This dataset contains surface shortwave albedo retrieval and surface classification data derived from both MODIS and SGLI satellite sensors over the Sea of Okhotsk, covering the period from January to May 2021. The data is stored in NetCDF format and standardized on a 1-km grid, allowing for direct pixel-to-pixel comparison. The dataset consists of albedo values and surface classifications, enabling an in-depth analysis of retrieval consistency and discrepancies between the two sensors. Data were averaged over weekly time periods for valid sea-ice pixels. The specific dates for each weekly period are provided. The retrieval algorithm is described in https://doi.org/10.5194/tc-17-1053-2023.
The dataset includes the following variables:1. MODIS_SW_albedo: Albedo values retrieved from the MODIS sensor using the SciML albedo retrieval algorithm. Values are provided for the period January-May 2021 on a grid with a spatial resolution of 1 km.2. MODIS_mask: Surface classification mask for the MODIS sensor. The mask values correspond to different surface types and features:• 0: Water• 1: Sea-ice• 2: Sea-covered ice3. SGLI_SW_albedo: Albedo values retrieved from the SGLI sensor using the the SciML albedo retrieval algorithm. These values are provided for the same time period (January-May 2021) and grid as the MODIS data.4. SGLI_mask: Surface classification mask for the SGLI sensor. The mask uses the same classification scheme as the MODIS mask:• 0: Water• 1: Sea-ice• 2: Sea-covered ice---Time Periods Covered: The dataset spans eight weekly time intervals, corresponding to the following periods: • 2021-01-08 ~ 2021-01-13 • 2021-01-31 ~ 2021-02-06 • 2021-02-11 ~ 2021-02-17 • 2021-03-05 ~ 2021-03-12 • 2021-03-15 ~ 2021-03-20 • 2021-04-01 ~ 2021-04-07 • 2021-04-22 ~ 2021-04-30 • 2021-05-07 ~ 2021-05-12Each period represents a weekly aggregate of albedo and surface classification data from both sensors, averaged where valid sea-ice pixels were present.---Format: The dataset is provided in NetCDF format (.nc), which can be read and analyzed using common geospatial and scientific data tools such as Python's xarray or Panoply.
