A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

Jing, Yinghong; Shen, Huanfeng; Li, Xinghua; Guan, Xiaobin

doi:10.3390/rs11192261

Cited by 21 publications

(13 citation statements)

References 46 publications

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“…MODIS NDSI datasets are unable to represent the daily conditions of snow accumulation and ablation accurately because the optical remote-sensed images are subject to severe cloud pollution. Therefore, a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which is derived from our two-stage spatio-temporal fusion method (Jing et al, 2019), is presented to produce a spatio-temporal continuous snow collection. As shown in Fig.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China

Jing

Shen

2022

Preprint

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Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (XJ), Northeast China (NC), and Tibetan Plateau (TP). Spatio-temporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, the integrated normalized difference snow index (NDSI) maps containing additional details on snow cover extent are still extremely scarce. In this study, a recent 20-year stretch seamless MODIS NDSI collection in China is generated for the first time using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the gap-filled STAR NDSI collection is highly consistent with the Landsat NDSI dataset, with an average correlation coefficient of approximately 0.84. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).

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Section: Algorithm Descriptionmentioning

confidence: 99%

STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China

Jing

Shen

2022

Preprint

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“…Thus, several gap-filling methods with associated concern for spatial and temporal correlations of snow presence were proposed to remove clouds from NDSI Chen et al, 2020;Li et al, 2020). Among these methods, the spatiotemporal feature-based methods with relatively high robustness are more effective for improving NDSI datasets (Jing et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China

Jing

Li²,

Shen³

2022

Earth Syst. Sci. Data

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Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (NX), Northeast China (NC), and the Qinghai–Tibet Plateau (QTP). Spatiotemporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, the integrated normalized difference snow index (NDSI) maps containing additional details on snow cover extent are still extremely scarce. In this study, a recent 20-year stretch seamless Terra–Aqua MODIS NDSI collection in China is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the cloud-free STAR NDSI collection is superior to the two baseline datasets. The omission error decreased by 10 % in NX compared to the snow cover extent product, and the average correlation coefficient increased by 0.11 compared to the global cloud-gap-filled MODIS NDSI product. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues in China. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).

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“…Therefore, it is urgently required to fill the cloud gaps in daily NDSI data based on the MODIS V006 snow cover product [33]. Recently, a two-stage fusion framework through the Gaussian kernel function and error correction was proposed to produce a cloud-free MODIS NDSI product with a high accuracy over the Tibetan Plateau [34]. This method selected similar blocks within an eight-day temporal window and then predicted the NDSI value by Gaussian kernel function.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series

Chen

Wang

Guo

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The normalized difference snow index (NDSI) is the most popular snow detection index. Due to cloud cover, it is difficult to produce complete and gap-free NDSI datasets. In this study, a spatial and temporal adaptive gap-filling method (STAGFM) is developed, whereby a weighted cloud-free similar pixel function is established for NDSI prediction. Cloud-covered NDSI gaps are filled by combining daily MOD10A1 and MYD10A1, and adjacent temporal composite is applied. STAGFM is implemented with long-time interval data to completely recover NDSI gaps. Moderate Resolution Imaging Spectroradiometer NDSI product data (from November 1, 2017 to March 31, 2018) of Northeast China are chosen as an example, and daily cloud-free NDSI time series over this period are produced. The method effectiveness was validated by cloud assumption and snow depth data, and the results show that STAGFM completely removes clouds and achieves an average correlation coefficient (r), root-mean-square error, and mean absolute error of 0.95, 0.08, and 0.06, respectively.

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A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

Cited by 21 publications

References 46 publications

STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China

STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China

STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China

Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series

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