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Assessment of vegetation phenology using Sentinel-2 time series data
Danilchyk, Tatsiana ; Štych, Přemysl (advisor) ; Bohovic, Roman (referee)
This work aims to evaluate the detection of phenological phases of vegetation based on phenometric parameters according to archival Sentinel-2 data in the selected areas over the period 2018-2020. The first part of the work describes literature review of the relevant publications, which is followed by the description of the suggested methodology. Then, there are the results with the graphic material and description for each monitored site. In the final part of the work, advantages and disadvantages of the developed algorithm are discussed followed up by suggestions for future research and improvement. The developed algorithm consists of two parts. Masking out cloudy and cloud shadow pixels and generation on the vegetation indices time series is done in the GEE platform. The time series analysis and detection of SOS and EOS as well as statistical analysis are done in the R environment. The study areas of size 20 x 20 m represent different species of perennial vegetation across the Czech Republic. For the assessment of the phenophases detection are selected NDVI, RENDVI, NDRE, NDMI and MCARI. The Asymmetric Gaussian function and Double Logistic function are fitted to the time series of each vegetation season in each tested site, the phenology metrics are derived based on threshold or derivatives...
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Detection of drought events using combination of satellite data and soil moisture modelling
Semerádová, Daniela ; Trnka, Miroslav ; Hlavinka, Petr ; Balek, Jan ; Bohovič, Roman ; Tadesse, T. ; Hayes, M. ; Wardlow, B. ; Žalud, Zdeněk
The use of satellite data offers a potentially well usable tool to accurate drought monitoring. The study examines the space-time possibility of agricultural drought detection using freely available data from the MODIS instrument onboard Terra and Aqua satellites that reflects vegetation condition. Vegetation greenness metrics used in this study are based on the spectral reflectance curves in the visible red and near-infrared part of the spectrum and are expressed in relation to the average for the period of 2000-2014. The results are presented in weekly time step for the whole area of the Czech Republic, and are compared to the drought monitor system, based on the SoilClim dynamic model for soil water content estimates. These data, as well as other parameters, such as soil properties and land use, are integrated at 500 meters spatial resolution.
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Reliability of regional crop yield predictions in the Czech Republic based on remotely sensed data
Hlavinka, Petr ; Semerádová, Daniela ; Balek, Jan ; Bohovič, Roman ; Žalud, Zdeněk ; Trnka, Miroslav
Vegetation indices sensed by satellite optical sensors are valuable tools for assessing vegetation conditions including field crops. The aim of this study was to assess the reliability of regional yield predictions based on the use of the Normalized Difference Vegetation Index and the Enhanced Vegetation Index derived from the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite. Data available from the year 2000 were analysed and tested for seasonal yield predictions within selected districts of the Czech Republic. In particular, yields of spring barley, winter wheat, and oilseed winter rape during 2000–2014 were assessed. Observed yields from 14 districts were collected and thus 210 examples (15 years within 14 districts) were included. Selected districts differ considerably in soil fertility and terrain configuration and represent a transect across various agroclimatic conditions (from warm/dry to relatively cool/wet regions). Two approaches were tested: 1) using 16-day temporal composites of remotely sensed data provided by the United States Geological Survey, and 2) using daily remotely sensed data in combination with an originally developed smoothing method. Yields were predicted based on established regression models using remotely sensed data as an independent parameter. In addition to other findings, the impact of severe drought episodes within vegetation was identified and yield reductions at a district level were predicted. As a result, those periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above-normal yields of the tested field crops were predicted using the proposed method within the study region up to 30 days prior to harvest.
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