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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #345136
Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Combining Landsat-8 and WorldView-3 data to assess crop residue cover

Author
item Daughtry, Craig
item Stern, Alan
item Hively, Wells - Dean
item Russ, Andrew - Andy
item QUEMADA, M. - University Of Madrid

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2017
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Crop residues on the soil surface contribute to soil quality and form the first line defense against the erosive forces of water and wind. Quantifying crop residue cover on the soil surface after crops are planted is crucial for monitoring soil tillage intensity and assessing the extent of conservation practices. Currently, no program exists for objectively monitoring crop residue cover or tillage intensity at field to watershed scales. Spectral indices that detect absorption features associated with cellulose and lignin in crop residues can provide reliable assessments of crop residue cover. However, current multispectral satellite sensors either lack appropriate spectral bands to reliably distinguish crop residues from soil or cannot provide wall-to-wall coverage. Our objective was to estimate crop residue cover and tillage intensity in corn and soybean fields by combining data from two multispectral satellites. We measured crop residue cover in two locations/field in >45 fields using the line-point transect method. Landsat-8 and WorldView-3 images were acquired within 10 days of the field measurements and corrected to surface reflectance. The Landsat Normalized Difference Tillage Index (NDTI) required local calibrations to account for changes in soils, crops, and scene moisture. In contrast, the WorldView-3 Shortwave Infrared Normalized Difference Residue Index (SINDRI) reliably estimated crop residue cover with minimal ground truth data. Although WorldView-3 images cannot provide wall-to-wall coverage, they can augment and extend ground truth observations for calibrating the Landsat and Sentinel-2 tillage indices. Classifications of tillage intensity corresponding to conventional tillage (<30% residue cover) and conservation tillage (=30% residue cover) were significantly better than random using either Landsat-8 data only or Landsat-8 plus WorldView-3 data. The contribution of the WorldView-3 data significantly reduced the number of ground truth samples required for calibrating the Landsat tillage indices.