Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

Abstract

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.

Advancements in Remote Sensing for Evapotranspiration Estimation: A Comprehensive Review of Temperature-Based Models

“…The Landsat-9 satellite was launched in September 2021 and closely resembles Landsat-8. These Landsat missions ensure a consistent supply of multispectral images [122]. To this constellation Landsat Next will be added in late 2030.…”

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

“…These spectral analyses aid in precise crop condition assessments and contribute to water conservation strategies crucial in geothermal fields. Enhanced monitoring promotes optimum irrigation methods, minimizing wasteful water usage and contributing to the balance of local hydrological cycles, therefore maintaining the integrity and sustainability of both agricultural and geothermal resources (see Figure 6) [64,105]. The utilization of the Random Forest (RF) technique for land use classification within geothermal areas leverages the unique spectral sensitivities of Landsat bands 7 (shortwave infrared 2), 3 (green), and 6 (shortwave infrared 1), which have proven particularly effective by 2023.…”

Full-Season Crop Phenology Monitoring Using Two-Dimensional Normalized Difference Pairs

“…The basis of this approach is that greater biomass translates into more water in the pixel, which can be detected using satellite-based SWIR. The SWIR part of the spectrum has also been used to estimate nonphotosynthetic vegetation cover relative to soil cover, which may indicate that chlorophyllbased vegetation indices (e.g., NDVI) and SWIR-based vegetation indices may follow different time series [33]. As with the NDVI, SWIR measurements would not be able to distinguish between a young plant and a senescing plant if both exhibited comparable water content on a pixel basis.…”