USDA CropScape

This article demonstrates a compact workflow for USDA CropScape (Cropland Data Layer) rasters.

This vignette runs its live workflow when rendered locally. The heavy download, processing, extraction, and plotting chunks are skipped automatically on CI, CRAN checks, and pkgdown builds; set AMADEUS_RUN_VIGNETTES=true to force live execution in those environments.

Available inputs and data availability

download_data(dataset_name = "cropscape", ...) accepts a year and source = "USDA" or "GMU".
CropScape is an annual 30 m Cropland Data Layer product: USDA downloads are available from 2008 through the latest release, while GMU extends back to 1997.
File formats depend on source: USDA serves yearly ZIP archives and GMU serves yearly .tar.gz archives.
CropScape values are categorical crop/land-use classes. Covariate calculation are a fraction of each land cover class within the specified radius or areal unit.

The table below reproduces the crop and related land-use classes listed in the official USDA CDL metadata used by CropScape; see the 2024 CDL metadata for the complete legend and additional class details.

CDL code	cropland class
1	Corn
2	Cotton
3	Rice
4	Sorghum
5	Soybeans
6	Sunflower
10	Peanuts
11	Tobacco
12	Sweet Corn
13	Pop or Orn Corn
14	Mint
21	Barley
22	Durum Wheat
23	Spring Wheat
24	Winter Wheat
25	Other Small Grains
26	Dbl Crop WinWht/Soybeans
27	Rye
28	Oats
29	Millet
30	Speltz
31	Canola
32	Flaxseed
33	Safflower
34	Rape Seed
35	Mustard
36	Alfalfa
37	Other Hay/Non Alfalfa
38	Camelina
39	Buckwheat
41	Sugarbeets
42	Dry Beans
43	Potatoes
44	Other Crops
45	Sugarcane
46	Sweet Potatoes
47	Misc Vegs & Fruits
48	Watermelons
49	Onions
50	Cucumbers
51	Chick Peas
52	Lentils
53	Peas
54	Tomatoes
55	Caneberries
56	Hops
57	Herbs
58	Clover/Wildflowers
59	Sod/Grass Seed
60	Switchgrass
61	Fallow/Idle Cropland
64	Shrubland
66	Cherries
67	Peaches
68	Apples
69	Grapes
70	Christmas Trees
71	Other Tree Crops
72	Citrus
74	Pecans
75	Almonds
76	Walnuts
77	Pears
92	Aquaculture
204	Pistachios
205	Triticale
206	Carrots
207	Asparagus
208	Garlic
209	Cantaloupes
210	Prunes
211	Olives
212	Oranges
213	Honeydew Melons
214	Broccoli
215	Avocados
216	Peppers
217	Pomegranates
218	Nectarines
219	Greens
220	Plums
221	Strawberries
222	Squash
223	Apricots
224	Vetch
225	Dbl Crop WinWht/Corn
226	Dbl Crop Oats/Corn
227	Lettuce
228	Dbl Crop Triticale/Corn
229	Pumpkins
230	Dbl Crop Lettuce/Durum Wht
231	Dbl Crop Lettuce/Cantaloupe
232	Dbl Crop Lettuce/Cotton
233	Dbl Crop Lettuce/Barley
236	Dbl Crop WinWht/Sorghum
237	Dbl Crop Barley/Corn
238	Dbl Crop WinWht/Cotton
239	Dbl Crop Soybeans/Cotton
240	Dbl Crop Soybeans/Oats
241	Dbl Crop Corn/Soybeans
242	Blueberries
243	Cabbage
244	Cauliflower
245	Celery
246	Radishes
247	Turnips
248	Eggplants
249	Gourds
250	Cranberries
254	Dbl Crop Barley/Soybeans

Download representative requests

directory_to_save <- file.path(tempdir(), "cropscape_workflow")

download_data(
  dataset_name = "cropscape",
  year = 2020,
  source = "USDA",
  directory_to_save = file.path(directory_to_save, "usda_2020"),
  acknowledgement = TRUE,
  unzip = TRUE
)

Process one workflow-ready data product

cropscape_path <- list.files(
  file.path(directory_to_save, "usda_2020"),
  pattern = "2020.*\\.tif$",
  recursive = TRUE,
  full.names = TRUE
)[1]

cropscape_reference <- terra::rast(cropscape_path)

print(cropscape_reference)

processed_data <- process_covariates(
  covariate = "cropscape",
  path = cropscape_path,
  year = 2020,
  extent = {
    ref_ext <- terra::ext(cropscape_reference)
    dx <- terra::xmax(ref_ext) - terra::xmin(ref_ext)
    dy <- terra::ymax(ref_ext) - terra::ymin(ref_ext)
    terra::ext(
      terra::xmin(ref_ext) + 0.35 * dx,
      terra::xmin(ref_ext) + 0.65 * dx,
      terra::ymin(ref_ext) + 0.35 * dy,
      terra::ymin(ref_ext) + 0.65 * dy
    )
  }
)
plot(processed_data, main = "Processed CropScape raster")

Calculate covariates at points

Note that if no radius is specified, the point extraction will return the value of the raster cell containing each point. In this case, the output columns are binary indicators of whether each CropScape class is present at that location. With a radius specified, the output columns are a fraction of each class within the circular buffer around each point, so values can range from 0 to 1 and multiple classes can be present at the same location with varying proportions.


domain_x <- c(terra::xmin(processed_data), terra::xmax(processed_data))
domain_y <- c(terra::ymin(processed_data), terra::ymax(processed_data))
domain_dx <- diff(domain_x)
domain_dy <- diff(domain_y)

candidate_xy <- expand.grid(
  lon = seq(domain_x[1] + 0.12 * domain_dx, domain_x[2] - 0.12 * domain_dx, length.out = 5),
  lat = seq(domain_y[1] + 0.12 * domain_dy, domain_y[2] - 0.12 * domain_dy, length.out = 5)
)
raster_crs <- sf::st_crs(terra::crs(processed_data))
if (is.na(raster_crs)) {
  stop("`processed_data` is missing CRS; cannot build extraction points.")
}
example_points_sf <- sf::st_as_sf(
  candidate_xy,
  coords = c("lon", "lat"),
  crs = raster_crs
)
example_points_sf$site_id <- paste0("site_", seq_len(nrow(example_points_sf)))


point_values <- calculate_covariates(
  covariate = "cropscape",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  radius = 0,
  geom = "sf"
)

print(point_values)

point_proportion_100m <- calculate_covariates(
  covariate = "cropscape",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  radius = 100,
  geom = "sf"
)

print(point_proportion_100m)

Kyle Messier, with assistance from GitHub Copilot

2026-05-20

Available inputs and data availability

Download representative requests

Process one workflow-ready data product

Calculate covariates at points