Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Abstract

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.

Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands

“…Remote Sensing can provide valuable techniques to assess the presence of NPV over large areas through the analysis of aerial and satellite data [6,10,11]. Hyperspectral Narrow Bands (HNB) data proved to be more efficient and reliable than multispectral broadband (MSBB) data [1,[12][13][14] for two main reasons: (i) hyperspectral sensors allow for the recognition of even subtle absorption features, as those diagnostic of carbon-based constituents (CBC) of Crop Residues, such as cellulose, lignin, hemicellulose and starch [15,16]; (ii) CBCs exhibit diagnostic absorption features mainly in the short-wave infrared (SWIR), a spectral region where most multispectral sensors provide limited information, having few and coarse resolution bands. Since crop residues show many diagnostic features in the range 1.6-2.3 µm to be distinguished from soils [17], high spectral resolution in the SWIR is desirable.…”

Use of remote sensing to track postindustrial vegetation development

“…The fraction of soil cover should also be considered as part of the RS signal. Nonphotosynthetic vegetation (NPV) and soil cover are difficult to assess accurately because the wave absorption by some organic molecules, including high fractions of NPV, for example, lignin and cellulose, cannot be differentiated from soil by broadband multispectral data (Dennison et al, 2019; He & Mui, 2010).…”

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

“…Hyperspectral field spectrometer data published in Dennison et al [43] were used to simulate and subsequently evaluate five SWIR bands and associated NPV indices relevant to the Landsat Next mission, focusing on 916 source spectra collected by Daughtry and Hunt (2008) [16] and by Quemada and Daughtry (2016) [32] [16]. These spectra captured fields with varying percent cover of soil, crop residue, and young green corn (Zea mays L.), soybean (Glycine max (L.) Merr.…”

“…Relative water content (RWC) of soil and residue was measured for all spectra. Spectra of material with RWC in excess of 60% were excluded from the Dennison et al [43] analysis due to strong attenuation of SWIR reflectance, resulting in a total of 410 spectra and associated NPV-soil fractional cover values. All samples exhibited NDVI < 0.3.…”

“…Two versions of this dataset were subsequently compiled: gaussian surface reflectance with and without simulated residual atmospheric effects and sensor noise (Version A), and boxcar surface reflectance (Version B). For the gaussian dataset, Dennison et al [43] first convolved the source surface reflectance spectra to 10 nm using full-width half-maximum (FWHM) gaussian spectral response functions (SRF), creating the gaussian "original" dataset. To add sensor noise, these spectra were subsequently converted to simulated radiance for a satellite sensor using a radiative transfer model and then a radiance-dependent noise function was applied.…”