A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance ($L_{\mathrm{rs},\mathrm{t}}\left(\lambda \right)$). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (${\rho }_{\mathrm{t},\mathrm{pre}}$) where effects associated with $L_{\mathrm{rs},\mathrm{t}}\left(\lambda \right)$ are less influential. The method identifies pixels comprising pure materials from ${\rho }_{\mathrm{t},\mathrm{pre}}$. AOD values at the pure pixels are iteratively estimated using $l_2$-norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06–0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5–10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

The atmospheric condition parameters used in the radiative transfer-based atmospheric correction (AC) are often uncertain. This uncertainty propagates to the estimated reflectance. The reflectance, is, however, not equally sensitive to all the parameters. A sensitivity analysis (SA) helps in prioritizing the parameters. The objective of this study was to perform an SA of reflectance to water vapor concentration (wv) and aerosol optical thickness (AOT). SA was performed using the Fourier amplitude sensitivity test (FAST) method, which computes sensitivity indices (SI) of these parameters. Besides variation in the two parameters, we also studied the effect of surface albedo on the SI by quantifying SI for three target surfaces (in the spectral range 0.44-0.96 μm): 1) a dark target (water); 2) a bright target (bare soil); and 3) a target having low albedo in the visible and high albedo in near-infrared range (forest). For AOT, high (≈0.9) SI values were observed at the nonwater absorption wavelengths. For wv, high SI values were observed at wavelengths, where strong absorption features are located and when the surface albedo was high. For the dark target, the effect of AOT was prominent throughout the spectral range. We found that the sensitivity of reflectance to wv and AOT is a function of wavelength, strength of the absorption features, and surface albedo. We conclude that AOT is a more important parameter for dark targets than wv even at the principal absorption feature. For bright targets, the importance of wv and AOT depends on the strength of the absorption feature.

Bhatia, N., and G. S. Cumming. 2020. Deforestation and economic growth trends on oceanic islands highlight the need for meso-scale analysis and improved mid-range theory in conservation. Ecology and Society 25(3):10. https://doi.org/10.5751/ES-11713-250310

The article describes a novel approach to estimate and calibrate column water vapour (CWV), a key parameter for atmospheric correction of remote-sensing data. CWV is spatially and temporally variable, and image-based methods are used for its inference. This inference, however, is affected by methodological and numeric limitations, which likely propagate to reflectance estimates. In this article, a method is proposed to estimate CWV iteratively from target surface reflectances. The method is free from assumptions for at sensor radiance-based CWV estimation methods. We consider two cases: (a) CWV is incorrectly estimated in a processing chain and (b) CWV is not estimated in a processing chain. To solve (a) we use the incorrect estimations as initial values to the proposed method during calibration. In (b), CWV is estimated without initial information. Next, we combined the two scenarios, resulting in a generic method to calibrate and estimate CWV. We utilized the hyperspectral mapper (HyMap) and airborne prism experiment (APEX) instruments for the synthetic and real data experiments, respectively. Noise levels were added to the synthetic data to simulate real imaging conditions. The real data used in this research are cloud-free scenes acquired from the airborne campaigns. For performance assessment, we compared the proposed method with two state-of-the-art methods. Our method performed better as it minimizes the absolute error close to zero, only within 8–10 iterations. It thus suits existing operational chains where the number of iterations is considerable. Finally, the method is simple to implement and can be extended to address other atmospheric trace gases.

Remote sensing products are widely used in ecology and environmental science to understand how surfaces are composed and configured in a landscape and how they change through time. Land cover maps that describe the nature of habitats available to organisms have become critical tools in the study of anthropogenic impacts, such as fragmentation, on biodiversity and related ecological processes. Despite the increasing importance and widespread use of remote sensing derived landscape pattern indices measured using landscape composition and configuration metrics via land cover maps, these indices may conceal substantial and potentially problematic concerns about the uncertainty of reflectance data. This uncertainty derives from the presence of varying atmospheric conditions between the earth and the sensor and is often unreported. We asked how the magnitudes of measured landscape composition and configuration change and the dispersion in the changes caused by reflectance uncertainty compare. Using a Monte Carlo framework, we estimated multiple realizations of reflectance images of a simulated hyperspectral dataset depicting a peri-urban area. These estimates were influenced by uncertainty originating from atmospheric condition parameters (column water vapour, aerosol optical depth, and aerosol type). We generated multiple classified maps using Support Vector Machines and quantified standard landscape composition and configuration metrics. High dispersion in composition and configuration metrics caused by reflectance uncertainty strongly impacted our ability to detect and compare changes of the kind that natural scientists typically derive from such maps. We conclude that the propagation of uncertainty limits the extent to which current landscape patterns and Land Use and Land Cover change (LULC) analyses can be used for fine-scale decision-making for landscape management.