Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images
Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/12/1942 |
| _version_ | 1797565100140265472 |
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| author | Robert F. Paul Yaping Cai Bin Peng Wendy H. Yang Kaiyu Guan Evan H. DeLucia |
| author_facet | Robert F. Paul Yaping Cai Bin Peng Wendy H. Yang Kaiyu Guan Evan H. DeLucia |
| author_sort | Robert F. Paul |
| collection | DOAJ |
| description | Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding of its impacts on crop growth and biogeochemical cycling. Satellite remote sensing offers a unique opportunity to observe and analyze this dynamic phenomenon at the landscape scale. Here we analyzed a series of red–green–blue (RGB) and near infrared (NIR) remote sensing images from the Planet Labs CubeSat constellation following a period of heavy precipitation in May 2017 to determine the spatiotemporal characteristics of ponding events in the maize–soybean cropland of Champaign County, Illinois USA. We trained Random Forest algorithms for near-daily images to create binary classifications of surface water versus none, which achieved kappa values around 0.9. We then analyzed the morphology of classification results for connected pixels across space and time and found that 2.5% (5180 ha) of this cropland was classified as water surface at some point during this period. The frequency distribution of areal ponding extent exhibited a log–log relationship; the mean and median areas of ponds were 1231 m<sup>2</sup> and 126 m<sup>2</sup>, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m<sup>2</sup>, and 2.5% of the ponds having an area greater than 104 m<sup>2</sup> (1 ha). Ponds lasted for a mean duration of 2.4 ± 1.7 days, and 2.3% of ponds lasted for more than a week. Our results suggest that transient ponding may be significant at the landscape scale and ought to be considered in assessments of crop risk, soil and water conservation, biogeochemistry, and sustainability. |
| first_indexed | 2024-03-10T19:08:13Z |
| format | Article |
| id | doaj.art-7f697bf9d41d408b80210427586b8d99 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:08:13Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7f697bf9d41d408b80210427586b8d992023-11-20T04:02:18ZengMDPI AGRemote Sensing2072-42922020-06-011212194210.3390/rs12121942Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat ImagesRobert F. Paul0Yaping Cai1Bin Peng2Wendy H. Yang3Kaiyu Guan4Evan H. DeLucia5Department of Plant Biology, University of Illinois, Urbana, IL 61801, USADepartment of Natural Resources and Environmental Science, University of Illinois, Urbana, IL 61801, USADepartment of Natural Resources and Environmental Science, University of Illinois, Urbana, IL 61801, USADepartment of Plant Biology, University of Illinois, Urbana, IL 61801, USADepartment of Natural Resources and Environmental Science, University of Illinois, Urbana, IL 61801, USADepartment of Plant Biology, University of Illinois, Urbana, IL 61801, USAClimate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding of its impacts on crop growth and biogeochemical cycling. Satellite remote sensing offers a unique opportunity to observe and analyze this dynamic phenomenon at the landscape scale. Here we analyzed a series of red–green–blue (RGB) and near infrared (NIR) remote sensing images from the Planet Labs CubeSat constellation following a period of heavy precipitation in May 2017 to determine the spatiotemporal characteristics of ponding events in the maize–soybean cropland of Champaign County, Illinois USA. We trained Random Forest algorithms for near-daily images to create binary classifications of surface water versus none, which achieved kappa values around 0.9. We then analyzed the morphology of classification results for connected pixels across space and time and found that 2.5% (5180 ha) of this cropland was classified as water surface at some point during this period. The frequency distribution of areal ponding extent exhibited a log–log relationship; the mean and median areas of ponds were 1231 m<sup>2</sup> and 126 m<sup>2</sup>, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m<sup>2</sup>, and 2.5% of the ponds having an area greater than 104 m<sup>2</sup> (1 ha). Ponds lasted for a mean duration of 2.4 ± 1.7 days, and 2.3% of ponds lasted for more than a week. Our results suggest that transient ponding may be significant at the landscape scale and ought to be considered in assessments of crop risk, soil and water conservation, biogeochemistry, and sustainability.https://www.mdpi.com/2072-4292/12/12/1942agricultureintense precipitationfloodinghigh spatiotemporal resolution analysis |
| spellingShingle | Robert F. Paul Yaping Cai Bin Peng Wendy H. Yang Kaiyu Guan Evan H. DeLucia Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images Remote Sensing agriculture intense precipitation flooding high spatiotemporal resolution analysis |
| title | Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images |
| title_full | Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images |
| title_fullStr | Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images |
| title_full_unstemmed | Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images |
| title_short | Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images |
| title_sort | spatiotemporal derivation of intermittent ponding in a maize soybean landscape from planet labs cubesat images |
| topic | agriculture intense precipitation flooding high spatiotemporal resolution analysis |
| url | https://www.mdpi.com/2072-4292/12/12/1942 |
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