Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations
In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (<i>Triticum aestivum</i>), s...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/18/3659 |
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| author | Jiří Tomíček Jan Mišurec Petr Lukeš |
| author_facet | Jiří Tomíček Jan Mišurec Petr Lukeš |
| author_sort | Jiří Tomíček |
| collection | DOAJ |
| description | In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (<i>Triticum aestivum</i>), spring barley (<i>Hordeum vulgare),</i> winter rapeseed (<i>Brassica napus</i> subsp. <i>napus)</i>, alfalfa (<i>Medicago sativa</i>), sugar beet (<i>Beta vulgaris</i>), and corn (<i>Zea mays</i> subsp. <i>Mays</i>) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (<i>rRMSE</i>; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (<i>r</i> = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment. |
| first_indexed | 2024-03-10T07:15:50Z |
| format | Article |
| id | doaj.art-d3a204d4ec0847da837a540f7e551e2d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T07:15:50Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d3a204d4ec0847da837a540f7e551e2d2023-11-22T15:06:25ZengMDPI AGRemote Sensing2072-42922021-09-011318365910.3390/rs13183659Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite ObservationsJiří Tomíček0Jan Mišurec1Petr Lukeš2Gisat Ltd., Milady Horákové 57, 17000 Praha, Czech RepublicGisat Ltd., Milady Horákové 57, 17000 Praha, Czech RepublicGlobal Change Research Institute, Czech Academy of Sciences, Bělidla 986/4a, 60300 Brno, Czech RepublicIn this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (<i>Triticum aestivum</i>), spring barley (<i>Hordeum vulgare),</i> winter rapeseed (<i>Brassica napus</i> subsp. <i>napus)</i>, alfalfa (<i>Medicago sativa</i>), sugar beet (<i>Beta vulgaris</i>), and corn (<i>Zea mays</i> subsp. <i>Mays</i>) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (<i>rRMSE</i>; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (<i>r</i> = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment.https://www.mdpi.com/2072-4292/13/18/3659Sentinel-2PROSAILradiative transferleaf area indexleaf chlorophyll contentleaf water content |
| spellingShingle | Jiří Tomíček Jan Mišurec Petr Lukeš Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations Remote Sensing Sentinel-2 PROSAIL radiative transfer leaf area index leaf chlorophyll content leaf water content |
| title | Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations |
| title_full | Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations |
| title_fullStr | Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations |
| title_full_unstemmed | Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations |
| title_short | Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations |
| title_sort | prototyping a generic algorithm for crop parameter retrieval across the season using radiative transfer model inversion and sentinel 2 satellite observations |
| topic | Sentinel-2 PROSAIL radiative transfer leaf area index leaf chlorophyll content leaf water content |
| url | https://www.mdpi.com/2072-4292/13/18/3659 |
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