Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements
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...
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/12/1917 |
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| author | Tomáš Řezník Tomáš Pavelka Lukáš Herman Vojtěch Lukas Petr Širůček Šimon Leitgeb Filip Leitner |
| author_facet | Tomáš Řezník Tomáš Pavelka Lukáš Herman Vojtěch Lukas Petr Širůček Šimon Leitgeb Filip Leitner |
| author_sort | Tomáš Řezník |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:12:17Z |
| format | Article |
| id | doaj.art-9865bb84804d4bef816544bffec45a64 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:12:17Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9865bb84804d4bef816544bffec45a642023-11-20T03:43:33ZengMDPI AGRemote Sensing2072-42922020-06-011212191710.3390/rs12121917Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery MeasurementsTomáš Řezník0Tomáš Pavelka1Lukáš Herman2Vojtěch Lukas3Petr Širůček4Šimon Leitgeb5Filip Leitner6Department of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech RepublicDepartment of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech RepublicDepartment of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech RepublicDepartment of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University, Zemědělská 1, 613 00 Brno, Czech RepublicDepartment of Agrosystems and Bioclimatology, Faculty of Agronomy, Mendel University, Zemědělská 1, 613 00 Brno, Czech RepublicDepartment of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech RepublicDepartment of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech RepublicYield 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.https://www.mdpi.com/2072-4292/12/12/1917yield productivity zonesyield measurementssatellite imagesprecision agricultureEnhanced Vegetation Index |
| spellingShingle | Tomáš Řezník Tomáš Pavelka Lukáš Herman Vojtěch Lukas Petr Širůček Šimon Leitgeb Filip Leitner Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements Remote Sensing yield productivity zones yield measurements satellite images precision agriculture Enhanced Vegetation Index |
| title | Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements |
| title_full | Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements |
| title_fullStr | Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements |
| title_full_unstemmed | Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements |
| title_short | Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements |
| title_sort | prediction of yield productivity zones from landsat 8 and sentinel 2a b and their evaluation using farm machinery measurements |
| topic | yield productivity zones yield measurements satellite images precision agriculture Enhanced Vegetation Index |
| url | https://www.mdpi.com/2072-4292/12/12/1917 |
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