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Open AccessArticle

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

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Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
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College of Information Studies (iSchool), University of Maryland, College Park, MD 20742, USA
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NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
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NASA Goddard Space Flight Center Code 610, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
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National Agricultural Statistics Service, United States Department of Agriculture, 1400 Independence Ave SW, Washington, DC 20250, USA
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Physics of the Earth and Thermodynamics, University of Valencia, 46003 Valencia, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Enrico Borgogno Mondino
Remote Sens. 2021, 13(5), 872; https://doi.org/10.3390/rs13050872
Received: 29 January 2021 / Revised: 15 February 2021 / Accepted: 23 February 2021 / Published: 26 February 2021
Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields. View Full-Text
Keywords: agriculture; yield; within-field; corn; soybean; remote sensing; satellite; WorldView-3; planet; Sentinel-2; Landsat 8 agriculture; yield; within-field; corn; soybean; remote sensing; satellite; WorldView-3; planet; Sentinel-2; Landsat 8
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MDPI and ACS Style

Skakun, S.; Kalecinski, N.I.; Brown, M.G.L.; Johnson, D.M.; Vermote, E.F.; Roger, J.-C.; Franch, B. Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery. Remote Sens. 2021, 13, 872. https://doi.org/10.3390/rs13050872

AMA Style

Skakun S, Kalecinski NI, Brown MGL, Johnson DM, Vermote EF, Roger J-C, Franch B. Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery. Remote Sensing. 2021; 13(5):872. https://doi.org/10.3390/rs13050872

Chicago/Turabian Style

Skakun, Sergii; Kalecinski, Natacha I.; Brown, Meredith G.L.; Johnson, David M.; Vermote, Eric F.; Roger, Jean-Claude; Franch, Belen. 2021. "Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery" Remote Sens. 13, no. 5: 872. https://doi.org/10.3390/rs13050872

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