Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue Characterization

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-12, Vol.14 (23), p.6128
• Main Authors: Lamb, Brian T., Dennison, Philip E., Hively, W. Dean, Kokaly, Raymond F., Serbin, Guy, Wu, Zhuoting, Dabney, Philip W., Masek, Jeffrey G., Campbell, Michael, Daughtry, Craig S. T.
• Format: Article
• Language: English
• Subjects: Agriculture; Cellulose; Cover crops; crop residue; Crop residues; Crops; Datasets; Earth Resources and Remote Sensing; Iterative methods; Landsat; Landsat Next; Landsat satellites; Lignocellulose; Moisture effects; non-photosynthetic vegetation; Normalized difference vegetative index; Optimization; Remote sensing; Residues; Short wave radiation; shortwave infrared; tillage; Tolerances; Vegetation; Vegetation cover; Wavelength; Wavelengths
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14236128

This 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.