Crop Residue Cover In-Field Photographs, WorldView-3 Spectral Indices, and Derived Residue Maps for Maryland, USA, 2015-2022
April 21, 2025
This data release supports the publication in the Soil and Tillage Research journal titled "Multiyear Crop Residue Cover Mapping Using Narrow-Band vs. Broad-Band Shortwave Infrared Satellite Imagery" by Lamb and others (2025) (https://doi.org/10.1016/j.still.2025.106524)
The dataset includes field sampling data comprising in-field nadir photographs collected from a collaborating farm in Talbot County, Maryland, during the spring of 2015, 2016, 2017, 2019, 2021, and 2022. These photographs were classified for fractional ground cover, specifically green vegetation, crop residue, and bare soil, utilizing SamplePoint software. The dates of photograph collection align with the acquisition of WorldView-3 satellite imagery. The classified photographs were used to calibrate WorldView-3 spectral indices to fractional crop residue cover rasters.
The release includes the following datasets:
1. fractional_crop_residue_gv_soil_lon_lat_photo_v5_FINAL_with_reflectance_indices.csv: This CSV file contains tabular data for 895 photograph sampling locations. It includes classifications of fractional crop residue, green vegetation, and bare soil, as well as photograph acquisition dates, latitude, longitude, and corresponding WorldView-3 reflectance spectra and spectral indices.
2. GroundCoverPhotographs.zip: A zip file containing all 895 photographs used for ground cover calculations.
3. Raster maps derived from WorldView-3 satellite imagery. Raster file sizes range from 36 MB to 833 MB, which may affect download times. The raster maps include four bands:
a. Fractional crop residue cover,
b. crop residue indices (SINDRI or SWIRA),
c. green vegetation index (NDVI), and
d. water index (WI).
The dataset includes field sampling data comprising in-field nadir photographs collected from a collaborating farm in Talbot County, Maryland, during the spring of 2015, 2016, 2017, 2019, 2021, and 2022. These photographs were classified for fractional ground cover, specifically green vegetation, crop residue, and bare soil, utilizing SamplePoint software. The dates of photograph collection align with the acquisition of WorldView-3 satellite imagery. The classified photographs were used to calibrate WorldView-3 spectral indices to fractional crop residue cover rasters.
The release includes the following datasets:
1. fractional_crop_residue_gv_soil_lon_lat_photo_v5_FINAL_with_reflectance_indices.csv: This CSV file contains tabular data for 895 photograph sampling locations. It includes classifications of fractional crop residue, green vegetation, and bare soil, as well as photograph acquisition dates, latitude, longitude, and corresponding WorldView-3 reflectance spectra and spectral indices.
2. GroundCoverPhotographs.zip: A zip file containing all 895 photographs used for ground cover calculations.
3. Raster maps derived from WorldView-3 satellite imagery. Raster file sizes range from 36 MB to 833 MB, which may affect download times. The raster maps include four bands:
a. Fractional crop residue cover,
b. crop residue indices (SINDRI or SWIRA),
c. green vegetation index (NDVI), and
d. water index (WI).
Citation Information
Publication Year | 2025 |
---|---|
Title | Crop Residue Cover In-Field Photographs, WorldView-3 Spectral Indices, and Derived Residue Maps for Maryland, USA, 2015-2022 |
DOI | 10.5066/P13DX6FS |
Authors | Wells D Hively, Brian T Lamb, Jyoti Jennewein, Alexander M Soroka |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Lower Mississippi-Gulf Water Science Center - Nashville, TN Office |
Rights | This work is marked with CC0 1.0 Universal |
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Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery
Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover (fR) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify fR at scale has been limited. In this study we produced annual maps of fR for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm...
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