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

J 2020

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

ŘEZNÍK, Tomáš; Tomáš PAVELKA; Lukáš HERMAN; Vojtěch LUKAS; Petr ŠIRŮČEK et al.

Základní údaje

Originální název

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

Autoři

ŘEZNÍK, Tomáš; Tomáš PAVELKA ; Lukáš HERMAN; Vojtěch LUKAS; Petr ŠIRŮČEK; Šimon LEITGEB a Filip LEITNER

Vydání

Remote Sensing, Basel, Switzerland, MDPI, 2020, 2072-4292

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10500 1.5. Earth and related environmental sciences

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.848

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/20:00115784

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index

Štítky

14315030, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 21. 10. 2020 16:23, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

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.

Návaznosti

818346, interní kód MU

Název: Si-EU-Soil (Akronym: SIEUSIOL)

Investor: Evropská unie, Si-EU-Soil, Food security, sustainable agriculture and forestry, marine and maritime and inland water research (Societal Challenges)

Citovat

ŘEZNÍK, Tomáš; Tomáš PAVELKA; Lukáš HERMAN; Vojtěch LUKAS; Petr ŠIRŮČEK; Šimon LEITGEB a Filip LEITNER. Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements. Remote Sensing. Basel, Switzerland: MDPI, 2020, roč. 12, č. 12, s. 1-17. ISSN 2072-4292. Dostupné z: https://doi.org/10.3390/rs12121917.

@article{1661836,
   author = {Řezník, Tomáš and Pavelka, Tomáš and Herman, Lukáš and Lukas, Vojtěch and Širůček, Petr and Leitgeb, Šimon and Leitner, Filip},
   article_location = {Basel, Switzerland},
   article_number = {12},
   doi = {https://doi.org/10.3390/rs12121917},
   keywords = {yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index},
   language = {eng},
   issn = {2072-4292},
   journal = {Remote Sensing},
   title = {Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements},
   url = {https://www.mdpi.com/2072-4292/12/12/1917},
   volume = {12},
   year = {2020}
}

TY  - JOUR
ID  - 1661836
AU  - Řezník, Tomáš - Pavelka, Tomáš - Herman, Lukáš - Lukas, Vojtěch - Širůček, Petr - Leitgeb, Šimon - Leitner, Filip
PY  - 2020
TI  - Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements
JF  - Remote Sensing
VL  - 12
IS  - 12
SP  - 1-17
EP  - 1-17
PB  - MDPI
SN  - 20724292
KW  - yield productivity zones
KW  - yield measurements
KW  - satellite images
KW  - precision agriculture
KW  - Enhanced Vegetation Index
UR  - https://www.mdpi.com/2072-4292/12/12/1917
L2  - https://www.mdpi.com/2072-4292/12/12/1917
N2  - 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.
ER  -

ŘEZNÍK, Tomáš; Tomáš PAVELKA; Lukáš HERMAN; Vojtěch LUKAS; Petr ŠIRŮČEK; Šimon LEITGEB a Filip LEITNER. Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements. \textit{Remote Sensing}. Basel, Switzerland: MDPI, 2020, roč.~12, č.~12, s.~1-17. ISSN~2072-4292. Dostupné z: https://doi.org/10.3390/rs12121917.

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