MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES Article Swipe
Thorsten Dahms
,
Sylvia Seissiger
,
Christopher Conrad
,
Erik Borg
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.5194/isprsarchives-xli-b2-171-2016
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.5194/isprsarchives-xli-b2-171-2016
Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. <br><br> In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. <br><br> The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. <br><br> Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. <br><br> Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.
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- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.5194/isprsarchives-xli-b2-171-2016
- https://doi.org/10.5194/isprsarchives-xli-b2-171-2016
- OA Status
- diamond
- Cited By
- 2
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4241705099
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- OpenAlex ID
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https://openalex.org/W4241705099Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/isprsarchives-xli-b2-171-2016Digital Object Identifier
- Title
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MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIESWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
- Publication date
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2016-06-07Full publication date if available
- Authors
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Thorsten Dahms, Sylvia Seissiger, Christopher Conrad, Erik BorgList of authors in order
- Landing page
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https://doi.org/10.5194/isprsarchives-xli-b2-171-2016Publisher landing page
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https://doi.org/10.5194/isprsarchives-xli-b2-171-2016Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.5194/isprsarchives-xli-b2-171-2016Direct OA link when available
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Remote sensing, Environmental science, Random forest, Inference, Leaf area index, Vegetation (pathology), Computer science, Geography, Ecology, Machine learning, Artificial intelligence, Biology, Pathology, MedicineTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2022: 1, 2021: 1Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.plots | 116 |
| abstract_inverted_index.stock | 203 |
| abstract_inverted_index.study | 59, 100, 124 |
| abstract_inverted_index.their | 168 |
| abstract_inverted_index.these | 26 |
| abstract_inverted_index.using | 241, 275 |
| abstract_inverted_index.Random | 158 |
| abstract_inverted_index.access | 3 |
| abstract_inverted_index.entire | 48, 151, 226, 238 |
| abstract_inverted_index.forest | 159, 208 |
| abstract_inverted_index.namely | 69 |
| abstract_inverted_index.period | 240 |
| abstract_inverted_index.random | 207, 242 |
| abstract_inverted_index.region | 101 |
| abstract_inverted_index.remote | 27, 35, 89, 284 |
| abstract_inverted_index.robust | 41 |
| abstract_inverted_index.scenes | 95 |
| abstract_inverted_index.season | 50, 120 |
| abstract_inverted_index.strong | 170 |
| abstract_inverted_index.summer | 119 |
| abstract_inverted_index.weekly | 109 |
| abstract_inverted_index.(FPAR), | 76 |
| abstract_inverted_index.(SPAD): | 262 |
| abstract_inverted_index.In-situ | 106 |
| abstract_inverted_index.Landsat | 92 |
| abstract_inverted_index.affects | 31 |
| abstract_inverted_index.allowed | 178 |
| abstract_inverted_index.because | 166 |
| abstract_inverted_index.between | 183 |
| abstract_inverted_index.content | 261 |
| abstract_inverted_index.dataset | 153 |
| abstract_inverted_index.forests | 163, 243 |
| abstract_inverted_index.fortify | 13 |
| abstract_inverted_index.growing | 49, 227 |
| abstract_inverted_index.in-situ | 152 |
| abstract_inverted_index.machine | 62 |
| abstract_inverted_index.methods | 64, 277 |
| abstract_inverted_index.period. | 228 |
| abstract_inverted_index.predict | 66 |
| abstract_inverted_index.respect | 188 |
| abstract_inverted_index.results | 269 |
| abstract_inverted_index.sensing | 28, 36, 285 |
| abstract_inverted_index.spatial | 23 |
| abstract_inverted_index.Germany. | 105 |
| abstract_inverted_index.Sentinel | 9 |
| abstract_inverted_index.Variable | 175 |
| abstract_inverted_index.absorbed | 73 |
| abstract_inverted_index.content, | 85 |
| abstract_inverted_index.datasets | 29, 286 |
| abstract_inverted_index.directly | 30 |
| abstract_inverted_index.example, | 230 |
| abstract_inverted_index.explicit | 169 |
| abstract_inverted_index.forests, | 216 |
| abstract_inverted_index.fraction | 71 |
| abstract_inverted_index.indices. | 144 |
| abstract_inverted_index.instance | 39 |
| abstract_inverted_index.learning | 63 |
| abstract_inverted_index.measures | 177 |
| abstract_inverted_index.methods, | 37 |
| abstract_inverted_index.purpose, | 147 |
| abstract_inverted_index.relation | 182 |
| abstract_inverted_index.sensing. | 90 |
| abstract_inverted_index.spectral | 140, 191 |
| abstract_inverted_index.temporal | 21 |
| abstract_inverted_index.yielded: | 244 |
| abstract_inverted_index.Classical | 206 |
| abstract_inverted_index.RMSE=4.9. | 266 |
| abstract_inverted_index.analysing | 180 |
| abstract_inverted_index.available | 97 |
| abstract_inverted_index.bi-weekly | 111 |
| abstract_inverted_index.collected | 112 |
| abstract_inverted_index.geometric | 54 |
| abstract_inverted_index.inference | 162, 215 |
| abstract_inverted_index.long-term | 282 |
| abstract_inverted_index.modelling | 220, 231 |
| abstract_inverted_index.optimized | 128 |
| abstract_inverted_index.potential | 273 |
| abstract_inverted_index.radiation | 75 |
| abstract_inverted_index.response, | 192 |
| abstract_inverted_index.satellite | 18 |
| abstract_inverted_index.24.03.2015 | 155 |
| abstract_inverted_index.Pomerania, | 104 |
| abstract_inverted_index.approaches | 199 |
| abstract_inverted_index.character. | 174 |
| abstract_inverted_index.explaining | 139 |
| abstract_inverted_index.importance | 176 |
| abstract_inverted_index.outreached | 210 |
| abstract_inverted_index.parameters | 45, 132, 186, 223, 233 |
| abstract_inverted_index.particular | 218 |
| abstract_inverted_index.prediction | 129 |
| abstract_inverted_index.predictive | 173 |
| abstract_inverted_index.regression | 209 |
| abstract_inverted_index.resolution | 24, 88 |
| abstract_inverted_index.retrieving | 42 |
| abstract_inverted_index.throughout | 117 |
| abstract_inverted_index.vegetation | 143, 239 |
| abstract_inverted_index.15.10.2015. | 157 |
| abstract_inverted_index.biophysical | 44, 67, 131, 185, 222, 232, 289 |
| abstract_inverted_index.chlorophyll | 84, 260 |
| abstract_inverted_index.conditional | 161, 214 |
| abstract_inverted_index.demonstrate | 270 |
| abstract_inverted_index.evolvement. | 204 |
| abstract_inverted_index.exploratory | 171 |
| abstract_inverted_index.parameters, | 68 |
| abstract_inverted_index.parameters. | 290 |
| abstract_inverted_index.performance | 195, 212 |
| abstract_inverted_index.resolution. | 55 |
| abstract_inverted_index.agricultural | 14 |
| abstract_inverted_index.applications | 15 |
| abstract_inverted_index.applicability | 33 |
| abstract_inverted_index.identification | 135 |
| abstract_inverted_index.interpretation | 280 |
| abstract_inverted_index.multi-frequent | 5, 283 |
| abstract_inverted_index.photosynthetic | 74 |
| abstract_inverted_index.high-resolution | 6 |
| abstract_inverted_index.machine-learning | 276 |
| abstract_inverted_index.Mecklenburg-Western | 103 |
| abstract_inverted_index.&lt;br&gt;&lt;br&gt; | 56, 122, 205, 267 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.24732686 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |