Data for: The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield
Published: 27 April 2020| Version 1 | DOI: 10.17632/5dgjpmjk85.1
Contributors:
Trenton Franz, Derek Heeren, Sayli Pokal, Hamed Gholizadeh, Daran Rudnick, Zhenong Jin, Fatima Tenorio, Yuzhen Zhou, Justin Gibson, John Gates, Matthew McCabe, Kaiyu Guan, Matteo Ziliani, Ming Pan, Brian WardlowDescription
Excel file contains the 3 supplemental tables in the manuscript. Table S1 contains individual tabs for each of the eight study sites (S1 to S8). Tables S2-S3 are individual tabs. Supplementary Table S1. 10 m resolution QA/QC data for each study site including: location, elevation, hydrogeophyscial surveys of EMI and neutron intensity, Landsat GCVI by year, crop yield by year, and First EOF of each covariate. Supplementary Table S2. Summary of MLR and RF fitting coefficients and statistical metrics by site and crop type. Supplementary Table S3. Summary of MLR and RF fitting coefficients and statistical metrics by site, crop type, and year. Supplementary R Code and results for sites S1 to S8 by crop.
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Categories
Agronomy, Hydrology, Remote Sensing