Upcoming satellite imaging spectroscopy missions will deliver spatiotemporal explicit data streams to be exploited for mapping vegetation properties, such as nitrogen (N) content. Within retrieval workflows for real-time mapping over agricultural regions, such crop-specific information products need to be derived precisely and rapidly. To allow fast processing, intelligent sampling schemes for training databases should be incorporated to establish efficient machine learning (ML) models. In this study, we implemented active learning (AL) heuristics using kernel ridge regression (KRR) to minimize and optimize a training database for variational heteroscedastic Gaussian processes regression (VHGPR) to estimate aboveground N content. Several uncertainty and diversity criteria were applied on a lookup table (LUT) composed of aboveground N content and corresponding hyperspectral reflectance simulated by the PROSAIL-PRO model. The best-performing AL criteria were Euclidian distance-based diversity (EBD) resulting in a reduction of the LUT training data set by 81% (50 initial samples plus 141 samples selected from a pool of 1000 samples). This reduced LUT was used for training VHGPR, which is not only a competitive algorithm but also provides uncertainty estimates. Validation against N reference data provided excellent results with a root-mean-square error (RMSE) of 1.84 g/m and a coefficient of determination ( ) of 0.92. Mapping aboveground N content over an agricultural region yielded reliable estimates and meaningful associated uncertainties. These promising results encourage the transfer of such hybrid workflows into space and time within the frame of future operational N monitoring from satellite imaging spectroscopy data.

Seismic reflectivity inversion is widely applied to improve the seismic resolution to obtain detailed underground understandings. Based on the convolution model, seismic inversion removes the wavelet effect by solving an optimization problem. Taking advantage of the sparsity property, the norm is commonly adopted in the regularization terms to overcome the noise/interference vulnerability observed in the -losses minimization. However, no one has provided a deterministic conclusion that ℓ norm regularization is the best choice for seismic reflectivity inversion. Instead of using an unproved fixed regularization norm, we propose an optimal seismic reflectivity inversion approach. Our method adaptively adopts a -loss- -regularization (i.e. regularization) for = 2, 0 < < 1 to estimate a more accurate and detailed reflectivity profile. In addition, we employ a fold cross-validation based approach to obtain the optimal damping factor λ to further improve the seismic inversion results. The letter starts with the introduction of non-convex constraint for seismic inversion, and the necessity of the norm regularization. Then the majorization-minimization and cross validation algorithms are briefly described. The performance of the proposed seismic inversion approach is evaluated through synthetic examples and a field example from the Bohai Bay Basin, China.

Infrared (IR) emissivity is a critical parameter for modeling and predicting heat transfer by radiation. Microwave absorbing materials, having a high emissivity in the microwave spectrum, are crucial in a wide array of applications, such as electromagnetic interference mitigation, stealth technology, and microwave remote sensing and radiometer calibration. Accurate knowledge of the thermal properties of these materials is necessary for efficient design and optimization of these types of systems. Typical microwave absorbing materials consist of a dielectric epoxy material impregnated with a lossy material, such as iron or carbon. We study a novel cryogenically compatible epoxy-based absorber material that has been loaded with varying concentrations of carbonyl iron powder (CIP). We study six materials with CIP concentrations of 0%, 5%, 10%, 20%, 30%, and 50% by tap volume. We use a commercial IR camera with sensitivity in the range 7.5-13 m to measure the radiance of the samples and a waterbath IR blackbody at ten temperatures between about 19 °C and 45 °C. A linear Deming fitting is performed, considering uncertainties in both the measured parameters, and the slope of the linear fit is shown to be the IR emissivity, averaged over the spectral response of the camera. The emissivity ranges between 0.868 and 0.757, decreasing monotonically as a function of iron carbonyl concentration between 0% and 50%. The uncertainty of the emissivity determination method is derived and presented. The uncertainty of the presented method is shown to be no larger than 3.3% for all measured samples.

This study aims at analyzing sub-pixel misregistration between multi-spectral images acquired by the Multi-Spectral Instrument (MSI) aboard Sentinel-2A remote sensing satellite, and exploring its potential for moving target and cloud detection. By virtue of its hardware design, MSI's detectors exhibit a parallax angle that leads to sub-pixel shifts that are corrected with special pre-processing routines. However, these routines do not correct shifts for moving and/or high altitude objects. In this letter, we apply a phase correlation approach to detect sub-pixel shifts between B2 (blue), B3 (green) and B4 (red) Sentinel-2A/MSI images. We show that shifts of more than 1.1 pixels can be observed for moving targets, such as airplanes and clouds, and can be used for cloud detection. We demonstrate that the proposed approach can detect clouds that are not identified in the built-in cloud mask provided within the Sentinel-2A Level-1C (L1C) product.

Spaceborne spectrometers require spectral-temporal stability characterization to aid validation of derived data products. EO-1 began orbital precession in 2011 after exhausting onboard fuel resources. In the Libya-4 Pseudo Invariant Calibration Site (PICS) this resulted in a progressive shift from a mean local equatorial crossing time of ~10:00 AM in 2011 to ~8:30 AM in late 2015. Here, we studied precession impacts to Hyperion surface reflectance products using three atmospheric correction approaches from 2004 to 2015. Combined difference estimates of surface reflectance were < 5% in the visible near infrared (VNIR) and < 10% for most of the shortwave infrared (SWIR). Combined coefficient of variation (CV) estimates in the VNIR ranged from 0.025 - 0.095, and in the SWIR ranged from 0.025 - 0.06, excluding bands near atmospheric absorption features. Reflectances produced with different atmospheric models were correlated ( ) in VNIR from 0.25 - 0.94 and SWIR from 0.12 - 0.88 ( < 0.01). The uncertainties in all models increased with terrain slope up to 15° and selecting dune flats could reduce errors. We conclude that these data remain a useful resource over this period.

At the end of 2019, the very first COVID-19 coronavirus infection was reported and then it spread across the world just like wildfires. From late January to March 2020, most cities and villages in China were locked down, and consequently, human activities decreased dramatically. This letter presents an "offline learning and online inference" approach to explore the variation of PM2.5 pollution during this period. In the experiments, a deep regression model was trained to establish the complex relationship between remote sensing data and PM2.5 observations, and then the spatially continuous monthly PM2.5 distribution map was simulated using the Google Earth Engine platform. The results reveal that the COVID-19 lockdown truly decreased the PM2.5 pollution with certain hysteresis and the fine particle pollution begins to increase when advancing resumption of work and production gradually.

The present study addresses the impacts of reduced anthropogenic activities during the lockdown period of COVID-19 pandemic on the aerosol concentration, treated as heat absorbing agent, and on the related atmospheric processes, using ground-based and spaceborne measurements over a highly polluted Indian metropolis, Kolkata. The investigation reveals that reduced aerosol concentrations during the pre-monsoon of 2020, when the lockdown was implemented, decreased atmospheric instability as indicated by low values of the convective available potential energy (CAPE). This hindered the abundance of aerosols above the atmospheric boundary layer. Also, micro rain radar (MRR) observations showed a significant reduction of convective precipitation occurrences over Kolkata during this period. The back trajectory analysis has revealed the absence of continental component toward the wind clusters associated with rain occurrences during pre-monsoon 2020. This resulted in increased occurrences of stratiform rain events during the pre-monsoon of 2020 compared to the same period of previous years.