2023
DOI: 10.1016/j.isprsjprs.2023.01.004
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Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

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Cited by 7 publications
(2 citation statements)
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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%
“…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%
“…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%