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29 results on '"Bian, Zunjian"'

1. Stripe Noise Elimination with a Novel Trend Repair Method for Push-Broom Thermal Images.

Author

Zhang, Zelin, Li, Hua, Du, Yongming, Chen, Yao, Zhao, Guoxiang, Bian, Zunjian, Cao, Biao, Xiao, Qing, and Liu, Qinhuo

Subjects
THERMOGRAPHY, REMOTE sensing, SIGNAL-to-noise ratio, LANDSAT satellites, COLUMNS, PIXELS
Abstract

Stripe noise is a general phenomenon in original remote sensing images that both degrades image quality and severely limits its quantitative application. While the classical statistical method is effective in correcting common stripes caused by inaccurately calibrating relative gains and offsets between detectors, it falls short in correcting other nonlinear stripe noises originating from subtle nonlinear changes or random contamination within the same detector. Therefore, this paper proposes a novel trend repair method based on two normal columns directly adjacent to a defective column to rectify the trend by considering the geospatial structure of contaminated pixels, eliminating residual stripe noise evident in level 0 (L0) remote sensing images after histogram matching. GF5-02 VIMI (Gaofen5-02, visual and infrared multispectral imager) images and simulated Landsat 8 thermal infrared sensor (TIRS) images deliberately infused with stripe noise are selected to test the new method and two other existing methods, the piece-wise method and the iterated weighted least squares (WLS) method. The effectiveness of these three methods is reflected by streaking metrics (Streaking), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and improvement factor (IF) on the uniformity, structure, and information content of the corrected GF5-02 VIMI images and by the accuracy of the corrected simulated Landsat 8 TIRS images. The experimental results indicate that the trend repair method proposed in this paper removes nonlinear stripe noise effectively, making the results of IF > 20. The remaining indicators also show satisfactory results; in particular, the mean accuracy derived from the simulated image remains below a digital number (DN) of 15, which is far superior to the other two methods. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime.

Author

Na, Qiang, Cao, Biao, Qin, Boxiong, Mo, Fan, Zheng, Limeng, Du, Yongming, Li, Hua, Bian, Zunjian, Xiao, Qing, and Liu, Qinhuo

Subjects
LAND surface temperature, HEAT radiation & absorption, REMOTE sensing
Abstract

Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains. [ABSTRACT FROM AUTHOR]

Published
2024
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5. An Analytical Thermal Anisotropy Model for the Urban Canopy Over Sloping Terrain

Author

Sang, Xinguang, Luo, Xiaobo, Cao, Biao, Bian, Zunjian, Fan, Tengyuan, and Mu, Shilin

Abstract

Land surface temperature (LST) is an important parameter in many research fields. As a temperature characteristic, thermal radiation has an obvious directionality. In urban areas, the directional anisotropy (DA) in surface temperatures can reach 10 K during airborne measurements, limiting the applicability of urban surface temperature products. Notably, the intricate 3-D structures and heterogeneous temperature distributions in urban environments significantly influence this anisotropy. In recent decades, numerous models have been developed for urban thermal anisotropy (UTA) analysis; however, most of these models predominantly focus on horizontal surfaces, with little consideration of mountainous architectures. To balance the calculation efficiency and model complexity, this study introduces an analytical thermal anisotropy model applicable to urban areas. This model considers the effects of 3-D structures and slopes and is labeled as the AU3SM. The multiangle data used for the analysis are recorded by an unmanned aerial vehicle (UAV) with a multicircle observation scheme in Chongqing, China. Comparisons of the AU3SM simulated data with these measurement data yield root mean square error (RMSE) and coefficient of determination ( $R^{2}$ ) values of 0.57 K and 0.89, respectively. Furthermore, similar comparisons with the discrete anisotropic radiative transfer (DART) model yield $R^{2}$ and RMSE values of 0.98 and 0.12 K, respectively. Simulations reveal that slope influences the UTA, and the hotspot lies on the opposite side of the solar direction; ignoring slope values of 5°, 10°, 15°, and 25° results in UTA maximum biases of approximately 1.2, 3.5, 7, and 7.5 K, respectively, under certain conditions. These findings demonstrate that the proposed model can accomplish rapid UTA assessments in mountainous areas.

Published
2024
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6. Comprehensive Analysis of Current 1-km Land Surface Temperature Products in Sparsely Vegetated Area: T-Based Evaluation, Thermal Anisotropy, and Joint Application

Author

Na, Qiang, Li, Hua, Cao, Biao, Qin, Boxiong, Zheng, Limeng, Bian, Zunjian, Du, Yongming, Xiao, Qing, and Liu, Qinhuo

Abstract

Land surface temperature (LST) is an essential parameter for geoscience studies, and the 1-km polar-orbiting satellite LST products are widely used due to their global coverage on a daily basis. Aimed to jointly utilize all 1-km LST products to thoroughly quantify the LST temporal tendency within one day, comprehensive evaluation of existing 1-km LST products over the same sites is meaningful. In this study, taking sparsely vegetated sites as an example, ten 1-km polar-orbiting LST products were evaluated against the in situ measurements of ten USCRN sites with high spatial representativeness (from December 2019 to November 2020 covering four seasons). The evaluated LST products included three Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (i.e., MOD11, MYD11, and MYD21), two Sea and LST Radiometer (SLSTR) LST products (i.e., Sentinel-3A and Sentinel-3B), one LST product from Advanced Very High Resolution Radiometer (AVHRR) onboard Metop-B, three Visible Infrared Imaging Radiometer Suite (VIIRS) LST products (i.e., VNP21, S-NPP EDR, and NOAA-20 EDR), and one LST product from Visible and Infra-Red Radiometer (VIRR) onboard FY-3B. The results indicate that: 1) the RMSE is much higher in the daytime (2.5–4.0 K) than in the nighttime (1.7–2.5 K); 2) the daytime MBE is negative (from −0.3 to −2.8 K) and this underestimate trend is significantly slighter for nighttime MBE (from −1.7 to 0.2 K); and 3) the daytime RMSE varies from 2.7 to 4.6 K in summer, 2.7–4.5 K in spring, 2.5–3.7 K in winter, and 2.0–3.4 K in autumn. Furthermore, we found that the severe daytime RMSE is related to the thermal anisotropy amplitude, which is around 4 K in summer, 2 K in spring and autumn, and 1 K in winter. Correcting the thermal anisotropy of daytime LST is an essential step that needs to be taken seriously before conducting high-quality joint applications, such as diurnal temperature cycle (DTC) modeling.

Published
2024
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27. Evaluation of Four Kernel-Driven Models in the Thermal Infrared Band.

Author

Cao, Biao, Gastellu-Etchegorry, Jean-Philippe, Du, Yongming, Li, Hua, Bian, Zunjian, Hu, Tian, Fan, Wenjie, Xiao, Qing, and Liu, Qinhuo

Subjects
LAND surface temperature, TEMPERATURE distribution, BRIGHTNESS temperature, PHOTOSYNTHETICALLY active radiation (PAR)
Abstract

Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and RL model, gave the best fitting results for all the scenes. The $R^{2}$ of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions. [ABSTRACT FROM AUTHOR]

Published
2019
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