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Article

Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images

1
Department of Plant Biology, University of Illinois, Urbana, IL 61801, USA
2
Department of Natural Resources and Environmental Science, University of Illinois, Urbana, IL 61801, USA
3
National Center for Supercomputing Applications, University of Illinois, Urbana, IL 61801, USA
4
Institute for Sustainability, Energy, and Environment, University of Illinois, Urbana, IL 61801, USA
5
Department of Geology, University of Illinois, Urbana, IL 61801, USA
6
Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1942; https://doi.org/10.3390/rs12121942
Received: 28 April 2020 / Revised: 4 June 2020 / Accepted: 11 June 2020 / Published: 16 June 2020
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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 m2 and 126 m2, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m2, and 2.5% of the ponds having an area greater than 104 m2 (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. View Full-Text
Keywords: agriculture; intense precipitation; flooding; high spatiotemporal resolution analysis agriculture; intense precipitation; flooding; high spatiotemporal resolution analysis
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MDPI and ACS Style

Paul, R.F.; Cai, Y.; Peng, B.; Yang, W.H.; Guan, K.; DeLucia, E.H. Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images. Remote Sens. 2020, 12, 1942. https://doi.org/10.3390/rs12121942

AMA Style

Paul RF, Cai Y, Peng B, Yang WH, Guan K, DeLucia EH. Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images. Remote Sensing. 2020; 12(12):1942. https://doi.org/10.3390/rs12121942

Chicago/Turabian Style

Paul, Robert F., Yaping Cai, Bin Peng, Wendy H. Yang, Kaiyu Guan, and Evan H. DeLucia 2020. "Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images" Remote Sensing 12, no. 12: 1942. https://doi.org/10.3390/rs12121942

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