Accurate and up-to-date mapping of soil organic carbon density (SOCD) spatial distribution and temporal dynamics is essential for understanding terrestrial ecosystem carbon fluxes and monitoring global climate change....
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Accurate and up-to-date mapping of soil organic carbon density (SOCD) spatial distribution and temporal dynamics is essential for understanding terrestrial ecosystem carbon fluxes and monitoring global climate change. However, the available historical soil sample data remained insufficient to meet the high-precision spatiotemporal mapping requirements of SOCD across large regions. Therefore, we attempted to apply the Third Law of geography (also known as the Law of geographic Similarity) to address the issue of small sample size in modelling. In this study, we proposed a weighted multivariate similarity index and a similarity threshold index, along with the identification of optimal thresholds for measuring geographic similarity, to effectively increase the soil sample size. Based on the different input samples, we designed various modeling schemes for SOCD mapping. Our results suggest that the geographic similarity threshold-driven framework successfully reconciles the trade-off between sample quantity and quality, increasing sample sizes by up to three times while enhancing spatial representativeness and reducing prediction uncertainty. Accuracy evaluation and uncertainty analysis consistently demonstrated that models incorporating similarity-based input samples outperformed those relying solely on limited local samples. In comparison to the model utilizing only a limited data sample, the S1-1980 s model, achieved a coefficient of determination ( R 2 ) of 0.04 and a root mean square error ( R M S E ) of 2.47 Kg C m −2 . Conversely, the S3-1980 s model, based on similarity-expanded samples, demonstrated a significant improvement, achieving an R 2 of 0.64 and a R M S E of 1.36 Kg C m −2 . Consequently, the prediction using the improved model achieved accurate detection of regional spatiotemporal patterns of SOCD. This study provides a reference for addressing small sample size issues in time-series soil organic carbon mapping.
Arc array synthetic aperture radar (SAR) is a novel array imaging system for wide-area observation, with wide observation range and high resolution. Arc array synthetic aperture radar uses W-band as carrier signal, an...
ISBN:
(数字)9781728129129
ISBN:
(纸本)9781728129136
Arc array synthetic aperture radar (SAR) is a novel array imaging system for wide-area observation, with wide observation range and high resolution. Arc array synthetic aperture radar uses W-band as carrier signal, and W-band wavelength is short. The small high-frequency vibration of the helicopter-borne platform will cause significant changes in the phase of the echo signal, which will seriously deteriorate the imaging performance of arc array synthetic aperture radar. Based on the high order approximate imaging algorithm of arc array radar, this paper proposes a vibration phase error compensation imaging algorithm based on Short-time Fourier transform (STFT) parameter estimation. The algorithm uses the high-order approximation imaging algorithm of curved array radar to perform high-order approximation of the slant range model, compensates the range cell migration (RCM) and range azimuth coupling in the two-dimensional frequency domain, and finally realizes the vibration error compensation in the range Doppler domain. The simulation results of the lattice target verify the effectiveness of the imaging method.
The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive ...
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The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.
Gaofen-3 (GF-3) is the first Chinese multichannel synthetic aperture radar (SAR) sensor that can operate in the dual receive channel (DRC) mode. Different from the traditional single-channel SAR system, the multichann...
ISBN:
(数字)9781728129129
ISBN:
(纸本)9781728129136
Gaofen-3 (GF-3) is the first Chinese multichannel synthetic aperture radar (SAR) sensor that can operate in the dual receive channel (DRC) mode. Different from the traditional single-channel SAR system, the multichannel SAR system can overcome the inherent limitation to achieve high-resolution and wide-swath (HRWS) at the same time. However, the key challenge it faces is false target suppression. Especially for the moving vessels on the ocean, the existence of false targets will increase false alarm probability and affect the interpretation of SAR images. In this paper, the method of integration of detection, velocity estimation, location, and imaging for moving targets in the HRWS SAR system is proposed as well as applied to get an unambiguous image. The simulation and GF-3 real data experimental results show the validity of the method.
Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalabil...
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Sparse signal processing has been applied in synthetic aperture radar (SAR) imaging successfully. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The es...
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Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its ...
Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of imag...
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TOPSAR is an earth-imaging technique, which can provide wide swath coverage. The paper introduces a TOPSAR focusing and calibrating experiment based on the TOPSAR data acquired by Gaofen3(GF3). In this paper, we first...
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Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations only shows invariance to small local...
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