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Open AccessArticle

Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices

1
U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Beltsville, MD 20705, USA
2
Department of Earth and Atmospheric Sciences, The City College of New York, City University of New York, New York, NY 10031, USA
3
U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
4
U.S. Geological Survey, Eastern Geographic Science Center; Reston, VA 20192, USA
5
School of Agricultural Engineering, Dept. Agricultural Production, CEIGRAM, Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Current address: In-Q-Tel, CosmiQ Works, Arlington, VA 22003, USA.
Remote Sens. 2018, 10(10), 1657; https://doi.org/10.3390/rs10101657
Received: 23 August 2018 / Revised: 20 September 2018 / Accepted: 9 October 2018 / Published: 18 October 2018
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
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 residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was 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. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 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 from the SWIR indices using both polynomial and linear least squares regressions. 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 more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd 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. View Full-Text
Keywords: remote sensing; crop residue; conservation tillage; residue index; cellulose; lignin; water quality; NDTI; SINDRI; LCA; WorldView; non-photosynthetic vegetation; SWIR remote sensing; crop residue; conservation tillage; residue index; cellulose; lignin; water quality; NDTI; SINDRI; LCA; WorldView; non-photosynthetic vegetation; SWIR
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MDPI and ACS Style

Hively, W.D.; Lamb, B.T.; Daughtry, C.S.T.; Shermeyer, J.; McCarty, G.W.; Quemada, M. Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices. Remote Sens. 2018, 10, 1657.

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  • Externally hosted supplementary file 1
    Doi: 10.5066/F7930SDB
    Link: https://doi.org/10.5066/F7930SDB
    Description: The data used to support the findings presented in this manuscript are available as a U.S. Geological Society data release at https://doi.org/10.5066/F7930SDB (Hively et al., 2018).
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