Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the cont...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/1957 |
| _version_ | 1827671750346801152 |
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| author | Chunyuan Diao Geyang Li |
| author_facet | Chunyuan Diao Geyang Li |
| author_sort | Chunyuan Diao |
| collection | DOAJ |
| description | Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization. |
| first_indexed | 2024-03-10T03:46:13Z |
| format | Article |
| id | doaj.art-bf83d83510724d438dc43717c34ca102 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:46:13Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bf83d83510724d438dc43717c34ca1022023-11-23T09:08:26ZengMDPI AGRemote Sensing2072-42922022-04-01149195710.3390/rs14091957Near-Surface and High-Resolution Satellite Time Series for Detecting Crop PhenologyChunyuan Diao0Geyang Li1Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USADepartment of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USADetecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization.https://www.mdpi.com/2072-4292/14/9/1957crop phenologyPhenoCamPlanetScopehigh resolutionnear-surface |
| spellingShingle | Chunyuan Diao Geyang Li Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology Remote Sensing crop phenology PhenoCam PlanetScope high resolution near-surface |
| title | Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology |
| title_full | Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology |
| title_fullStr | Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology |
| title_full_unstemmed | Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology |
| title_short | Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology |
| title_sort | near surface and high resolution satellite time series for detecting crop phenology |
| topic | crop phenology PhenoCam PlanetScope high resolution near-surface |
| url | https://www.mdpi.com/2072-4292/14/9/1957 |
| work_keys_str_mv | AT chunyuandiao nearsurfaceandhighresolutionsatellitetimeseriesfordetectingcropphenology AT geyangli nearsurfaceandhighresolutionsatellitetimeseriesfordetectingcropphenology |