Row crop and cover crop residue spectra from lab spectrometer and spaceborne PRISMA imagery, Maryland, USA.,2006; 2021-2022. U.S. Geological Survey Data Release.

This data release contains reflectance spectra of residue (senesced vegetation) for common row crops (corn, soybean, winter wheat) as well as diverse cover crops (cereals, legumes, brassicas) collected in the laboratory using Analytical Spectral Devices (ASD) spectrophotometers (n = 296) and collected in the field using the Itialian Space Agency's spaceborne PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectrometer (n = 65). The data release also contains biochemical trait concentrations (nitrogen, nonstructural carbohydrates, holocellulose, and lignin) from physical samples used to evaluate biochemical trait mapping of cash crop and cover crop residue. Data collection occurred at the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD, USA, and on the Eastern Shore of MD, USA, between 2006 and 2022.

The data, as well as the processes used to prepare and analyze them, are discussed in detail in a related interpretive summary:
Jennewein, J.S., W.D. Hively, B.T. Lamb, C.S.T. Daughtry, R. Thapa, A. Thieme, C. Reberg-Horton, and S. Mirsky. 2024. Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues. Precision Agriculture. https:/doi.org/xxx TBD

Contents:
1. Metadata - Row crop and cover crop residue spectra from lab spectrometer and spaceborne PRISMA imagery, Maryland, USA.xml : metadata file describing dataset parameters
2. FieldSpec4_ASD_mean_corrected_reflectance_spectra_cash_and_cover_crops.csv : comma delimited spreadsheet containing sample biochemical traits with ASD reflectance spectra collected in the lab
3. PRISMA_reflectance_spectra_smoothed_brightness_normalized_cash_and_cover_crops.csv : comma delimited spreadsheet containing sample biochemical traits with PRISMA spaceborne surface reflectance spectra that have been smoothed and brightness normalized associated with field sampling locations
 

Additional works cited in the metadatafile:

 Berger, K., Hank, T., Halabuk, A., Rivera-Caicedo, J. P., Wocher, M., Mojses, M., Gerhátová, K., Tagliabue, G., Dolz, M. M., Venteo, A. B. P., & Verrelst, J. (2021). Assessing non-photosynthetic cropland biomass from spaceborne hyperspectral imagery. Remote Sensing, 13(22), 1–20. https://doi.org/10.3390/rs13224711
Daughtry, C. S. T., Serbin, G., Iii, J. B. R., Doraiswamy, P. C., Raymond, E., & Jr, H. (2010). Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover. Remote Sensing, 2(2), 416–431. https://doi.org/10.3390/rs2020416
Feilhauer, H., Asner, G. P., Martin, R. E., & Schmidtlein, S. (2010). Brightness-normalized Partial Least Squares Regression for hyperspectral data. Journal of Quantitative Spectroscopy and Radiative Transfer, 111(12–13), 1947–1957. https://doi.org/10.1016/j.jqsrt.2010.03.007
Kokaly, R. F., & Skidmore, A. K. (2015). Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm. International Journal of Applied Earth Observation and Geoinformation, 43, 55–83. https://doi.org/10.1016/j.jag.2015.01.010
Marshall, M., Belgiu, M., Boschetti, M., Pepe, M., Stein, A., & Nelson, A. (2022). Field-level crop yield estimation with PRISMA and Sentinel-2. ISPRS Journal of Photogrammetry and Remote Sensing, 187(February), 191–210. https://doi.org/10.1016/j.isprsjprs.2022.03.008
Tagliabue, G., Boschetti, M., Bramati, G., Candiani, G., Colombo, R., Nutini, F., Pompilio, L., Rivera-caicedo, J. P., Rossi, M., Rossini, M., Verrelst, J., & Panigada, C. (2022). Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 187(February), 362–377. https://doi.org/10.1016/j.isprsjprs.2022.03.014