A comparative Spectral Mixture Analysis (SMA) of Landsat 7 Enhanced Thematic Mapper (ETM+) imagery for a collection of 28 urban areas worldwide provides a physical basis for a spectral characterization of urban reflectance properties. These urban areas have similar mixing space topologies and can be represented by three‐component linear mixture models in both scene‐specific and global composite mixing spaces. The results of the analysis indicate that the reflectance of these cities can be accurately described as linear combinations of High Albedo, Dark and Vegetation spectral endmembers within a two‐dimensional mixing space containing over 90% of the variance in the observed reflectance. Only two of the 28 cities had greater than 10% median RMS misfit to the three‐endmember linear model. The relative proportions of these endmembers vary considerably among different cities and within individual cities but in all cases the reflectance of the urban core lies near the dark end of a mixing line between the High Albedo and Dark endmembers. The most consistent spectral characteristic of the urban mosaic is spectral heterogeneity at scales of 10–20 m. In spite of their heterogeneity, built‐up areas do occupy distinct regions of the spectral mixing space. This localization in mixing space allows spectrally mixed pixels in built‐up areas to be discriminated from undeveloped land cover types. This provides a basis for mapping the spatial extent of human settlements using broadband optical satellite imagery collected over the past 30 years.

Since the 1960s, ~5000 km2 of tidal deltaplain in southwest Bangladesh has been embanked and converted to densely inhabited, agricultural islands (i.e., polders). This landscape is juxtaposed to the adjacent Sundarbans, a pristine mangrove forest, both well connected by a dense network of tidal channels that effectively convey water and sediment throughout the region. The extensive embanking in poldered areas, however, has greatly reduced the tidal prism (i.e., volume of water) transported through local channels. We reveal that >600 km of these major waterways have infilled in recent decades, converting to land through enhanced sedimentation and the direct blocking of waterways by embankments and sluice gates. Nearly all of the observed closures (~98%) have occurred along the embanked polder systems, with no comparable changes occurring in channels of the Sundarbans (<2% change). We attribute most of the channel infilling to the local reduction of tidal prism in poldered areas and the associated decline in current velocities. The infilled channels account for ~90 km2 of new land in the last 40–50 years, the rate of which, ~2 km2/yr, offsets the 4 km2/yr that is eroded at the coast, and is equivalent to ~20% of the new land produced naturally at the Ganges-Brahmaputra tidal rivermouth. Most of this new land, called ‘khas’ in Bengali, has been reclaimed for agriculture or aquaculture, contributing to the local economy. However, benefits are tempered by the loss of navigable waterways for commerce, transportation, and fishing, as well as the forced rerouting of tidal waters and sediments necessary to sustain this low-lying landscape against rising sea level. A more sustainable delta will require detailed knowledge of the consequences of these hydrodynamic changes to support more scientifically-grounded management of water, sediment, and tidal energy distribution.

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.

The spatial distribution of human population on the land surface is a fundamental determinant of land-use impacts on Earth's ecosystems. Census enumerations and satellite-detected night lights provide two complementary, but distinct, representations of human population distribution. Census estimates indicate that 37% of Earth's enumerated land area is populated at densities greater than 1 person per square kilometer. Human populations are strongly clustered within this area. Spatial variations in human population density span more than six orders of magnitude with 50% of the 1990 population occupying less than 3% of the inhabited land area. Temporally stable lighted areas detectable from space provide an independent proxy for the spatial distribution of urban settlements and the intensive land-cover changes that accompany them. These ∼60 000 lighted areas account for less than 2% of inhabited land area, and 50% of this lighted area is associated with the largest 5% of cities and conurbations. Urb... Abstract The spatial distribution of human population on the land surface is a fundamental determinant of land-use impacts on Earth's ecosystems. Census enumerations and satellite-detected night lights provide two complementary, but distinct, representations of human population distribution. Census estimates indicate that 37% of Earth's enumerated land area is populated at densities greater than 1 person per square kilometer. Human populations are strongly clustered within this area. Spatial variations in human population density span more than six orders of magnitude with 50% of the 1990 population occupying less than 3% of the inhabited land area. Temporally stable lighted areas detectable from space provide an independent proxy for the spatial distribution of urban settlements and the intensive land-cover changes that accompany them. These ∼60 000 lighted areas account for less than 2% of inhabited land area, and 50% of this lighted area is associated with the largest 5% of cities and conurbations. Urb...

A composite spectral feature space is used to characterize the spectral mixing properties of Sentinel 2 Multispectral Instrument (MSI) spectra over a wide diversity of landscapes. Characterizing the linearity of spectral mixing and identifying bounding spectral endmembers allows the Substrate Vegetation Dark (SVD) spectral mixture model previously developed for the Landsat and MODIS sensors to be extended to the Sentinel 2 MSI sensors. The utility of the SVD model is its ability to represent a wide variety of landscapes in terms of the areal abundance of their most spectrally and physically distinct components. Combining the benefits of location-specific spectral mixture models with standardized spectral indices, the physically based SVD model offers simplicity, consistency, inclusivity and applicability for a wide variety of land cover mapping applications. In this study, a set of 110 image tiles compiled from spectral diversity hotspots worldwide provide a basis for this characterization, and for identification of spectral endmembers that span the feature space. The resulting spectral mixing space of these 13,000,000,000 spectra is effectively 3D, with 99% of variance in 3 low order principal component dimensions. Four physically distinct spectral mixing continua are identified: Snow:Firn:Ice, Reef:Water, Evaporite:Water and Substrate:Vegetation:Dark (water or shadow). The first 3 continua exhibit complex nonlinearities, but the geographically dominant Substrate:Vegetation:Dark (SVD) continuum is conspicuous in the linearity of its spectral mixing. Bounding endmember spectra are identified for the SVD continuum. In a subset of 80 landscapes, excluding the 3 nonlinear mixing continua (reefs, evaporites, cryosphere), a 3 endmember (SVD) linear mixture model produces endmember fraction estimates that represent 99% of modeled spectra with <6% RMS misfit. Two sets of SVD endmembers are identified for the Sentinel 2 MSI sensors, allowing Sentinel 2 spectra to be unmixed globally and compared across time and space. In light of the apparent disparity between the 11D spectral feature space and the statistically 3D spectral mixing space, the relative contribution of 11 Sentinel 2 MSI spectral bands to the information content of this space is quantified using both parametric (Pearson Correlation) and nonparametric (Mutual Information) metrics. Comparison of linear (principal component) and nonlinear (Uniform Manifold Approximation and Projection) projections of the SVD mixing space reveal both physically interpretable spectral mixing continua and geographically distinct spectral properties not resolved in the linear projection.

This study illustrates a unified, physically-based framework for mapping landscape parameters of evapotranspiration (ET) using spectral mixture analysis (SMA). The framework integrates two widely used approaches by relating radiometric surface temperature to subpixel fractions of substrate (S), vegetation (V), and dark (D) spectral endmembers (EMs). Spatial and temporal variations in these spectral endmember fractions reflect process-driven variations in soil moisture, vegetation phenology, and illumination. Using all available Landsat 8 scenes from the peak growing season in the agriculturally diverse Sacramento Valley of northern California, we characterize the spatiotemporal relationships between each of the S, V, D land cover fractions and apparent brightness temperature (T) using bivariate distributions in the ET parameter spaces. The dark fraction scales inversely with shortwave broadband albedo (ρ < −0.98), and show a multilinear relationship to T. Substrate fraction estimates show a consistent (ρ ≈ 0.7 to 0.9) linear relationship to T. The vegetation fraction showed the expected triangular relationship to T. However, the bivariate distribution of V and T shows more distinct clustering than the distributions of Normalized Difference Vegetation Index (NDVI)-based proxies and T. Following the Triangle Method, the V fraction is used with T to compute the spatial maps of the ET fraction (EF; the ratio of the actual total ET to the net radiation) and moisture availability (Mo; the ratio of the actual soil surface evaporation to potential ET at the soil surface). EF and Mo estimates derived from the V fraction distinguish among rice growth stages, and between rice and non-rice agriculture, more clearly than those derived from transformed NDVI proxies. Met station-based reference ET & soil temperatures also track vegetation fraction-based estimates of EF & Mo more closely than do NDVI-based estimates of EF & Mo. The proposed approach using S, V, D land cover fractions in conjunction with T (SVD+T) provides a physically-based conceptual framework that unifies two widely-used approaches by simultaneously mapping the effects of albedo and vegetation abundance on the surface temperature field. The additional information provided by the third (Substrate) fraction suggests a potential avenue for ET model improvement by providing an explicit observational constraint on the exposed soil fraction and its moisture-modulated brightness. The structures of the T, EF & Mo vs SVD feature spaces are complementary and that can be interpreted in the context of physical variables that scale linearly and that can be represented directly in process models. Using the structure of the feature spaces to represent the spatiotemporal trajectory of crop phenology is possible in agricultural settings, because variations in the timing of planting and irrigation result in continuous trajectories in the physical parameter spaces that are represented by the feature spaces. The linear scaling properties of the SMA fraction estimates from meter to kilometer scales also facilitate the vicarious validation of ET estimates using multiple resolutions of imagery.