Here we provide Germany’s Deutsches Zentrum fu¨r Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) and Italy’s ASI’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) Derived Spectral Library of Agricultural Crops for California. It was developed using DESIS and PRISMA hyperspectral data acquired in August 2021 near Fresno, CA. The 4-bin DESIS image has 60 bands with approximately 10 nm bandwidths from 400 to 1000 nm and is available through the German Aerospace Center and Teledyne Brown (2022). The PRISMA image has 235 bands with 10-12 nm bandwidths from 400-2500 nm and is available through the Italian Space Agency (ASI) (2022). In these PRISMA data, two bands in the overlapping spectral region of the visible-near infrared sensor and the shortwave infrared sensor were removed, for a total of 233 bands. Both images were downloaded as surface reflectance products. Some DESIS bands contain negative values; the user can choose to discard these bands. The crop type data and confidence layer for the year 2021 can be accessed through the USDA National Agricultural Statistics Service (2022). The DESIS and PRISMA Derived Spectral Library of Agricultural Crops dataset characteristics are described below, with DESIS and PRISMA data provided in two separate CSV files. Each dataset contains information for 16,581 samples located where the two images overlap. The user should examine the data against the USDA NASS CDL 2021 layer and decide whether some samples should be discarded due to uncertainty in the crop class label.

Here we provide information for the DESIS and PRISMA Derived Spectral Library of Agricultural Crops in California which was developed using DESIS and PRISMA hyperspectral data acquired for 2020. The DESIS images used for this dataset are available through the German Aerospace Center and Teledyne Brown (2022). PRISMA images are available through the Italian Space Agency (ASI) (2022). The crop type data and confidence layer for the year 2020 can be accessed through the USDA National Agricultural Statistics Service (2022). The DESIS and PRISMA Derived Spectral Library of Agricultural Crops dataset characteristics are described below, with DESIS and PRISMA data provided in two separate CSV files. Related Primary Publication: Aneece, I.P., and Thenkabail, P.S., 2022, New generation hyperspectral sensor (DESIS and PRISMA) performances in agriculture.

Here we provide information for the PlanetScope and d Deutsches Zentrum fur Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) Derived Spectral Library of Agricultural Crops in California which was developed using PlanetScope Dove-R high spatial resolution data and DESIS hyperspectral data acquired for 2020. PlanetScope images are available through Planet Labs (2022). The DESIS images used for this dataset are available through the German Aerospace Center and Teledyne Brown (2022). The crop type data and confidence layer for 2020 can be accessed through the United States Department of Agriculture National Agricultural Statistics Service (2022). The PlanetScope and DESIS Derived Spectral Library of Agricultural Crops dataset characteristics are described below, with PlanetScope and DESIS data provided in two separate CSV files. Related Primary Publication: Aneece, I., Foley, D., Thenkabail, P.S., Oliphant, A., and Teluguntla, P. 2022. New generation hyperspectral data from DESIS compared and contrasted with hyperspatial resolution PlanetScope data for crop type classification. DOI: https://doi.org/10.5066/xxxxxxxx.

Here we provide information for the PlanetScope and d Deutsches Zentrum fur Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) Derived Spectral Library of Agricultural Crops in California which was developed using PlanetScope Dove-R high spatial resolution data and DESIS hyperspectral data acquired for 2020. PlanetScope images are available through Planet Labs (2022). The DESIS images used for this dataset are available through the German Aerospace Center and Teledyne Brown (2022). The crop type data and confidence layer for 2020 can be accessed through the United States Department of Agriculture National Agricultural Statistics Service (2022). The PlanetScope and DESIS Derived Spectral Library of Agricultural Crops dataset characteristics are described below, with PlanetScope and DESIS data provided in two separate CSV files. Related Primary Publication: Aneece, I., Foley, D., Thenkabail, P.S., Oliphant, A., and Teluguntla, P. 2022. New generation hyperspectral data from DESIS compared and contrasted with hyperspatial resolution PlanetScope data for crop type classification. DOI: https://doi.org/10.5066/xxxxxxxx.

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This dataset is support materials for the publication "Crop type classification, trends, and patterns of central California agricultural fields from 2005 – 2020". This data release is comprised of two child datasets. The first dataset, 'Labeled_CropType_Points', is a shapefile that consists of randomly selected point locations in which crop types were verified using high resolution imagery for each examined year across the study period (2005 - 2020). The second dataset, 'Central_CA_Classified_Croplands', is also a shapefile, but contains polygons of 9 classified crop types derived from a random forest machine learning classifier for central California for each examined year across the study period (2005 - 2020).

This dataset is support materials for the publication "Crop type classification, trends, and patterns of central California agricultural fields from 2005 – 2020". This data release is comprised of two child datasets. The first dataset, 'Labeled_CropType_Points', is a shapefile that consists of randomly selected point locations in which crop types were verified using high resolution imagery for each examined year across the study period (2005 - 2020). The second dataset, 'Central_CA_Classified_Croplands', is also a shapefile, but contains polygons of 9 classified crop types derived from a random forest machine learning classifier for central California for each examined year across the study period (2005 - 2020).

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