A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

Chen, Siyong; Wang, Xiaoyan; Guo, Hui; Xie, Peiyao; Wang, Jian; Hao, Xiaohua

doi:10.3390/rs12213577

Cited by 11 publications

(5 citation statements)

References 67 publications

(86 reference statements)

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“…In a target TAC image, N candidate cloud-free pixels are extracted within a 3 × 3 × t (7 ≤ t ≤ 15) space-time cube, which denotes a spatial window of dimensions 3 × 3 and a time window of t days with the center of the time window corresponding to the day of data gap. A space-time cube that is too small may not provide sufficient candidate cloud-free pixels, while a space-time cube that is too large can result in increased computational and time costs during the cloud removal process [72]. Thus, the value of t depends on whether it is satisfied such that N accounts for at least 30% (this threshold has been tested to be appropriate) of the total number of pixels in the space-time cube.…”

Section: Spatio-temporal Weighted (Stw) Methodsmentioning

confidence: 99%

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

Deng,

Tang,

Dong

et al. 2024

Remote Sensing

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Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.

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Section: Spatio-temporal Weighted (Stw) Methodsmentioning

confidence: 99%

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

Deng,

Tang,

Dong

et al. 2024

Remote Sensing

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“…Additionally, the conditional probability interpolation method [13] can effectively calculate the conditional probability of snow pixels being covered by clouds and meteorological data to remove clouds, but this method has limited capacity for removing clouds in areas with few in situ observations. Furthermore, Chen [21] proposes a conditional probability interpolation method based on a space-time cube (STCPI), which takes the conditional probability as the weight of the spacetime neighborhood pixels to calculate the snow probability of the cloud pixels, and then the snow condition of the cloud pixels can be recovered by the snow probability. However, existing one-step cloud removal algorithms that utilize spatiotemporal information have significant time computational costs and require multiple auxiliary data, which to some extent limits the application of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Zhu,

Ma,

Zhang

et al. 2023

Water

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Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

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“…Popular methods to fill the cloud gaps in the MODIS snow cover product include temporal-spatial filtering methods [36][37][38][39], adjacent temporal composite methods [40,41], and multisource fusion methods in which the cloud pixels are filled with the microwave snow water equivalent (SWE) or snow depth data [42][43][44]. Recently, some new gap-filling methods have been developed, such as methods based on similar pixels [45][46][47], time-space cubes [48], and conditional probability interpolation [49,50]. The basic idea of these cloud removal methods is to fill the value of pixels under the cloud with cloud-free pixels in the spatiotemporal neighborhood.…”

Section: Introductionmentioning

confidence: 99%

Cloud–Snow Confusion with MODIS Snow Products in Boreal Forest Regions

et al. 2022

Self Cite

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Reliable cloud masks in Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have a high potential to improve the retrieval of snow properties. However, cloud–snow confusion is a popular problem in MODIS snow cover products, especially in boreal forest areas. A large amount of forest snow is misclassified as clouds because of the low normalized difference snow index (NDSI), and excessive cloud masks limit the application of snow products. In addition, ice clouds are easily misclassified as snow due to their similar spectral characteristics, which leads to snow commission errors. In this paper, we quantitatively evaluated the cloud–snow confusion in Northeast China and found that snow-covered forests and transition zones from snow-covered to snow-free areas are prone to being misclassified as clouds, while clouds are less likely to be misclassified as snow. A temporal-sequence cloud–snow-distinguishing algorithm based on the high-frequency observation characteristics of the Himawarri-8 geostationary meteorological satellite is proposed. In the temporal-sequence images acquired from that satellite, the NDSI variance in cloud pixels should be greater than that of snow because clouds vary over time, while snow is relatively stable. In the MODIS snow cover products, the cloud pixels with NDSI variance lower than a threshold are identified as cloud-free areas and attributed their raw NDSI value, while the snow pixels with NDSI variance greater than the threshold are marked as clouds. We applied this method to MOD10A1 C6 in Northeast China. The results showed that the excessive cloud masks were greatly eliminated, and the new cloud mask was in good agreement with the real cloud distribution. At the same time, some possible ice clouds which had been misclassified as snow for their spectral characteristics similar to those of snow were identified correctly.

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A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

Cited by 11 publications

References 67 publications

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Cloud–Snow Confusion with MODIS Snow Products in Boreal Forest Regions

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