Multi-Temporal Crop Classification with HLS Imagery across CONUS

Dataset Description

Dataset Summary

This dataset contains temporal Harmonized Landsat-Sentinel imagery of diverse land cover and crop type classes across the Contiguous United States for the year 2022. The target labels are derived from USDA's Crop Data Layer (CDL). It's primary purpose is for training segmentation geospatial machine learning models.

Dataset Structure

HLS Scenes

Each GeoTIFF file located under hls/ covers a 224 x 224 pixel area at 30m spatial resolution. Each input satellite file contains 18 bands including 6 spectral bands for three time steps stacked together. The file name structure is chip_XXX_YYY_merged.tif where XXX and YYY refer to row and column of a tile grid we imposed on the CDL data layer. Since the dataset is sampled from this country-wide grid not all XXXs and YYYs are present in the dataset. So you need to load the train_data.txt and validation_data.txt to get the list of all chips in the dataset.

CDL Labels

Each GeoTIFF file located under masks/ covers the same area as the corresponding HLS chips and contains one band with the target classes for each pixel. The file name structure is chip_XXX_YYY.mask.tif.

Band Order

In each HLS input GeoTIFF the following bands are repeated three times for three observations throughout the growing season:

Channel	Name	HLS S30 Band number
1	Blue	B02
2	Green	B03
3	Red	B04
4	NIR	B8A
5	SW 1	B11
6	SW 2	B12

Masks are a single band with the following values:

Value	Class Name
0	No Data
1	Natural Vegetation
2	Forest
3	Corn
4	Soybeans
5	Wetlands
6	Developed/Barren
7	Open Water
8	Winter Wheat
9	Alfalfa
10	Fallow/Idle Cropland
11	Cotton
12	Sorghum
13	Other

Data Splits

The 3,854 chips have been randomly split into training (80%) and validation (20%) with corresponding ids recorded in cvs files train_data.txt and validation_data.txt.

Dataset Creation

Query and Scene Selection

First, a set of 5,000 chips was defined based on samples from the USDA CDL to ensure a representative sampling across the CONUS. Next, for each chip, the corresponding HLS S30 scenes between March and September 2022 were queried, and scenes with low cloud cover were retrieved. Then, three scenes are selected among the low cloudy scenes to ensure a scene from early in the season, one in the middle, and one toward the end. The three final scenes were then reprojected to CDL's projection grid (EPSG:5070) using bilinear interpolation.

Chip Generation

In the final step, the three scenes for each chip were clipped to the bounding box of the chip, and 18 spectral bands were stacked together. In addition, a quality control was applied to each chip using the Fmask layer of the HLS dataset. Any chip containing clouds, cloud shadow, adjacent to cloud or missing values were discarded. This resulted in 3,854 chips.

License and Citation

This dataset is published under a CC-BY-4.0 license. If you find this dataset useful for your application, you can cite it as following:

@misc{hls-multi-temporal-crop-classification,
    author = {Cecil, Michael and Kordi, Fatemeh and Li, Hanxi (Steve) and Khallaghi, Sam and Alemohammad, Hamed},
    doi    = {10.57967/hf/0955},
    month  = aug,
    title  = {{HLS Multi Temporal Crop Classification}},
    year   = {2023}
}

Contact

For any questions about the dataset, you can contact Dr. Hamed Alemohammad.

Funding

This dataset is generated with funding from a grant awarded to Clark University Center for Geospatial Analytics (CGA) by NASA.