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

Panidi, Evgeny; Rykin, Ivan; Nico, Giovanni; Tsepelev, Valery

doi:10.1007/978-3-030-01440-7_31

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Examples of automated mapping of interpolated normals for average daily ground air temperature are presented in Figure 3. Computing of climatological normal (temperature and precipitation normals) and vegetation index normal are needed to ensure resolution of weather instability problem ( [8], [11], [12]) in future, when analytical functionality will be implemented to study climate dynamics. Time series of climate parameters, vegetation indexes and their normal can be mapped ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Taking into account 8-day time resolution of data series available for analysis on previous study stages (MODIS data were used [6], [7], [8]), we established the aim of time resolution enhancement to one day. Accordingly to this, we used AISORI database (http://aisori-m.meteo.ru/waisori/) as a source of the meteorological data (daily averaged ground air temperature and air precipitation data series were used); and Level-1 MODIS satellite imagery (https://ladsweb.modaps.eosdis.nasa.gov/search/).…”

Section: Methodsmentioning

confidence: 99%

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Toward Structure of Geospatial Database Used for Gis-Based Climate Change Modeling

Rykin¹,

Geoinformatics²,

Panidi³

et al. 2019

Proceedings 2019

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Climate is a dynamic and extremely multifactorial phenomenon. Various climate models can be implemented to assess and forecast climate change, which incorporate various numbers of climate factors and consider these factors either as independent or as complex (through grouping more or less similar factors). In accordance to such flexibility of climate modeling, issues of models verification and model-based computations evaluation remain most important tasks. Solution for the verification and evaluation problems can be achieved, in particular, through implementation of a complex geographic information system (GIS) capable to provide accumulation and joint analysis of retrospective and real-time arrays of climate and supplementary geospatial data. Our study series is devoted to the joint analysis of the dynamics of climate and vegetation cover parameters in the Northern regions. Currently we are presenting initial model of the geospatial database, which includes ground-based meteorological observations and satellite remote sensing data and allows assessment of various climate parameters (surface air temperature and humidity trends, framing dates and duration of growing seasons, etc.).

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Toward Structure of Geospatial Database Used for Gis-Based Climate Change Modeling

Rykin¹,

Geoinformatics²,

Panidi³

et al. 2019

Proceedings 2019

Self Cite

View full text Add to dashboard Cite

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“…2-4. As the initial graphs were noised due to the aberrations of used dataset and due to the weather (and vegetation cower) instability (Panidi et al, 2019), we applied graph smoothing using 5-day sliding window. Using growing season parameters (detected currently at the locations of meteorological stations), we are able to provide spatial interpolation and mapping of growing season dynamics (Fig.…”

Section: Mapping Possibilitiesmentioning

confidence: 99%

Highly Discrete Mapping of the Growing Season Time Frames And Time Dynamics

Rykin

Shagnieva

Panidi

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Growing season time frames can be estimated and mapped using the vegetation indexes mapping and analysis. This approach brings significant benefit consisted in the ability of detailed (highly discrete in the meaning of spatial resolution) mapping of spatial differences in growing season stage and length. In comparison with interpolation of ground air temperature (applied when using temperature to detect growing seasons), real spatial resolution raises to kilometers per pixel and higher, while nodes of ground observation network can be spaced by thousands of kilometers in some regions. Our ongoing study is devoted to design a processing chain for mapping of growing season time frames basing on vegetation indexes data with close-to-one-day time resolution. We used MOD09GA dataset as an initial data. Data processing was implemented in Google Earth Engine big geospatial data platform.

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“…According to the (USSR climate reference book…, 1968), the growing season is defined as the transition of the surface air temperature through threshold of +5 and +10 0 С. However, in some areas where there is no well spread meteorological network, the daily graphs of the normalized difference water index (NDWI) can be used (Panidi et al, 2018;Panidi et al, 2019). Where absolute minimums are the boundaries of the beginning and end of the growing season.…”

Section: Introductionmentioning

confidence: 99%

Automated Gis-Based Technique for Evaluation of Indirect Growing Season Estimations

Rykin

Panidi

Tsepelev

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.

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

Cited by 7 publications

References 4 publications

Toward Structure of Geospatial Database Used for Gis-Based Climate Change Modeling

Toward Structure of Geospatial Database Used for Gis-Based Climate Change Modeling

Highly Discrete Mapping of the Growing Season Time Frames And Time Dynamics

Automated Gis-Based Technique for Evaluation of Indirect Growing Season Estimations

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