Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

Jia, Aolin; Wang, Dongdong; Liang, Shunlin; Peng, Jingjing; Yu, Yunyue

doi:10.1016/j.isprsjprs.2023.01.004

Cited by 5 publications

(2 citation statements)

References 125 publications

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“…Pixels missing in the area of interest can typically be compensated for through temporal interpolation by multiple years' average [25]. To enhance the reliability of predictions pertaining to pixel value changes within the covered area, the average values can be physically constrained by combining a priori knowledge, such as albedo phenology [26]. Temporal filter techniques are commonly used to smooth the time series for noise reduction purposes, making the albedo change consistent with the phenology trend [27].…”

Section: Introductionmentioning

confidence: 99%

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Chen,

Yu,

Zhang

et al. 2024

Atmosphere

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Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

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Section: Introductionmentioning

confidence: 99%

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Chen,

Yu,

Zhang

et al. 2024

Atmosphere

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“…In these cases, some drawbacks remain related to the limited interaction of the SAR signal with snowpack, especially in dry conditions [34]. Other authors have also studied snow variables such as snow cover fraction [38], SWE [39], or albedo [40] by using microwave passive sensors instead of active ones. The coarse spatial resolution is the main drawback of these sensors when analyzing snow properties in mountain areas [41].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

Torralbo,

Pimentel,

Polo

et al. 2023

Remote Sensing

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Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims to understand the possibilities of S-1 SAR imagery to capture snowmelt dynamics and related changes in streamflow response in semi-arid mountains. The results proved that S-1 SAR imagery was able not only to capture the final spring melting but also all melting cycles that commonly appear throughout the year in these types of environments. The general change detection approach to identify wet snow was adapted for these regions using as reference the average S-1 SAR image from the previous summer, and a threshold of −3.00 dB, which has been assessed using Landsat images as reference dataset obtaining a general accuracy of 0.79. In addition, four different types of melting-runoff onsets depending on physical snow condition were identified. When translating that at the catchment scale, distributed melting-runoff onset maps were defined to better understand the spatiotemporal evolution of melting dynamics. Finally, a linear connection between melting dynamics and streamflow was found for long-lasting melting cycles, with a determination coefficient (R2) ranging from 0.62 to 0.83 and an average delay between the melting onset and streamflow peak of about 21 days.

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Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Ding,

Liang,

et al. 2024

Science of Remote Sensing

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Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

Cited by 5 publications

References 125 publications

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

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