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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 #357496

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices

item HIVELY, W.D. - Us Geological Survey (USGS)
item LAMB, B.T. - State University Of New York (SUNY)
item Daughtry, Craig
item SHERMEYER, J. - Us Geological Survey (USGS)
item McCarty, Gregory
item QUEMADA, M. - University Of Madrid

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/9/2018
Publication Date: 10/18/2018
Citation: Hively, W., Lamb, B., Daughtry, C.S., Shermeyer, J., McCarty, G.W., Quemada, M. 2018. Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices. Remote Sensing. 10(10):1657.

Interpretive Summary: Crop residues are plant litter that remains in agricultural fields after harvest. Crop residues on the soil surface form a mulch layer that reduces soil erosion, improves soil quality, and supports healthy soil ecosystems. Crop residue cover is directly linked to crop management including tillage practices, crop rotations, and harvest methods. High residue crop management practices are key components of conservation agriculture and sustainable cropping systems. Accurate measurements of crop residue cover are crucial for assessing the extent and effectiveness of conservations tillage practices across watersheds. Current ground-based techniques for measuring crop residue cover are not suitable for monitoring many fields in a timely manner. Remote sensing has the potential to offer rapid and accurate assessment of crop residue cover over large areas in a timely and cost effective manner. Most satellites that provide global coverage, such as Landsat, have only a few broad spectral bands, and are not well-suited for mapping crop residue cover. The WorldView-3 satellite, which has 8 narrow infrared bands, acquired images over our study area within the Choptank River watershed on the Eastern Shore of the Chesapeake Bay watershed in Maryland. We measured crop residue cover using standard ground-based techniques in multiple fields with a wide range of tillage practices. Residue indices, calculated with the WorldView-3 data, accurately predicted crop residue cover and mapped the distribution of tillage intensity across the agricultural landscape. The narrow spectral bands of WorldView-3 are better suited for assessing crop residue cover than the broad spectral bands of Landsat.

Technical Abstract: Crop residues serve many important functions in agricultural conservation, including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using spectrally-narrow residue indices that characterize cellulose and lignin absorption features found near 2100nm in the shortwave infrared (SWIR) electromagnetic spectrum. The 2015 launch of the Worldview-3 (WV3) satellite has now provided a space platform for collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (May 14th, 2015) acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA) was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. Percent residue cover was measured at 174 locations falling within 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in-situ measurements were used to develop percent residue cover prediction models for the SWIR indices using both polynomial and linear least squares regression. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index <0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally-narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally-broad, Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R2 values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40 and 12.00. Additionally, SINDRI and LCA were shown to be more resistant to interference from low levels of green vegetation. The most accurate correlation (second order polynomial SINDRI, R2 = 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. Worldview-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.