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  • Chen, Hao  (12)
  • Geography  (12)
  • 1
    In: International Journal of Climatology, Wiley, Vol. 40, No. 13 ( 2020-11-15), p. 5783-5800
    Abstract: The surface energy balance is a key issue in land surface process research and important for studies of climate and hydrology. In this paper, the surface energy fluxes (net radiation, ground heat flux, sensible heat flux and latent heat flux) at the Tanggula (TGL) and Xidatan (XDT) sites were measured and the distributions of the regional surface energy fluxes on the Tibetan Plateau were obtained using a revised surface energy balance system (SEBS) model. The results show that the surface energy fluxes have obvious seasonal variations. At both sites, the sensible heat flux is highest in spring and lowest in summer, and the latent heat flux is highest in summer and lowest in winter. The high elevation, snow cover, freeze–thaw process, precipitation, vegetation and soil texture are important influencing factors for land surface energy fluxes. The time‐phase difference between the net radiation and ground heat flux for bare soils is estimated to be 2–3 hr. The ratio of ground heat flux and net radiation ranged from approximately 0.18 to 0.33, and a parameterization scheme for the remote sensing of ground heat flux over the Tibetan Plateau bare soil in summer is developed. The simulation results of the regional surface energy fluxes show that the distributions of surface parameters, such as vegetation, soil texture and soil moisture content, are important for understanding regional changes in the surface energy fluxes.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 2
    In: International Journal of Climatology, Wiley, Vol. 41, No. 15 ( 2021-12), p. 6768-6784
    Abstract: Uneven and sparse surface air temperature ( T a ) observations challenge the understanding of the atmosphere‐land interactions and global environmental changes. Lapse rate () is thought of with great potential to obtain accurate long‐term and high‐resolution T a dataset. However, previous studies on over China were solely based on observed data, which limited our understanding since few stations are distributed in western China. In this study, an improved procedure for estimating by combining sparse observations and newly released ERA5 reanalysis data was proposed. The spatial heterogeneity in over China and its influencing factors (e.g., topography, climate, and vegetation) were then explored. A re‐examination of long‐term spatiotemporal variations in and its potential driving forces over China was also performed at grid scales. The results demonstrated that our estimations reproduced similar patterns to those derived from observations. The subsequent analyses revealed that the annual and seasonal variability in varied substantially among different altitude gradients, land cover types, and climate zones. The vital role played by vegetation covers in regulating local was emphasized in this study, which was poorly assessed previously. Moreover, in low altitude zones showed lower values but strong temporal fluctuations and contributed more to the interannual variability in over China, which could be mainly attributed to human activities. The present findings can provide an alternative method for estimating and be further used to assess regional or global temperature changes.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 3
    In: International Journal of Climatology, Wiley, Vol. 42, No. 2 ( 2022-02), p. 928-951
    Abstract: Surface albedo plays a key role in the energy and water cycles, and reasonable parameterizations of surface albedo will be greatly helpful to improve the simulation of radiation partition in climate models. In‐situ measurements of albedo from five sites over the Tibetan Plateau (TP) and MODIS albedo product are used to evaluate monthly, annual, and seasonal variations of the surface albedo simulated by 24 Global Climate Models (GCMs) archived by the Coupled Model Intercomparison Project Phase 5 (CMIP5). Potential factors contributing to the bias of simulated albedo were investigated. The results show that the monthly albedo of 24 GCMs varied across models, and the difference among the models was smaller in the June–July–August period than the December–January–February period. The ensemble mean albedo of the 24 GCMs was more consistent with the in‐situ measurements than the individual model. The albedo calculated from the BNU‐ESM, GFDL‐CM3, INM‐CM4, MIROC4h, MIROC‐ESM, and MIROC‐ESM‐CHEM models coincided with the observations from June to September, increasing rapidly to above 0.4 from November. However, the annual cycle of surface albedo was insignificant when simulated by some models, such as CanESM2, CSIRO‐Mk3.6.0, CMCC‐CMS, IPSL‐CM5A‐MR, and MPI‐ESM‐MR. Additionally, the surface albedo was overestimated by CMIP5 multi‐model ensemble mean—with a smaller amplitude of daily albedo—compared with the values estimated from in‐situ observations and MCD43A3. In all 24 models, snow albedo parameterization schemes were found to variably fit the attenuation of snow albedo over time. The cold temperature, relatively more precipitation, fresh snow density, and albedo in these models led to large biases in snow depth and ablation. The surface albedo tends to increase without the influence of blowing snow and withered grass on the model's albedo.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 4
    Online Resource
    Online Resource
    American Society for Photogrammetry and Remote Sensing ; 2020
    In:  Photogrammetric Engineering & Remote Sensing Vol. 86, No. 5 ( 2020-05-01), p. 317-325
    In: Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 86, No. 5 ( 2020-05-01), p. 317-325
    Abstract: This article presents a novel strategy for improving the well-established component substitution-based multispectral image fusion methods, because the fused results obtained by component substitution methods tend to exhibit significant spectral distortion. The main cause of spectral distortion is analyzed and discussed based on the component substitution method's general model. An improved scheme is derived from the sensitivity imaging model to refine the approximate spatial detail and obtain one that is almost ideal. The experimental results on two data sets show that when it has been integrated into the Gram–Schmidt method and the generalized intensity-hue-saturation method, the proposed scheme allows the production of fused images of the same spatial sharpness as standard implementations but with significantly increased spectral quality. Quantitative scores and visual inspection at full resolution and spatially reduced resolution confirm the superiority of the improved methods over the conventional algorithms.
    Type of Medium: Online Resource
    ISSN: 0099-1112
    RVK:
    Language: English
    Publisher: American Society for Photogrammetry and Remote Sensing
    Publication Date: 2020
    detail.hit.zdb_id: 2317128-5
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  • 5
    Online Resource
    Online Resource
    American Society for Photogrammetry and Remote Sensing ; 2022
    In:  Photogrammetric Engineering & Remote Sensing Vol. 88, No. 10 ( 2022-10-01), p. 653-664
    In: Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 88, No. 10 ( 2022-10-01), p. 653-664
    Abstract: We present a conceptually simple and flexible method for hyperspectral-image open set classification. Unlike previous methods, where the abundant unlabeled data inherent in the data set are ignored completely and unknown classes are inferred using score post-calibration, our approach makes the unlabeled data join in and help to train a simple and practical model for open set classification. The model is able to provide an explicit decision score for both unknown classes and each known class. The main idea of the proposed method is augmenting the original training set of K known classes using the pseudo-labeled unknown-category samples that are detected elaborately from the unlabeled data using modified OpenMax and semi-supervised iterative learning. Then a (K + 1)-class deep convolutional neural network model is trained based on the augmented training set with (K + 1) class samples. The model can not only classify instances of each known class but also refuse instances of unknown class explicitly. We validated the proposed method on four well-known hyperspec tral-image data sets, obtaining superior performance over previous methods.
    Type of Medium: Online Resource
    ISSN: 0099-1112
    RVK:
    Language: English
    Publisher: American Society for Photogrammetry and Remote Sensing
    Publication Date: 2022
    detail.hit.zdb_id: 2317128-5
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  • 6
    Online Resource
    Online Resource
    American Society for Photogrammetry and Remote Sensing ; 2022
    In:  Photogrammetric Engineering & Remote Sensing Vol. 88, No. 8 ( 2022-08-01), p. 535-546
    In: Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 88, No. 8 ( 2022-08-01), p. 535-546
    Abstract: Hyperspectral image (HSI) classification is the most vibrant research field in the hyperspectral community, aiming to assign each pixel in the image to one certain land cover category based on its spectral or spectral-spatial characteristics. Recently, some spectral-spatial–feature deep learning–based convolutional neural networks have been proposed and demonstrated remarkable classification performance. However, these networks are time consuming when facing a real HSI in practical application. The trained mode l must be forwarded independently across m × m crops of the image in strides of 1 pixel. In this article, an efficient and practical network was proposed for HSI classification that can take an HSI as an input instance and directly output a dense pixel-level classification map. First, a novel mechanism, training based on pixels and prediction based on images (TPPI ), is proposed and formulated. Second, some basic rules that should be obeyed during network design and implementation are given. Finally, following the basic rules, three TPPI -Nets are derived and demonstrated based on state-of- the-art classification networks. Experimental results on three public data sets show that the proposed TPPI-Net can not only obtain higher classification accuracy than the existing DCNN-based methods but also greatly reduce the computational complexity of HSI classification.
    Type of Medium: Online Resource
    ISSN: 0099-1112
    RVK:
    Language: English
    Publisher: American Society for Photogrammetry and Remote Sensing
    Publication Date: 2022
    detail.hit.zdb_id: 2317128-5
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  • 7
    In: Geoderma, Elsevier BV, Vol. 352 ( 2019-10), p. 197-203
    Type of Medium: Online Resource
    ISSN: 0016-7061
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 281080-3
    detail.hit.zdb_id: 2001729-7
    SSG: 13
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  • 8
    In: Geoderma, Elsevier BV, Vol. 341 ( 2019-05), p. 1-9
    Type of Medium: Online Resource
    ISSN: 0016-7061
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 281080-3
    detail.hit.zdb_id: 2001729-7
    SSG: 13
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    In: Geoderma, Elsevier BV, Vol. 314 ( 2018-03), p. 184-189
    Type of Medium: Online Resource
    ISSN: 0016-7061
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 281080-3
    detail.hit.zdb_id: 2001729-7
    SSG: 13
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    Online Resource
    Online Resource
    Wiley ; 2020
    In:  International Journal of Climatology Vol. 40, No. 12 ( 2020-10), p. 5368-5388
    In: International Journal of Climatology, Wiley, Vol. 40, No. 12 ( 2020-10), p. 5368-5388
    Abstract: Due to the scarcity of gauge observations and inaccuracy of satellite estimation, obtaining reliable daily precipitation estimates over the Qinghai‐Tibetan Plateau (QTP) remains challenging. In this article, an integrated scheme is developed based on the assumption that in a specific climatic region, the similarity of environmental conditions related to precipitation (SEP) in two locations is positively correlated to the similarities of occurrence and magnitude of precipitation between them. First, the QTP was divided into the northwestern arid, middle semi‐arid/semi‐humid, and southeastern humid climatic sub‐regions by grouping analysis. Second, based on modified weighted k‐nearest neighbour model, daily precipitation of target locations in these climatic sub‐regions were predicted by weighted regression of a group of gauge observations that have the largest SEP with the target locations. SEP was calculated by the following auxiliary environmental factors: longitude, latitude, elevation, Normalized Difference Vegetation Index, relative humidity, and CMORPH (Climate Prediction Center's morphing technique) daily precipitation estimates (original CMORPH). The validation results demonstrate the effectiveness of the proposed scheme. Compared with the original CMORPH and PDF‐calibrated CMORPH (CMORPH calibrated by probability density function matching plus optimal interpolation method) at daily, monthly, and yearly scales, the scheme improves the rain/no rain detection capacity and the accuracy of daily precipitation estimates. In addition, the daily precipitation estimates obtained from this scheme can present significant discrimination over specific geographic units, particularly the Qaidam Basin, the great bend of the Brahmaputra River, and Hengduan Mountain.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1491204-1
    SSG: 14
    Library Location Call Number Volume/Issue/Year Availability
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