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

• Published in: Progress in earth and planetary science 2024-02, Vol.11 (1), p.9-23, Article 9
• Main Authors: Mizuochi, Hiroki, Sasagawa, Taiga, Ito, Akihiko, Iijima, Yoshihiro, Park, Hotaek, Nagano, Hirohiko, Ichii, Kazuhito, Hiyama, Tetsuya
• Format: Article
• Language: English
• Subjects: 5. Biogeosciences; Arctic climate changes; Arctic zone; Atmospheric Sciences; Biogeosciences; Climate change; Cloud cover; Coastal zone; Continental-scale water and vegetation maps; Data fusion; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Hydrogeology; Hydrologic cycle; Machine learning; Pan-Arctic region; Planetology; Remote sensing; Remote sensing techniques; Research Article; Satellite data; Satellite observation; Satellites; Sensing techniques; Temperature rise; Terrestrial ecosystems; Trend analysis; Vegetation; Vegetation index; Water flow; Water-carbon cycles and terrestrial changes in the Arctic and subarctic regions
• ISSN: 2197-4284; 2197-4284
• DOI: 10.1186/s40645-024-00614-1

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 m) 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.