Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

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Publikace nespadá pod Fakultu sportovních studií, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.
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ŘEZNÍK Tomáš PAVELKA Tomáš HERMAN Lukáš LUKAS Vojtěch ŠIRŮČEK Petr LEITGEB Šimon LEITNER Filip

Rok publikování 2020
Druh Článek v odborném periodiku
Časopis / Zdroj Remote Sensing
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://www.mdpi.com/2072-4292/12/12/1917
Doi http://dx.doi.org/10.3390/rs12121917
Klíčová slova yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index
Popis Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.
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