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 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 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 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.

,The 2000 Calaveras County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Central District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and Central District. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using developed photoquads. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method were not collected for this survey. 5. Not all land use codes will be represented in the survey.,

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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