Row crop and cover crop residue spectra from lab spectrometer and spaceborne PRISMA imagery, Maryland, USA., 20080801; 20210101-20220531.

This data release contains reflectance spectra of residue (senesced vegetation) for common row crops (corn, soybean, winter wheat) and cover crops (cereals, legumes, brassicas). Two-hundred and ninety-six cash and cover crop spectra were collected in the laboratory using Analytical Spectral Devices (ASD) spectrophotometers. Sixty-five physical samples were collected in the field that pair with the Italian Space Agency's spaceborne PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectrometer. The data release also contains biochemical trait concentrations (i.e., nitrogen, nonstructural carbohydrates, holocellulose, and lignin) from physical samples used to evaluate biochemical trait mapping of cash and crop cover residue. Data collection occurred at the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD, USA or on the Eastern Shore of MD, USA between 2010 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/ 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 cash and cover crop 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 this 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., and 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., and 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., and 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., and 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., and 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., and 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

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  "010:12"
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  "geospatial"
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