Standardized substrate, vegetation, and dark (S, V, & D) spectral endmembers (EMs) are presented for spectral mixture analysis (SMA) of MODIS version 6 daily nadir looking BRDF-adjusted reflectance (daily NBAR). EMs are derived from a diverse collection of over 43,000,000MODIS spectra spanning 6 continents and all non-polar biomes. EM spectra are similar to those from decametre studies. Sensitivity analysis using 351 pairs of 27 EM combinations shows mean differences in SVD fraction estimates of 4±3%, 3±2%, and 3±2%, respectively. An additional snow EM is identified, but deemed tentative pending further cryospheric analysis. Vicarious validation with Landsat shows under 0.5% bias in V fraction, −7% and +11% biases in S and D fractions, and comparable dispersion (standard deviation 6% to 7%) in all fractions. Model misfit is low (root-mean-square error under 5% for 99.9% pixels). When V is compared to Normalized Difference, Enhanced, and Soil Adjusted Vegetation Indices (NDVI, EVI, SAVI), NDVI shows over twice the dispersion (standard deviation 29% vs 13–14%) and over 4 times the bias (+18% vs 4%) of EVI and SAVI. Combined with previous studies, these results extend the scaling linearity and low misfit of the global SVD linear mixture model from 2m up to 500m.

Spectrally mixed pixels are the rule, not the exception, in decameter terrestrial imaging. By definition, the reflectance spectrum of a mixed pixel is a function of more than one generative process. Physically based surface biology and geology retrievals must therefore isolate the component of interest from a myriad of unrelated processes, each occurring with unknown presence and abundance across the hundreds of square meters sampled by each pixel. Foliar traits, for example, must be isolated from canopy structure and substrate composition. In many cases, these unrelated processes can dominate overall variance of spatially integrated reflectance. We propose a new approach to isolate low‐variance spectral signatures in mixed pixels. The aggregate effects of (high‐variance) spatial mixing processes within each pixel are modeled by treating the observed reflectance as a linear mixture of a small set of generic endmember spectra. Spatial mixing effects are removed by computing the (low‐variance) difference between the modeled and observed spectra, named the Mixture Residual (MR). The MR, a residual reflectance spectrum that is presumed to carry the subtler and variable signals of interest, is then leveraged as a source of signal. We illustrate the approach using three independent collections of reflectance spectra: synthetic composites computed from field measurements, NEON AOP airborne image compilations, and DESIS satellite data. The MR is found to discriminate between land cover versus plant trait signals, and to accentuate subtle absorption features. Mean band‐to‐band correlations within the visible, NIR, and SWIR wavebands decrease from 0.97, 0.94, and 0.97 to 0.95, 0.04, and 0.31 (respectively). The number of dimensions required to explain 99% of image variance increases from 4 to 13. We focus on vegetation as an illustrative example, but note that the concept can be extended to other classes of spectra and used as an input to other algorithms.

Mycorrhizae alter global patterns of CO₂ fertilization, carbon storage, and elemental cycling, yet knowledge of their global distributions is currently limited by the availability of forest inventory data. Here, we show that maps of tree‐mycorrhizal associations (hereafter “mycorrhizal maps”) can be improved by the novel technology of imaging spectroscopy because mycorrhizal signatures propagate up from plant roots to impact forest canopy chemistry. We analyzed measurements from 143 airborne imaging spectroscopy surveys over 112,975 individual trees collected across 13 years. Results show remarkable accuracy in capturing ground truth observations of mycorrhizal associations from canopy signals across disparate landscapes (R² = 0.92, p < 0.01). Upcoming imaging spectroscopy satellite missions can reveal new insights into landscape‐scale variations in water, nitrogen, phosphorus, carotenoid/anthocyanin, and cellulose/lignin composition. Applied globally, this approach could improve the spatial precision of mycorrhizal distributions by a factor of roughly 10⁴ and facilitate the incorporation of dynamic shifts in forest composition into Earth system models.