Data Used
The input datasets are continuously updated:
Code | Human-readable title | License | DOI |
---|---|---|---|
wheat | soybean | corn | cotton.cum : cropscape |
Cumulative years [year] cover under cotton at 30 m resolution | |||
The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States for the period 2000–2022+. Citation: | |||
- Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Computers and Electronics in Agriculture, 84, 111-123. https://doi.org/10.1016/j.compag.2012.03.005 | Public Domain | https://dx.doi.org/10.15482/USDA.ADC/1227096 | |
lc : cropscape | Crops | ||
The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States for the period 2000–2022+. Citation: | |||
- Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Computers and Electronics in Agriculture, 84, 111-123. https://doi.org/10.1016/j.compag.2012.03.005 | |||
lc : glance30 | GLanCE land cover classes: Water, Ice/Snow, Developed, Barren/Sparsely Vegetated, Tree Cover, Shrublands, Herbaceous | ||
Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat. Currently seven (7) land cover classes are used. Citation: | |||
- Friedl, M. A., Woodcock, C. E., Olofsson, P., Zhu, Z., Loveland, T., Stanimirova, R., ... & Souza, C. (2022). Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat. Frontiers in Remote Sensing, 3. https://dx.doi.org/10.3389/frsen.2022.894571 | https://dx.doi.org/10.5067/MEaSUREs/GLanCE/GLanCE30.001 | ||
slope | hillshade / Northerness … : ned6 | ||
Slope [%] / Hillshade / Northerness … at 30 m resolution | Digital terrain parameters derived from the National Elevation Dataset (NED; https://www.usgs.gov/3d-elevation-program). | Public Domain | |
soil.carbon.density : usda.c729 | Soil carbon density [kg/m3] at various standard depths (0–30 cm, 30–100 cm) at 30 m resolution | ||
Soil Carbon Mapper predictions of soil carbon density based on 3D machine learning and publicly available training data from KSSL (https://www.nrcs.usda.gov/conservation-basics/natural-resource-concerns/soil/kellogg-soil-survey-laboratory-kssl) and NRCS (https://catalog.data.gov/dataset/ncss-soil-characterization-database). | Proprietary | ||
soil.type | Soil type maps (USDA sub-group) probability [%] | ||
Soil Carbon Mapper predictions of the sub-group USDA soil taxonomy soil types (+750) based on the NASIS + NRCS (https://catalog.data.gov/dataset/ncss-soil-characterization-database) training points. | Proprietary | ||
lc : rangeland | Rangeland biomass, cover fraction and NPP annual time-series at 30 m resolution | ||
Annual rangeland cover data at 30 m resolution provided by the Rangeland Analysis Platform (https://rangelands.app/). Citation: | |||
- Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. et al. (2021). Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution. http://dx.doi.org/10.1111/2041-210x.13564 | Public Domain | https://doi.org/10.5066/P95IQ4BT |