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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2004

A Simulation Model of Carbon Cycling and Methane Emissions in Amazon Wetlands

An integrative carbon study is investigating the hypothesis that measured fluxes of methane from wetlands in the Amazon region can be predicted accurately using a combination of process modeling of ecosystem carbon cycles and remote sensing of regional floodplain dynamics. A new simulation model has been build using the NASA- CASA concept for predicting methane production and emission fluxes in Amazon river and floodplain ecosystems. Numerous innovations area being made to model Amazon wetland ecosystems, including: (1) prediction of wetland net primary production (NPP) as the source for plant litter decomposition and accumulation of sediment organic matter in two major vegetation classes - flooded forests (varzea or igapo) and floating macrophytes, (2) representation of controls on carbon processing and methane evasion at the diffusive boundary layer, through the lake water column, and in wetland sediments as a function of changes in floodplain water level, (3) inclusion of surface emissions controls on wetland methane fluxes, including variations in daily surface temperature and of hydrostatic pressure linked to water level fluctuations. A model design overview and early simulation results are presented.

Potter, Christopher↗

NASA NTRS2020

Satellite and airborne remote sensing of gross primary productivity in boreal Alaskan lakes

In terrestrial and marine ecosystems, remote sensing has been used to estimate gross primary productivity (GPP) for decades, but few applications exist for shallow freshwater ecosystems.Here we show field-based GPP correlates with satellite and airborne lake color across a range of optically and limnologically diverse lakes in interior Alaska. A strong relationship between in situ GPP derived from stable oxygen isotopes (δ18O) and space-based lake color from satellites (e.g. Landsat-8, Sentinel-2 and CubeSats) and airborne imagery (AVIRIS-NG) demonstrates the potential power of this technique for improving spatial and temporal monitoring of lake GPP when coupled with additional field validation measurements across different systems. In shallow waters clear enough for sunlight to reach lake bottoms, both submerged vegetation (macrophytes and algae) and phytoplankton likely contribute to GPP. The stable isotopes and remotely sensed shallow lake color used here integrate both components. These results demonstrate the utility of lake color as a feasible means for mapping lake GPP from remote sensing. This novel methodology estimates GPP from remote sensing in shallow lakes by combining field measurements of oxygen isotopes with airborne, satellite and CubeSat imagery. This use of lake color for providing insight into ecological processes of shallow lakes is recommended, especially for remote arctic and boreal landscapes.

airborne remote sensing↗

NASA NTRS2023

A Blind Convolutional Deep Autoencoder for Spectral Unmixing of Hyperspectral Images Over Waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

hyperspectral imaging↗