This data release contains spectra used to evaluate narrow-band shortwave infrared indices suitable for measurement of non-photosynthetic vegetation (NPV). The original data were collected using a proximal Analytical Spectral Devices(ASD) FieldSpecPro spectroradiometer, and are also provided in various states of processing, all of which is described in the manuscript referenced below. Items 1-9 include spectra, items 10-12 include statistical descriptions of correlation goodness of fit between derived indices and fractional NPV cover, and item 13 contains estimated Landsat Next sensor radiometric properties. Item 14 includes spectra added to version 1.1 of this data release which corresponds to a closely related 2022 research effort and publication. The data provided here, and the processes used to calculate and analyze them, are further discussed in Hively, W.D., Lamb, B.T., Daughtry, C.S.T., Serbin, G., Dennison, P., Kokaly, R.F., Wu, Z., and Masek, J., 2021. Evaluation of SWIR recommended crop residue bands for the Landsat Next mission. Remote Sensing, 13, 18, 3718. https://doi.org/10.3390/rs13183718. Additional spectra processing techniques for item 14 are discussed in an additional publication by Lamb, B.T., Dennison, P., Hively, W.D., Kokaly, R.F., Serbin, G., Wu, Z., Dabney, P., Masek, J.G., Campbell, M., Daughtry, C.S.T. 2022. Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue Characterization. Remote Sensing (in review). Contents 1 ASD spectra for agricultural targets.csv 2 Gaussian spectra for agricultural targets.csv 3 Gaussian atm spectra for agricultural targets.csv 4 Boxcar spectra for agricultural targets.csv 5 Crop residue spectra.csv 6 Soil spectra.csv 7 MODTRAN mean reflectance and calculated radiance.csv 8 Gaussian spectra for shrubs and grassland targets.csv 9 Gaussian atm spectra for shrubs and grassland targets.csv 10 Index gaussian correlation output for agricultural targets.csv 11 Index boxcar correlation output for agricultural targets.csv 12 Index correlation output for shrubs and grassland targets.csv 13 Sensor radiometric properties.csv 14 Boxcar 1 nm interval spectra for agricultural targets.csv

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

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

NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the “Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US)” product was produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with funding from several USGS programs (National Land Imaging and National Water-Quality Assessment). A primary objective was to identify, and map irrigated agricultural areas to factor into water quality studies and drought monitoring investigations. This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask for agricultural lands derived from NLCD to map the spatial distribution of irrigated lands across the conterminous United States. The MIrAD Version 4 offers the datasets for the years 2002, 2007, 2012, and 2017 at 250-m and 1-km spatial resolutions. The validation of MIrAD-US is a challenge because no other single-source current datasets are available at a national scale for comparison. Thus, this dataset should be considered provisional until a formal accuracy assessment can be completed. The product update is planned for every 5 years, synchronized with the update of the Census of Agriculture by the U.S Department of Agriculture (USDA) but contingent upon availability of Collection 6 (C6) Aqua eMODIS data and funding.

NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the “Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US)” product was produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with funding from several USGS programs (National Land Imaging and National Water-Quality Assessment). A primary objective was to identify, and map irrigated agricultural areas to factor into water quality studies and drought monitoring investigations. This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask for agricultural lands derived from NLCD to map the spatial distribution of irrigated lands across the conterminous United States. The MIrAD Version 4 offers the datasets for the years 2002, 2007, 2012, and 2017 at 250-m and 1-km spatial resolutions. The validation of MIrAD-US is a challenge because no other single-source current datasets are available at a national scale for comparison. Thus, this dataset should be considered provisional until a formal accuracy assessment can be completed. The product update is planned for every 5 years, synchronized with the update of the Census of Agriculture by the U.S Department of Agriculture (USDA) but contingent upon availability of Collection 6 (C6) Aqua eMODIS data and funding.

This dataset provides full-spectrum (350-2500 nm) reflectance measurements of dried ground leaf samples from meadow, shrub, and tree sites. Samples were collected during the period of February 23, 2022 to September 27th, 2022 for the 2022 NASA Surface Biology Geology (SBG) High Frequency Time series (SHIFT) campaign in Santa Barbara County, California, USA. The primary goal of the SHIFT campaign was to collect a repeated dense time series of airborne Visible to ShortWave Infrared (VSWIR) airborne imaging spectroscopy data with coincident field measurements in both inland terrestrial and coastal aquatic areas. Reflectance measurements were collected using a ASD FieldSpec 3 spectrometer following Serbin et al. (2014) and Wang et al. (2020). After sample collection, each leaf sample was divided into two portions: one portion with ~10 g fresh weight was oven dried and another portion with ~5 g fresh weight was flash frozen. Both samples were ground and homogenized (20-mesh, 833 micrometers). The oven dried samples were then re-dried in oven at 70 degrees C for 24 h and the flash frozen samples were dried using a Virtis Model 24DX48 specimen freeze dryer before the reflectance measurement. Data are in a comma-separated values (.csv) format.

This data release contains processed scan data from the Active Canopy Sensor (ACS) -214 proximal sensing instrument associated with 1242 cover crop biomass samples from 2020 - 2024 across 13 states. The ACS-214 is an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor. These data were collected as a part of the Precision Sustainable Agriculture’s national on-farm network, with two main hubs located in Maryland and North Carolina. The ACS-214 data included in this data release are summarized by plot polygons or 10m circular buffer to generate the metrics used to estimate cover crop biomass. The data, as well as the processes used to prepare and analyze them, are discussed in detail in a related interpretive summary: Jennewein, et al., 2025. Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions. Accepted in Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.101201 Contents: 1.Metadata file titled - 1 Proximal remote sensing data supporting on-farm measurement of cover crop performance.xml : metadata file describing dataset parameters 2.Comma delimited file of data titled - ACS-214_summarized_outputs_and_cover_crop_biomass_and_sample_details.csv: comma delimited spreadsheet containing cover crop biomass, sampling date, species, state sampled in, and summarized ACS-214 outputs.

This dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska. Reflectance measurements were collected at nearly weekly intervals through the growing seasons of 2000 to 2002 to describe characteristics of green-up, peak growth, and senescence. Reflectance measurements were also collected once near peak growth in 2022. Ancillary measurements were collected at intervals through the 2001 and 2002 growing seasons.

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113C2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C2 product: EVI and NDVI.