Method to smooth time series of NDVI

Kataev, Mikhail Yu.; Bekerov, A.A.; Medveckiy, D.V.

doi:10.21293/1818-0442-2017-20-1-85-88

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Application of artificial neural networks to reconstruction of the vegetation indices time series have became popular study direction in recent years. Usually, authors [2,7,9] discover the technique as an approach to alignment, smoothing and direct analysis of vegetation indices data obtained from remote sensing observations. Our study [6,12] is directed onto investigation of artificial intelligence technologies application to estimate and predict dynamics of vegetation cover.…”

Section: Introductionmentioning

confidence: 99%

Toward Methodology of Predictive Modeling of the Growing Season Dynamics

Shagnieva¹,

Panidi²

2021

Proceedings 2021

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We applied the Normalized Difference Vegetation Index (NDVI) as an indicator reflecting dynamics of vegetation cover growing. NDVI time series were derived from the Terra/MODIS satellite imagery (MOD09A1 product was used, available from the open archive of the US Geological survey). Data for 2006-2016 were used. The ground test plate is located in the Karelia Republic, Russia (61°,7218 N, 34°,3689 E). Moving average method with three inflection points was applied to ensure a piecewise monotone smoothing of NDVI time series. We also applied meteorological data (surface air temperature, average daily precipitation, average snow cover heightcollected at Petrozavodsk meteorological station in 2006-2016). Used meteorological data were derived from the website of the Russian Federal Service for Hydrometeorology and Environmental Monitoring -ROSHYDROMET (http://meteo.ru). As a result, we designed a methodology for selection and processing (including initial compilation, smoothing and interpolation of time series) of remote sensing and meteorological observations to generate training data and control datasets demanded by artificial neural network. Obtained results demonstrate that neural network trained using meteorological data can predict NDVI values. Less than 15% relative error of the NDVI prediction was gained at our ground test plate.

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Section: Introductionmentioning

confidence: 99%

Toward Methodology of Predictive Modeling of the Growing Season Dynamics

Shagnieva¹,

Panidi²

2021

Proceedings 2021

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Аппроксимация Временных Рядов Индексов Вегетации (NDVI И EVI) Для Мониторинга Сельхозкультур (Посевов) Хабаровского Края

Stepanov,

Fomina,

Illarionova

et al. 2023

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Approximation of the series of the seasonal vegetation index time series is the basis for monitoring agricultural crops, their identification and cropland classification. For cropland of the Khabarovsk Territory in the period from May to October 2021, NDVI and EVI time series were constructed using Sentinel-2A (20 m) multispectral images using a cloud mask. Five functions were used to approximate time series: Gaussian function; double Gaussian; double sine wave; Fourier series; double logistic. Characteristics of extremums for approximated time series for different types of arable land were built and calculated: buckwheat, perennial grasses, soybeans, fallow and ley. It was shown that each type requires a characteristic species. It was found (p<0.05) that Fourier approximation showed the highest accuracy for NDVI and EVI series (average error, respectively, 8.5% and 16.0%). Approximation of the NDVI series using a double sine, double Gaussian and double logistic function resulted in an error increase of 8.9-10.6%. Approximation of EVI series based on double Gaussian and double sine wave causes an increase in average errors up to 18.3-18.5%. The conducted a posteriori analysis using the Tukey criterion showed that for soybean, fallow and ley lands, it is better to use the Fourier series, double Gaussian or double sine wave to approximate vegetation indices, for buckwheat it is advisable to use the Fourier series or double Gaussian. In general, the average approximation error of the NDVI seasonal time series is 1.5-4 times less than the approximation error of the EVI series.

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Method to smooth time series of NDVI

Cited by 2 publications

References 3 publications

Toward Methodology of Predictive Modeling of the Growing Season Dynamics

Toward Methodology of Predictive Modeling of the Growing Season Dynamics

Аппроксимация Временных Рядов Индексов Вегетации (NDVI И EVI) Для Мониторинга Сельхозкультур (Посевов) Хабаровского Края

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