Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S leaf area index products in maize crops

Altres ajuts: this research was funded by the Copernicus Global Land Service (CGLOPS-1, 199494-JRC).We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S

A Dual-Band Printed End-Fire Antenna with DSPSL Feeding

A novel dual-band printed end-fired antenna with double-sided parallel-strip line (DSPSL) feeding is presented. The DSPSL acts in wideband transition using balanced transmission. Two different modes of the parasitic patches allow the antenna to work in different bands. The printed antenna is designed as a quasi-Yagi structure to achieve directivity in the lower band, and the parallel rectangular patches serve as the parasitic director. These patches act as radiation patches with end-fire direction characteristics in the upper band. The measured bandwidths were 18.3% for the lower frequency band (2.28–2.74 GHz) and 12.6% for the upper frequency band (5.46–6.2 GHz)

Predictors of everyday functional impairment in older patients with schizophrenia: A cross-sectional study

ObjectiveThis study investigates the prevalence of everyday functional impairment among older adults with schizophrenia and builds a predictive model of functional decline.MethodsA total of 113 hospitalized older patients enrolled in this study. Functional impairment is defined according to the Functional Activities Questionnaire (FAQ). Patients who scored <9 could function independently daily, while those who scored ≥9 had problems in everyday functional activities. Data collected include sociodemographic characteristics, depressive symptoms, social support, and physical comorbidities, which were classified according to the eight anatomical systems of the human body.ResultsThe sample comprised 75% female participants with a mean age of 63.74 ± 7.42 years old. A total of 33.6% had a functional impairment, while cognitive impairment was present in 63.7%. Independent participants had better urinary system and respiratory system health (P < 0.05). After adjusting for the potential confounders of age, disease course, physical comorbidities, psychiatric symptoms, the ability to independently carry out daily activities, and cognitive function, we found that impaired everyday function is associated with poor cognition, depressive symptoms, first admission, psychiatric symptoms (especially positive symptoms), ADL, and respiratory and urinary system diseases.ConclusionEveryday functional capacity is predicted by disease course, admission time, cognition, depressive symptoms, severity of psychosis, ability to carry out daily activities, and respiratory and urinary system health status. Urinary system diseases contribute significantly to the prediction of impaired function. Future studies should focus on health status, drug use, and everyday functional recovery in older patients with schizophrenia

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images

Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success in remote sensing image building change detection, it is still fraught with the problem of generating broken detection boundaries and separation of dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization network for building change detection. The main contribution of the proposed network is that it performs change detection by respectively modeling the main body and edge features of buildings, which is based on the characteristics that the similarity between the main body pixels is strong but weak between the edge pixels. Firstly, we employ a siamese ResNet structure to extract dual-temporal multi-scale difference features on the original remote sensing images. Subsequently, a flow field is built to separate the main body and edge features. Thereafter, a feature optimization module is designed to refine the main body and edge features using the main body and edge ground truth. Finally, we reorganize the optimized main body and edge features to obtain the output results. These constitute a complete end-to-end building change detection framework. The publicly available building dataset LEVIR-CD is employed to evaluate the change detection performance of our network. The experimental results show that the proposed method can accurately identify the boundaries of changed buildings, and obtain better results compared with the current state-of-the-art methods based on the U-Net structure or by combining spatial-temporal attention mechanisms

Self-Supervised Keypoint Detection and Cross-Fusion Matching Networks for Multimodal Remote Sensing Image Registration

Remote sensing image matching is the basis upon which to obtain integrated observations and complementary information representation of the same scene from multiple source sensors, which is a prerequisite for remote sensing tasks such as remote sensing image fusion and change detection. However, the intricate geometric and radiometric differences between the multimodal images render the registration quite challenging. Although multimodal remote sensing image matching methods have been developed in recent decades, most classical and deep learning based techniques cannot effectively extract high repeatable keypoints and discriminative descriptors for multimodal images. Therefore, we propose a two-step “detection + matching” framework in this paper, where each step consists of a deep neural network. A self-supervised detection network is first designed to generate similar keypoint feature maps between multimodal images, which is used to detect highly repeatable keypoints. We then propose a cross-fusion matching network, which aims to exploit global optimization and fusion information for cross-modal feature descriptors and matching. The experiments show that the proposed method has superior feature detection and matching performance compared with current state-of-the-art methods. Specifically, the keypoint repetition rate of the detection network and the NN mAP of the matching network are 0.435 and 0.712 on test datasets, respectively. The proposed whole pipeline framework is evaluated, which achieves an average M.S. and RMSE of 0.298 and 3.41, respectively. This provides a novel solution for the joint use of multimodal remote sensing images for observation and localization

Spatial multi-games under myopic update rule

Considering the population diversity and the limitation of individual information in repeated N-person games, we study a spatial multi-games model under the myopic rule in this paper, in which two distinct types of players participate in snowdrift game (SG) and prisoner’s dilemma game (PDG), respectively. Monte Carlo simulation method is used to study: the effects of game intensity parameters b and $$\delta$$, noise parameter k and mixing ratio p on the frequency of cooperators; the difference between learning update rule and myopic update rule. The results demonstrate that: (1) when the values of b and $$\delta$$ are small, noise parameter k can promote the emergence of cooperation in SG with myopic update rule; (2) different from learning mechanism, the effect of the parameters p on the frequency of cooperators is nonmonotonic under myopic mechanism; (3) cooperators can form clusters to resist the invasion of defectors under learning update rule, while cooperators and defectors tend to form the chessboard-like patterns to increase individual payoff under myopic update rule