Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection Article Swipe
Feng Gao
,
Martha C. Anderson
,
Rasmus Houborg
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16224145
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16224145
Crop emergence is critical for crop growth modeling, crop condition monitoring, and crop yield estimation. Ground collections of crop emergence dates are time-consuming and can only include limited fields. Remote sensing time series have been used to detect crop emergence. However, the impacts of the temporal and spatial resolutions of these time series on crop emergence detection have not been thoroughly evaluated. This paper assesses corn and soybean emergence detection using various remote sensing datasets (i.e., VENµS, Planet Fusion, Sentinel-2, Landsat, and Harmonized Landsat and Sentinel-2 (HLS)) with diverse spatial and temporal resolutions. The green-up dates from the remote sensing time series are detected using the within-season emergence (WISE) algorithm and assessed using ground emergence observations and planting records of corn, soybeans, and alfalfa from the Beltsville Agricultural Research Center (BARC) fields in Maryland, USA, from 2019 to 2023. Our results showed that most emergence events (~95%) could be detected when the frequency of usable observations reached ten days or less. Planet Fusion captured all crop emergences and outperformed other datasets, with a mean difference (MD) of <1 day, a mean absolute difference (MAD) of <5 days, and a root mean square error (RMSE) of <6 days compared to the ground-observed emergence dates. The HLS and Sentinel-2 time series captured most emergences of corn and soybeans with MD < 3 days, MAD < 7 days, and RMSE < 9 days. Landsat detected less than half of the crop emergences in recent years when both Landsat-8 and -9 were available. In our study area, temporal revisit plays a more crucial role in emergence detection than spatial resolution. Spatial resolutions from 5 to 30 m are suitable for field-level summaries in the study area. However, the 30 m HLS lacked sub-field details in fields with mixed cropping systems. The findings from this study could benefit remotely sensed crop emergence detection from local to regional scales.
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404142644Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/rs16224145Digital Object Identifier
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Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence DetectionWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-07Full publication date if available
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Feng Gao, Martha C. Anderson, Rasmus HouborgList of authors in order
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https://doi.org/10.3390/rs16224145Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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Remote sensing, Environmental science, GeographyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fields | 131, 291 |
| abstract_inverted_index.ground | 113 |
| abstract_inverted_index.growth | 6 |
| abstract_inverted_index.lacked | 287 |
| abstract_inverted_index.recent | 240 |
| abstract_inverted_index.remote | 72, 98 |
| abstract_inverted_index.sensed | 304 |
| abstract_inverted_index.series | 32, 52, 101, 208 |
| abstract_inverted_index.showed | 141 |
| abstract_inverted_index.square | 191 |
| abstract_inverted_index.usable | 154 |
| abstract_inverted_index.Fusion, | 78 |
| abstract_inverted_index.Landsat | 83, 230 |
| abstract_inverted_index.Spatial | 266 |
| abstract_inverted_index.VENµS, | 76 |
| abstract_inverted_index.alfalfa | 123 |
| abstract_inverted_index.benefit | 302 |
| abstract_inverted_index.crucial | 258 |
| abstract_inverted_index.details | 289 |
| abstract_inverted_index.diverse | 88 |
| abstract_inverted_index.fields. | 28 |
| abstract_inverted_index.impacts | 42 |
| abstract_inverted_index.include | 26 |
| abstract_inverted_index.limited | 27 |
| abstract_inverted_index.reached | 156 |
| abstract_inverted_index.records | 118 |
| abstract_inverted_index.results | 140 |
| abstract_inverted_index.revisit | 254 |
| abstract_inverted_index.scales. | 312 |
| abstract_inverted_index.sensing | 30, 73, 99 |
| abstract_inverted_index.soybean | 67 |
| abstract_inverted_index.spatial | 47, 89, 264 |
| abstract_inverted_index.various | 71 |
| abstract_inverted_index.However, | 40, 282 |
| abstract_inverted_index.Landsat, | 80 |
| abstract_inverted_index.Research | 128 |
| abstract_inverted_index.absolute | 181 |
| abstract_inverted_index.assessed | 111 |
| abstract_inverted_index.assesses | 64 |
| abstract_inverted_index.captured | 163, 209 |
| abstract_inverted_index.compared | 197 |
| abstract_inverted_index.critical | 3 |
| abstract_inverted_index.cropping | 294 |
| abstract_inverted_index.datasets | 74 |
| abstract_inverted_index.detected | 103, 149, 231 |
| abstract_inverted_index.findings | 297 |
| abstract_inverted_index.green-up | 94 |
| abstract_inverted_index.planting | 117 |
| abstract_inverted_index.regional | 311 |
| abstract_inverted_index.remotely | 303 |
| abstract_inverted_index.soybeans | 215 |
| abstract_inverted_index.suitable | 274 |
| abstract_inverted_index.systems. | 295 |
| abstract_inverted_index.temporal | 45, 91, 253 |
| abstract_inverted_index.Landsat-8 | 244 |
| abstract_inverted_index.Maryland, | 133 |
| abstract_inverted_index.algorithm | 109 |
| abstract_inverted_index.condition | 9 |
| abstract_inverted_index.datasets, | 170 |
| abstract_inverted_index.detection | 56, 69, 262, 307 |
| abstract_inverted_index.emergence | 1, 19, 55, 68, 107, 114, 144, 201, 261, 306 |
| abstract_inverted_index.frequency | 152 |
| abstract_inverted_index.modeling, | 7 |
| abstract_inverted_index.soybeans, | 121 |
| abstract_inverted_index.sub-field | 288 |
| abstract_inverted_index.summaries | 277 |
| abstract_inverted_index.Beltsville | 126 |
| abstract_inverted_index.Harmonized | 82 |
| abstract_inverted_index.Sentinel-2 | 85, 206 |
| abstract_inverted_index.available. | 248 |
| abstract_inverted_index.difference | 174, 182 |
| abstract_inverted_index.emergence. | 39 |
| abstract_inverted_index.emergences | 166, 211, 238 |
| abstract_inverted_index.evaluated. | 61 |
| abstract_inverted_index.thoroughly | 60 |
| abstract_inverted_index.Sentinel-2, | 79 |
| abstract_inverted_index.collections | 16 |
| abstract_inverted_index.estimation. | 14 |
| abstract_inverted_index.field-level | 276 |
| abstract_inverted_index.monitoring, | 10 |
| abstract_inverted_index.resolution. | 265 |
| abstract_inverted_index.resolutions | 48, 267 |
| abstract_inverted_index.Agricultural | 127 |
| abstract_inverted_index.observations | 115, 155 |
| abstract_inverted_index.outperformed | 168 |
| abstract_inverted_index.resolutions. | 92 |
| abstract_inverted_index.within-season | 106 |
| abstract_inverted_index.time-consuming | 22 |
| abstract_inverted_index.ground-observed | 200 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100362401 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I1312222531, https://openalex.org/I1336096307 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.94138539 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |