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Article

High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data

1
Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA
2
Granular, A Corteva AgriscienceTM Company, San Francisco, CA 94103, USA
3
Corteva AgriscienceTM, Wilmington, DE 19805, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3471; https://doi.org/10.3390/rs12213471
Received: 12 September 2020 / Revised: 12 October 2020 / Accepted: 19 October 2020 / Published: 22 October 2020
Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts. Here, we assess machine learning models’ capacity for soybean yield prediction using a unique ground-truth dataset of high-resolution (5 m) yield maps generated from combine harvester yield monitor data for over a million field-year observations across the Midwestern United States from 2008 to 2018. First, we compare random forest (RF) implementations, testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-m scale yield prediction. We find that Sentinel-2-based models can explain up to 45% of out-of-sample yield variability from 2017 to 2018 (r2 = 0.45), while Landsat models explain up to 43% across the longer 2008–2018 period. Using discrete Fourier transforms, or harmonic regressions, to capture soybean phenology improved the Landsat-based model considerably. Second, we compare RF models trained using this ground-truth data to models trained on available county-level statistics. We find that county-level models rely more heavily on just a few predictors, namely August weather covariates (vapor pressure deficit, rainfall, temperature) and July and August near-infrared observations. As a result, county-scale models perform relatively poorly on field-scale validation (r2 = 0.32), especially for high-yielding fields, but perform similarly to field-scale models when evaluated at the county scale (r2 = 0.82). Finally, we test whether our findings on variable importance can inform a simple, generalizable framework for regions or time periods beyond ground data availability. To do so, we test improvements to a Scalable Crop Yield Mapper (SCYM) approach that uses crop simulations to train statistical models for yield estimation. Based on findings from our RF models, we employ harmonic regressions to estimate peak vegetation index (VI) and a VI observation 30 days later, with August rainfall as the sole weather covariate in our new SCYM model. Modifications improved SCYM’s explained variance (r2 = 0.27 at the 30 m scale) and provide a new, parsimonious model. View Full-Text
Keywords: crop yields; yield mapping; US Corn Belt; Landsat; Sentinel; agricultural monitoring; machine learning crop yields; yield mapping; US Corn Belt; Landsat; Sentinel; agricultural monitoring; machine learning
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MDPI and ACS Style

Dado, W.T.; Deines, J.M.; Patel, R.; Liang, S.-Z.; Lobell, D.B. High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data. Remote Sens. 2020, 12, 3471. https://doi.org/10.3390/rs12213471

AMA Style

Dado WT, Deines JM, Patel R, Liang S-Z, Lobell DB. High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data. Remote Sensing. 2020; 12(21):3471. https://doi.org/10.3390/rs12213471

Chicago/Turabian Style

Dado, Walter T., Jillian M. Deines, Rinkal Patel, Sang-Zi Liang, and David B. Lobell 2020. "High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data" Remote Sensing 12, no. 21: 3471. https://doi.org/10.3390/rs12213471

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