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Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue CharacterizationThis study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation.
Document ID
20230014116
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Brian T. Lamb ORCID
(United States Geological Survey Reston, United States)
Philip E. Dennison ORCID
(University of Utah Salt Lake City, Utah, United States)
W. Dean Hively ORCID
(United States Geological Survey Reston, United States)
Raymond F. Kokaly
(United States Geological Survey Reston, United States)
Guy Serbin ORCID
(Mallon Technology)
Zhuoting Wu ORCID
(United States Geological Survey Reston, United States)
Philip W. Dabney
(Goddard Space Flight Center Greenbelt, United States)
Jeffrey G. Masek
(Goddard Space Flight Center Greenbelt, United States)
Michael Campbell ORCID
(University of Utah Salt Lake City, Utah, United States)
Craig S. T. Daughtry ORCID
(Agricultural Research Service - Northeast Area Beltsville, Maryland, United States)
Date Acquired
September 28, 2023
Publication Date
December 3, 2022
Publication Information
Publication: Remote Sensing
Publisher: MDPI
Volume: 14
Issue: 23
Issue Publication Date: December 1, 2022
e-ISSN: 2072-4292
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
WBS: 285372.04.01.02
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Peer Committee
Keywords
Landsat Next
crop residue
non-photosynthetic vegetation
tillage
lignocellulose
shortwave infrared
CAI
SINDRI
LCPCDI
NDTI
NDVI
SWIR
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