** Progress in Earth and Planetary Science is the official journal of the Japan Geoscience Union, published in collaboration with its 50 society members.

    >>Japan Geoscience Union

    >>Links to 50 society members

    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    Progress in Earth and Planetary Science

    Gallery View of PEPS Articles

    Research

    Biogeosciences

    202402202402

    Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

    Hiroki Mizuochi, Taiga Sasagawa, Akihiko Ito, Yoshihiro Iijima, Hotaek Park, Hirohiko Nagano, Kazuhito Ichii, Tetsuya HiyamaHiroki Mizuochi, Taiga Sasagawa, Akihiko Ito, Yoshihiro Iijima, Hotaek Park, Hirohiko Nagano, Kazuhito Ichii, Tetsuya Hiyama

    Continental-scale water and vegetation maps, Satellite data, Pan-Arctic region, Data fusion, Machine learning, Trend analysis, Phenological feature extraction

    (Top row) scheme of data integration. Microwave satellite data, meteorological reanalysis data were used to fill the observation gap of long-term optical satellite data (MODIS). (Bottom row) an example of validation for the data integration. (Left panel) the original MODIS image, (center panel) the gap-filled MODIS image, and (right panel) scatterplot of those data.

    As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500┬ám) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.