Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes

The specific inherent optical properties of three sub-tropical and tropical water reservoirs in Queensland, Australia

The underwater light climate, which is a major influence on the ecology of aquatic systems, is affected by the absorption and scattering processes that take place within the water column. Knowledge of the Specific Inherent Optical Properties (SIOPs) of water quality parameters and their spatial variation is essential for the modelling of underwater light fields and remote sensing applications. We measured the SIOPs and water quality parameter concentrations of three large inland water impoundments in Queensland, Australia. The measurements ranged from 0.9–42.7 μgl/1 for chlorophyll a concentration, 0.9–170.4 mgl/1 for tripton concentration, 0.36–1.59 m/1 for aCDOM(440) and 0.15–2.5 m for Secchi depth. The SIOP measurements showed that there is sufficient intra-impoundment variation in the specific absorption and specific scattering of phytoplankton and tripton to require a well distributed network of measurement stations to fully characterise the SIOPs of the optical water quality parameters. While significantly different SIOP sets were measured for each of the study sites the measurements were consistent with published values in other inland waters. The multiple measurement stations were allocated into optical domains as a necessary step to parameterise a semi-analytical inversion remote sensing algorithm. This paper also addresses the paucity of published global inland water SIOP sets by contributing Australian SIOP sets to allow international and national comparison

Development of a System to Invert Eddy-Current Data and Reconstruct Flaws

In this report we describe an approach to the reconstruction of flaws, not merely their detection. This will give us the ability to obtain much more information about the nature of the flaw. By “flaw” we mean virtually any departure of the medium from a standard condition, which is known a priori, such as may be produced not only by a crack but also by conductivity in homogeneities produced by stresses, magnetite build-up, etc. Our approach is very much in the spirit of contemporary work in inverse methods in electromagnetics [1–3] and electromagnetic-geophysical prospecting [4–11].</p