Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series
Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-02-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2020.1831969 |
| _version_ | 1818622149528649728 |
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| author | Jonas Schreier Gohar Ghazaryan Olena Dubovyk |
| author_facet | Jonas Schreier Gohar Ghazaryan Olena Dubovyk |
| author_sort | Jonas Schreier |
| collection | DOAJ |
| description | Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultural assessment. Remotely sensed data is ideally suited for both large-scale and field-level analyses due to the wide variability of datasets with diverse spatiotemporal resolution. To derive crop-specific phenometrics, we fused time series from Landsat 8 and Sentinel 2 with Moderate-resolution Imaging Spectroradiometer (MODIS) data. Using a linear regression approach, synthetic Landsat 8 and Sentinel 2 data were created based on MODIS imagery. This fusion-process resulted in synthetic imagery with radiometric characteristics of original Landsat 8 and Sentinel 2 data. We created four different time series using synthetic data as well as a mix of original and synthetic data. The extracted time series of phenometrics consisting of both synthetic and original data showed high detail in the final phenomaps which allowed intra-field level assessment of crops. In-situ field reports were used for validation. Our phenometrics showed only a few days of deviation for most crops and datasets. The proposed data integration method can be applied in areas where data from a single high-resolution source is scarce. |
| first_indexed | 2024-12-16T18:20:35Z |
| format | Article |
| id | doaj.art-51cba75f57ad4a1ca5e662cedae12ee4 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-12-16T18:20:35Z |
| publishDate | 2021-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-51cba75f57ad4a1ca5e662cedae12ee42022-12-21T22:21:34ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542021-02-0154S1475810.1080/22797254.2020.18319691831969Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time seriesJonas Schreier0Gohar Ghazaryan1Olena Dubovyk2University of BonnUniversity of BonnUniversity of BonnAgricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultural assessment. Remotely sensed data is ideally suited for both large-scale and field-level analyses due to the wide variability of datasets with diverse spatiotemporal resolution. To derive crop-specific phenometrics, we fused time series from Landsat 8 and Sentinel 2 with Moderate-resolution Imaging Spectroradiometer (MODIS) data. Using a linear regression approach, synthetic Landsat 8 and Sentinel 2 data were created based on MODIS imagery. This fusion-process resulted in synthetic imagery with radiometric characteristics of original Landsat 8 and Sentinel 2 data. We created four different time series using synthetic data as well as a mix of original and synthetic data. The extracted time series of phenometrics consisting of both synthetic and original data showed high detail in the final phenomaps which allowed intra-field level assessment of crops. In-situ field reports were used for validation. Our phenometrics showed only a few days of deviation for most crops and datasets. The proposed data integration method can be applied in areas where data from a single high-resolution source is scarce.http://dx.doi.org/10.1080/22797254.2020.1831969data-fusionphenometricshigh-resolutioncrops |
| spellingShingle | Jonas Schreier Gohar Ghazaryan Olena Dubovyk Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series European Journal of Remote Sensing data-fusion phenometrics high-resolution crops |
| title | Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series |
| title_full | Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series |
| title_fullStr | Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series |
| title_full_unstemmed | Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series |
| title_short | Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series |
| title_sort | crop specific phenomapping by fusing landsat and sentinel data with modis time series |
| topic | data-fusion phenometrics high-resolution crops |
| url | http://dx.doi.org/10.1080/22797254.2020.1831969 |
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