Phillip Dennison
W. Dean Hively
Raymond F. Kokaly
Guy Serbin
Zhuoting Wu
Philip W. Dabney
Jeffery G. Masek
Michael Campbell
Craig S. T. Daughtry
Brian T. Lamb
2022
<p><span>This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (</span><span class="html-italic">f</span><sub>R</sub><span>) 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 </span><span class="html-italic">f</span><sub>R</sub><span>. 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 </span><span class="html-italic">f</span><sub>R</sub><span> 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 </span><span class="html-italic">f</span><sub>R</sub><span> estimation performance (</span><span class="html-italic">R</span><sup>2</sup><span> = 0.8222; </span><span class="html-italic">RMSE</span><span> = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (</span><span class="html-italic">R</span><sup>2</sup><span> = 0.8145; </span><span class="html-italic">RMSE</span><span> = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (</span><span class="html-italic">R</span><sup>2</sup><span> = 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 (</span><span class="html-italic">R</span><sup>2</sup><span> = 0.8397; </span><span class="html-italic">RMSE</span><span> = 0.1231). Three-band indices with CAI-type wavelengths maintained top </span><span class="html-italic">f</span><sub>R</sub><span> estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (</span><span class="html-italic">R</span><sup>2</sup><span> = 0.7581; </span><span class="html-italic">RMSE</span><span> = 0.1548). The 2036–2111–2217 nm band combination was also top performing in </span><span class="html-italic">f</span><sub>R</sub><span> estimation (</span><span class="html-italic">R</span><sup>2</sup><span> = 0.8690; </span><span class="html-italic">RMSE</span><span> = 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 </span><span class="html-italic">f</span><sub>R</sub><span> estimation.</span></p>
application/pdf
10.3390/rs14236128
en
MDPI
Optimizing Landsat Next shortwave infrared bands for crop residue characterization
article