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

SMAP Level 1C Sigma Naught High Res Product Metadata

The dataset provides a near real time estimation of 2020 herbaceous mostly annual fractional cover predicted on July 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P96PVZIF., Boyte et al. 2018 https://doi.org/10.5066/P9RIV03D.), but starting this year we are mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). This dataset was generated using in situ observations from Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; weekly composites of harmonized Landsat and Sentinel-2 (HLS) data (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation, remotely sensed, and geophysical drivers. These data were integrating into regression tree (RT) models for prediction of weekly cloud free Normalized Difference Vegetation Index (NDVI). A total 11,065 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold RT models using a cross-validation approach (each observation was used as test data once). Cheatgrass (Bromus tectrorum) is the most common species, however, a number of other species were included in this study: Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and Taeniatherum caput-medusae. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2700-m elevation to target areas of unlikely substantial annual grass cover. To target likely sagebrush ecosystems, the mask also removed pixels classified as something other than shrub or grassland/herbaceous by the 2016 National Land Cover Dataset (NLCD).

The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on May 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P9ZEK5M1., Boyte et al. 2018 https://doi.org/10.5066/P9KSR9Z4.), but we are now mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). This dataset was generated using in situ observations from Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; weekly composites of harmonized Landsat and Sentinel-2 (HLS) data (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation, remotely sensed, and geophysical drivers. These data were integrating into regression tree (RT) models for prediction of weekly cloud free Normalized Difference Vegetation Index (NDVI). A total 11,002 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold RT models using a cross-validation approach (each observation was used as test data once). Cheatgrass (Bromus tectrorum) is the most common species, however, number of other species were included in this study: Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and Taeniatherum caput-medusae. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2700-m elevation to target areas of unlikely substantial annual grass cover. To target likely sagebrush ecosystems, the mask also removed pixels classified as something other than shrub or grassland/herbaceous by the 2016 National Land Cover Dataset (NLCD).

This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.