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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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8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon ( Oncorhynchus tshawytscha ) along the Sacramento River, California, Using Remote Sensing Imagery

Off-channel areas are one of the most impacted aquatic habitats by humans globally, as extensive agricultural and urban development has limited them to roughly 10% of historical extent. This is also true for California’s Sacramento River Valley, where historically frequent widespread inundation has been reduced to a few off-channel water bodies along the mid-Sacramento River. This remaining shallow-water habitat provides crucial ecological benefits to multiple avian and fish species, but especially to floodplain-adapted species such as Chinook salmon (Oncorhynchus tshawytscha). Characterizing spatiotemporal off-channel dynamics, including inundation extent and residence time, is fundamental to better understanding the intrinsic value of such habitats and their potential to support recovery actions. Remote sensing techniques have been increasingly used to map surface water at regional and local scales, with improved resolutions. As such, this study maps off-channel inundation areas and describes their temporal dynamics by analyzing pixel-based time- series of multiple water indices, modified Normalized Difference Water Index (mNDWI) and the Automated Water Extraction Index (AWEI), generated from LandSat-8 and Sentinel-2 data between 2013–2021. Quantified off-channel area was similar with each water index and method used, but improved performance was associated with Sentinel-2 products and AWEI index to identify wetted areas under lower mainstem discharges. Results indicate an uneven distribution of off-channel habitat in the study area, with limited inundated areas in upstream reaches (<16% of total off-channel area for greater flows). In addition, much less habitat exists for flows under 400 m 3 /s, an important migration cue for endangered winter-run Chinook salmon, limiting juvenile access to areas with enhanced rearing conditions. Off-channel habitat residence times averaged between 7 and 16 days, primarily defined by the rate of receding flows, with rapid flow recession providing marginal off-channel habitat. This study shows reasonable performance of moderate resolution LandSat-8 and Sentinel-2 remote sensing imagery to characterize shallow-water inundated habitat in higher-order rivers, and as a method to inform restoration and native fish recovery efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Imaging the Future Threats of the Sand Dunes Along the Northwestern Coast of Nile Delta Using SAR

Although the Sand dunes screen the coastal cities from the sand dunes hazards and their infrastructures from rising sea levels and storms, they might threaten and hide the surrounding developmental projects. Thus, imaging these coastal dunes' internal anatomy and dynamics are crucial to protect the environment. In this study, the Optical and Synthetic Aperture Radar (SAR) images were processed and integrated with field observations and surveys to extract information about the past, current, and future behavior of coastal dunes in the Nile Delta-Northwest sector. A conventional change detection method using the supervised Landsat-8 images for the years 2015 and 2017 were generated to show a rapid change in the sand dune cover, where the high-resolution images of Google Earth were used to digitize the dune crests and measure its encroachment rate, which has been reached about 4 m/year with the NW–SE direction. The full-polarimetric ALOS/PALSAR-2 images from the years 2015 and 2017 show very low coherence for the sand dunes, which means these dunes are dynamic, where the changes in both power and phase imbalances of different dune fields during the investigated years (2015 and 2017) show high differences in values, which reached 40 dB and 0.06°, respectively. Finally, the extracted radar facies from the field survey show two successive groups of coastal sand dunes show a decline in the heavy minerals content to a level that might threaten the properties at the shoreline due to sea level rise and wave storms if mitigation actions are not taken place soon.

Ramadan, Rabab↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R² from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

Zeng, Yelu↗
DOE OSTI2022

Ten Years of VIIRS Land Surface Temperature Product Validation

The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users in climate studies and atmospheric models. In this study, the VIIRS LST is validated with ground observations from multiple high-quality radiation networks, including six stations from the Surface Radiation budget (SURFRAD) network, two stations from the Baseline Surface Radiation Network (BSRN), and 13 stations from the Atmospheric Radiation Measurement (ARM) network, to evaluate its performance over various land-cover types. The VNP21A1 LST was validated against the same ground observations as a reference. The results yield a close agreement between the SNPP VIIRS LST and ground LSTs with a bias of -0.4 K and a RMSE of 1.96 K over six SURFRAD sites; a bias of -0.2 K and a RMSE of 1.93 K over two BSRN sites; and a bias of -0.1 K and a RMSE of 1.7 K over the 13 ARM sites. The time series of the LST errors over individual sites indicate seasonal cycles. The data anomaly over the BSRN site in Cabauw and the SURFRAD site in Desert Rock is revealed and discussed in this study. In addition, a method using Landsat-8 data is applied to quantify the heterogeneity level of each ground station and the results provide promising insights. The validation results demonstrate the maturity of the JPSS VIIRS LST products and their readiness for various application studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

High-Resolution PM2.5 Concentrations Estimation Based on Stacked Ensemble Learning Model Using Multi-Source Satellite TOA Data

Nepal has experienced severe fine particulate matter (PM2.5) pollution in recent years. However, few studies have focused on the distribution of PM2.5 and its variations in Nepal. Although many researchers have developed PM2.5 estimation models, these models have mainly focused on the kilometer scale, which cannot provide accurate spatial distribution of PM2.5 pollution. Based on Gaofen-1/6 and Landsat-8/9 satellite data, we developed a stacked ensemble learning model (named XGBLL) combined with meteorological data, ground PM2.5 concentrations, ground elevation, and population data. The model includes two layers: a XGBoost and Light GBM model in the first layer, and a linear regression model in the second layer. The accuracy of XGBLL model is better than that of a single model, and the fusion of multi-source satellite remote sensing data effectively improves the spatial coverage of PM2.5 concentrations. Besides, the spatial distribution of the daily mean PM2.5 concentrations in the Kathmandu region under different air conditions was analyzed. The validation results showed that the monthly averaged dataset was accurate (R2 = 0.80 and root mean square error = 7.07). In addition, compared to previous satellite PM2.5 datasets in Nepal, the dataset produced in this study achieved superior accuracy and spatial resolution.

Environmental Sciences & Ecology↗