Advertisement

Toward Satellite-Based Estimation of Growing Season Framing Dates in Conditions of Unstable Weather

  • Evgeny PanidiEmail author
  • Ivan Rykin
  • Giovanni Nico
  • Valery Tsepelev
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

This paper described an experiment of developing a complex technique for satellite imagery time series processing when estimating spatial distribution of framing (or changing) calendar dates of the growing seasons. Particularly, we reflected on allocation of growing season framing dates in conditions of unstable weather. As surface air temperature may fluctuate in many cases around bordering values during some days or weeks, the allocation of stable crossing of temperature through the control values (that marks time frames of growing seasons) is a fundamental problem in the case of ground observations. We compared some results of the growing season frames allocation based on ground data observations of the temperature (needed for the verification and calibration purposes), and the estimation results made relying on the data of remotely observed Normalized Difference Water Index (NDWI).

Keywords

Growing seasons Ground meteorological observations Remote sensing data NDWI 

Notes

Acknowledgements

The MOD9A1 V006 datasets were retrieved from the online LP DAAC2Disk download manager, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.usgs.gov/data_access/daac2disk).

The MODIS Level 1B datasets were acquired from the Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/).

Ground observations data were retrieved from Waisori Web interface, courtesy of the RIHMI-WDC of Roshydromet, Veselov V.M., Pribylskaya I.R., Mirzeabasov O.A. (http://aisori-m.meteo.ru/waisori/).

References

  1. 1.
    Delbart, N.J.-P., Kergoats, L., Le Toan, T., Lhermitte, J., Picard, G.: Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens. Environ. 97(1), 26–38 (2005).  https://doi.org/10.1016/j.rse.2005.03.011CrossRefGoogle Scholar
  2. 2.
    Gao, B.C.: NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Env. 58(3), 257–266 (1996)CrossRefGoogle Scholar
  3. 3.
    Panidi, E., Tsepelev, V.: NDWI-based technique for detection of change dates of the growing seasons in Russian Subarctic. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W2, 179–182 (2017).  https://doi.org/10.5194/isprs-archives-xlii-3-w2-179-2017CrossRefGoogle Scholar
  4. 4.
    Panidi, E., Tsepelev, V., Torlopova, N., Bobkov, A.: Mapping of the land cover spatiotemporal characteristics in Northern Russia caused by climate change. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 997–1002 (2016).  https://doi.org/10.5194/isprs-archives-xli-b8-997-2016CrossRefGoogle Scholar
  5. 5.
    Sekhon, N.S., Hassan, Q.K., Sleep, R.W.: A remote sensing based system to predict early spring phenology over boreal forest. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XXXVIII, 5, Part 1 (2010)Google Scholar
  6. 6.
    Skrynyk, O.A., Snizhko, S.I.: The issue of determining of the surface air temperature stable transition dates through a certain fixed value (analysis of methods). Ukrai’ns’kyj gidrometeorologichnyj zhurnal 3, 56–66 (2008). in UkrainianGoogle Scholar
  7. 7.
    Skrynnik, O.Y., Skrynnik, O.A.: Climatological method for determining of the date of average daily air temperature steady transition through a given threshold. Meteorologiya i gidrologiya 10, 90–99 (2009). in RussianGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Evgeny Panidi
    • 1
    Email author
  • Ivan Rykin
    • 1
  • Giovanni Nico
    • 1
    • 2
  • Valery Tsepelev
    • 3
  1. 1.Saint Petersburg State UniversitySt. PetersburgRussia
  2. 2.Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del CalcoloBariItaly
  3. 3.Russian State Hydrometeorological UniversitySt. PetersburgRussia

Personalised recommendations