The file “CropTypeDatasetProcessed.RData” contains the following four objects:

1.	France_crop_type_df
2.	Kenya_crop_type_df
3. 	feature_names
4.	OccitanieFrance_cropType_dist_fromGovStatistics

For users who do not have R, those four objects can alternatively be found in the following four .csv files:

1.	France_crop_type_data.csv
2.	Kenya_crop_type_data.csv
3. 	feature_names.csv
4.	OccitanieFrance_cropType_dist_fromGovStatistics.csv

We describe the contents of each of the four objects below:

1. France crop type dataset: Each row of the dataset corresponds to the centroid of a different agricultural land parcel (see Section 3.1.1 of Kluger et. al (2021) for details). The first 70 columns are the processed features titled X1,…,X70 (see feature names for details on each processed feature). Column 71 includes the crop type label. There are 6 crop type labels in this processed dataset (see Section 3.1.1 of Kluger et. al, for details on how some crop types were combined into the same class). The last column gives the department (or region) within Occitanie, France (see Figure 1 of Kluger et. al (2021) for a map of Occitanie, France and the labelled data).

2. Kenya crop type dataset: Each row of the dataset corresponds to a different agricultural pixel (see Section 3.1.2 of Kluger et. al (2021) for details). The first 70 columns are the processed features titled X1,…,X70 (see feature names for details on each processed feature). Column 71 includes the crop type label. There are 9 crop type labels in this dataset. The last column gives the region within Western Province, Kenya (see Figure 1 of Kluger et. al (2021) for a map of Western Province, Kenya and the labelled data).

3. Feature names: feature_names provides names for each of the processed features X1,…,X70. To understand what the each of the 70 feature names mean, we briefly describe how the raw datasets were processed. Sentinel-2 has 13 spectral bands (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR#bands) for capturing electromagnetic radiation at different frequencies. The 13 bands we used (from highest to lowest frequency) are called Aerosol, Blue, Green, Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, Red Edge 4, Water Vapor, Cirrus, SWIR 1 and SWIR 2.  For each location in the dataset, our raw data was a time series of the 13 spectral bands. We also constructed a time series of a vegetation index called GCVI, which is given by NIR/GREEN -1 (see Section 3.4.1 of Kluger et. al (2021)). Because many observations within the time series were corrupted by cloud cover, we removed observations on days that were thought to be corrupted by cloud cover, snow, water, or shadow cover (see Section 3.4.2 of Kluger et. al (2021)), and subsequently, we fit harmonic regressions for each of the 14 filtered time series in each sampled location (see Section 3.4.3 of Kluger et. al (2021)). Each harmonic regression had 5 coefficients. The 14 x 5=70 features consist of the estimated coefficients for the harmonic regressions fit to the 14 filtered time series. The names of the features are based on the notation introduced in Section 3.4.3 of Kluger et. al (2021). In particular the ‘c’ stands for the intercept estimated via harmonic regression. ‘a1’ and ‘a2’ stand for the coefficients for the cosine terms with frequencies of 1 and 2 years^{-1}, respectively, estimated via harmonic regression. ‘b1’ and ‘b2’ stand for the coefficients for the sine terms with frequencies of 1 and 2 years^{-1}, respectively, estimated via harmonic regression. In feature_names, the name of the spectral band (or vegetation index) whose time series was used in the harmonic regression is displayed alongside the coefficient identifiers ‘c’,’a1’,’a2’,’b1’, and ’b2’.

4. Crop type label distribution in each department: This table gives estimates of the distribution of the crop types labels within each department of Occitanie, France. The estimates are made using aggregate-level government statistics and do not use the labelled data (see Section 3.2 of Kluger et. al (2021)). For users who are interested in studying domain adaptation methods designed for settings where the distribution of the labels in the target domain are not known a priori, we recommend ignoring this table.

Reference: Kluger D.M., Wang S., and Lobell, D.B. (2021). Two shifts for crop mapping: leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sensing of Environment. 262, 112488.

The reference can also be found at: https://arxiv.org/abs/2109.01246.