the correlation coefficients (r) between SAR backscatter and in situ soil moisture data from 0.63 to 0.73, improving about 16%. The r between SSM retrieval results and in situ data was above 0.66 at 500 m, 1 km and 3 km spatial resolutions. Consequently, the proposed method underscores the advantages of simplicity in parameters, high estimation precision, and robust adaptability, thereby augmenting the potential for large-scale global monitoring applications.

Synthetic aperture radar (SAR) is a crucial technique in Earth observation, providing vast amounts of data for monitoring the Earth’s surface. However, SAR’s side-looking imaging characteristics often result in significant geometric distortions in complex terrains such as mountainous gorges. Current methods struggle to accurately compute both active and passive geometric distortions when orbital state vector information is not available. This study aims to address this challenge by concentrating on the Southeastern Tibetan Plateau (SETP) and introducing a DEM-based radar incidence angle-tracking method (Angle-Tracking) based on ray tracing principles. The fundamental aspects of this method include constructing the discrete range direction vector (DRDV) to establish calculation directions, refining grid distribution via cubic-patch cells, and identifying potential topographic occluder points (PTOPs) to minimize redundancy in iterative computations. Through the utilization of this approach, we have acquired and disclosed the distribution of geometric distortion in ascending and descending Sentinel-1 data over the SETP region. Cross validation with results computed from precise orbital data showcases the efficacy of the angle-tracking method in identifying geometric distortions in the absence of satellite state vector information. Furthermore, the angle-tracking method demonstrates effectiveness when implemented on cloud computing platforms such as Google Earth Engine (GEE), thereby enhancing the feasibility of SAR-based research in mountainous areas.

Abstract Although there have been several studies of people's information behaviors during the COVID‐19 epidemic, the information practices of a specific group of people—those with depression—have been neglected. This study reports on qualitative interviews with 24 participants to explore the information practices of people living with depression during the pandemic lockdown. We use the theory of information worlds and the concept of transition to understand the phases of chaos, expansion, and contraction that the information worlds of this group present during the lockdown, and examine the interrelationship between the information worlds and the individual's transition experiences during specific periods. Our results show that, first, emotion, body, and embodiment play key roles in the individual's information worlds, while individuals' information practices are regulated by the economic environment, group norms, and other social circumstances. Second, government‐level information, the volunteer community, social media, and nonhuman objects further impact participants' information practices. We suggest that health management strategies need to have different priorities at different stages of the transition, and attention should be paid to the provision of emotional information support systems during the pandemic lockdown.

Forest fire not only seriously affects the stability of the forest ecosystem but also threatens the safety of human life and property. The previously burned areas extracting method mainly focuses on optical images, susceptible to cloud and fog objective environmental factors. Although there are related studies on the threshold segmentation of single SAR feature types, further information mining for SAR image data, e.g., backscattering intensity, polarization decomposition, texture, and other features, is still insufficient. Therefore, this letter proposes a burned area extracting method using polarization and texture features of Sentinel-1A images to combine and comprehensively mine various SAR feature change information caused by forest fires with a random forest (RF) model. For validation purposes, we compared the burned areas’ extracted results with the reference data acquired based on Sentinel-2A optical imagery. The comparative results show that the SAR extraction results highly agree with the reference data, with an accuracy of 87.12%, and the commission and omission errors were 20.44% and 12.88%, respectively. The proposed machine learning method helps extract fire areas covered by thick smoke or persistent clouds and provides a reference to related research.

Oblique photogrammetry with multiple cameras onboard unmanned aerial vehicle (UAV) has been widely applied in the construction of photorealistic three-dimensional (3-D) urban models, but how to obtain the optimal building facade texture images (BFTIs) from the abundant oblique images has been a challenging problem. This article presents an optimization method for selection of BFTIs from the image flows acquired by five oblique cameras onboard UAV. The proposed method uses multiobjective functions, which consists of the smallest occlusion of the BFTI and the largest façade texture area, to select the optimal BFTIs. Geometric correction, color equalization, and texture repairment are also considered for correction of BFTI's distortions, uneven color, and occlusion by other objects such as trees. Visual C++ and OpenGL under the Windows Operating System are used to implement the proposed methods and algorithms. The proposed method is verified using 49 800 oblique images collected by five cameras onboard the Matrice 600 Pro (M600 Pro) UAV system over Dongguan Street, in the City of Ji'nan, Shandong, China. To restore the partially occluded textures, different thresholds and different sizes of windows are experimented, and a template window of $200\times200$ pixels2is recommended. With the proposed method, 2740 BFTIs are extracted from 49 800 oblique images. As compared with the Pix4Dmapper and Smart 3-D method, it can be concluded that the optimal texture can be selected from the image flow acquired by multiple cameras onboard UAV and the approximately 95% memory occupied by the original BFTIs is reduced.

Recently, multi-attribute decision making (MADM) approaches concerning decision maker’s psychological behaviors have received increasing attention, but few of them have taken all the criteria interactions (positive, negative and dependent interactions) into consideration. In this paper, we combine the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method with the 2-additive fuzzy measure and Choquet integral theory to demonstrate how criteria interactions can be determined and further extend it into intuitionistic fuzzy environment. To begin with, we propose the novel score function and accuracy function to compare the difference among intuitionistic fuzzy sets, which have been proven to be more effective and rational than the existing measure functions. Next, we construct the nonlinear programming model based on maximum-entropy principal to obtain the optimal criteria interactions. Further, 2-additive Choquet-based dominance degree is defined whereby we put forward the 2-additive Choquet integral-based TODIM method under intuitionistic fuzzy environment to handle more challenging MADM problems. Finally, we present results of a didactic example, which concerns selection of suppliers for a manufacturing company, to evaluate the validity and rationality of proposed approach.