Remote sensing data for crop yield in CONUS

<strong>I) SUMMARY</strong> This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from USDA-NASS for years 2015–2018 at a county level, and collocated time series for the following variables: Enhanced Vegetation Index (EVI) from MODIS satellite (MOD13C1 v6 product) Soil Moisture (SM) from SMAP satellite through MT-DCA algorithm Vegetation Optical Depth (VOD) from SMAP satellite through MT-DCA algorithm Maximum temperature (TMAX) from Daymet v3 Precipitation (PRCP) from Daymet v3 <strong>II) CONTACT</strong> For questions, please email Laura Martínez-Ferrer at [e-mail] <strong>III) DATABASE</strong> For each crop type, we provided CSV files containing the time series of the variables and yield described above. Furthermore, additional information for spatial and temporal identification such as a county identifier and a year are included. Lastly, country-shapefiles (.shp) are added for geospatial representation. Further details in readme.txt file. <strong>IV) CITE</strong> We kindly encourage to cite the following works if this database is used L. Martínez-Ferrer, M. Piles, G. Camps-Valls, Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geoscience and Remote Sensing Letters, 2020, vol. 18, no 12, p. 2043-2047, DOI: 10.1109/LGRS.2020.3016140 A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Muñoz-Marí, A. Pérez-Suay and G. Camps-Valls, "Interpretable Long-Short Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2023.3244064