Estimating Visible Band Albedo from Aerial Orthophotographs in Urban Areas

Albedo is an important parameter in many environmental and renewable energy models. Satellite sensors can be used to derive broadband or narrowband albedos. However, the spatial resolution of such data can be insufficient in urban areas with complex morphology and land cover diversity. In this study, we propose the use of widely available aerial orthophotographs to derive visible band albedo in urban surfaces that can be effectively used in high-resolution applications. The solution is based on the estimation of the reflected irradiance captured by an RGB sensor and approximated by the brightness component in the hue-saturation-brightness (HSB) color model and incident solar irradiance modelled by the r.sun module in GRASS GIS. The visible band albedo values are calibrated by published reference values for selected land cover classes or, alternatively, by a spectroradiometer. The method is applied to the central part of Košice and compared to visible band albedo derived from the Landsat 8 OLI and Sentinel 2A sensors and previously published typical albedo values for various land cover classes, resulting in reasonable agreement. The proposed methodology is implemented using standard GIS tools that are easily applicable to any high-resolution urban data.

Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space

The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.

Three different glacier surges at a spot: What satellites observe and what not

In the Karakoram, dozens of glacier surges occurred in the past two decades, making the region one of its global hot spots. Detailed analyses of dense time series from optical and radar satellite images revealed a wide range of surge behaviour in this region: from slow advances longer than a decade at low flow velocities to short, pulse-like advances over one or two years with high velocities. In this study, we present an analysis of three currently surging glaciers in the central Karakoram: North and South Chongtar Glaciers and an unnamed glacier referred to as NN9. All three glaciers flow towards the same region but differ strongly in surge behaviour. A full suite of satellite sensors and digital elevation models (DEMs) from different sources are used to (a) obtain comprehensive information about the evolution of the surges from 2000 to 2021 and (b) to compare and evaluate capabilities and limitations of the different satellite sensors for monitoring relatively small glaciers in steep terrain. A strongly contrasting evolution of advance rates and flow velocities is found, though the elevation change pattern is more similar. For example, South Chongtar Glacier had short-lived advance rates above 10 km y−1, velocities up to 30 m d−1 and surface elevations increased by 200 m. In contrast, the neighbouring and three times smaller North Chongtar Glacier had a slow and near linear increase of advance rates (up to 500 m y−1), flow velocities below 1 m d−1 and elevation increases up to 100 m. The even smaller glacier NN9 changed from a slow advance to a full surge within a year, reaching advance rates higher than 1 km y−1. It seems that, despite a similar climatic setting, different surge mechanisms are at play and a transition from one mechanism to another can occur during a single surge. The sensor inter-comparison revealed a high agreement across sensors for deriving flow velocities, but limitations are found on small and narrow glaciers in steep terrain, in particular for Sentinel-1. All investigated DEMs have the required accuracy to clearly show the volume changes during the surges and elevations from ICESat-2 ATL06 data fit neatly. We conclude that the available satellite data allow for a comprehensive observation of glacier surges from space when combining different sensors to determine the temporal evolution of length, elevation and velocity changes.

Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods

The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning-based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The Random Forest (RF) and Neural Network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively-sensed cloud boundaries from the radar and lidar systems onboard the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.

Tracking aerosols and SO₂ clouds from the Raikoke eruption: 3D view from satellite observations

The 21 June 2019 eruption of the Raikoke volcano (Kuril Islands, Russia; 48∘ N, 153∘ E) produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. The dispersed SO2 and sulfate aerosols in the stratosphere were still detectable by multiple satellite sensors for many months after the eruption. For this study of SO2 and aerosol clouds we use data obtained from two of the Ozone Mapping and Profiler Suite sensors on the Suomi National Polar-orbiting Partnership satellite: total column SO2 from the Nadir Mapper and aerosol extinction profiles from the Limb Profiler as well as other satellite data sets. We evaluated the limb viewing geometry effect (the “arch effect”) in the retrieval of the LP standard aerosol extinction product at 674 nm. It was shown that the amount of SO2 decreases with a characteristic period of 8–18 d and the peak of stratospheric aerosol optical depth recorded at a wavelength of 674 nm lags the initial peak of SO2 mass by 1.5 months. Using satellite observations and a trajectory model, we examined the dynamics of an unusual atmospheric feature that was observed, a stratospheric coherent circular cloud of SO2 and aerosol from 18 July to 22 September 2019.

Aerosol radiative effects with dual view AOD retrievals

Seasonal maps of dual view retrieved mid-visible AOD and AODf for four selected years (1998, 2008, 2019, 2020) are introduced and assessed in comparisons to MODIS retrievals and general data of an aerosol climatology. Due to different sensor capabilities (ATSR-2, AATSR and SLSTR) there are still unresolved inconsistencies so that decadal regional trends are not as detectable as with MODIS retrievals. SLSTR retrieval, however, agree with MODIS retrievals that 2020 Covid impacts on AOD values (via comparisons to the pre-COVID 2019 reference) are at best minor and secondary to natural anomalies by wildfires and dust. In radiative transfer applications the dual view AOD data for the four years are processed in the MAC climatology environment to determine aerosol associated radiative effects for total aerosol and for anthropogenic aerosol. Even though the calculated radiative effects are affected by retrieval AOD retrieval tendencies, climate relevant TOA net-flux changes are consistent to result with AOD data from other satellite sensors and a general climatology: −0.9 W/m2 for total aerosol with a significant greenhouse effect and −0.8 and −0.2 W/m2 for anthropogenic aerosol with and without indirect effects, respectively. Aside from global averages, seasonal maps highlight the diversity of regional and seasonal radiative effects.

The Potential of Moonlight Remote Sensing: A Systematic Assessment with Multi-Source Nightlight Remote Sensing Data

One recent trend in optical remote sensing is to increase observation frequencies. However, there are still challenges on the night side when sunlight is not available. Due to their powerful capabilities in low-light sensing, nightlight satellite sensors have been deployed to capture nightscapes of Earth from space, observing anthropomorphic and natural activities at night. To date, the mainstream of nightlight remote sensing applications has mainly focused on artificial lights, especially within cities or self-luminous bodies, such as fisheries, oil, offshore rigs, etc. Observations taken under moonlight are often discarded or corrected to reduce lunar effects. Some researchers have discussed the possibility of using moonlight as a useful illuminating source at night for the detection of nocturnal features on Earth, but no quantitative analysis has been reported so far. This study aims to systematically evaluate the potential of moonlight remote sensing with mono-spectral Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB) imagery and multi-spectral photos taken by astronauts from the International Space Station (ISS), as well as unmanned aerial vehicle (UAV) night-time imagery. Using the VIIRS/DNB, ISS and UAV moonlight images, the possibilities of the moonlight remote sensing were first discussed. Then, the VIIRS/DNB, ISS, UAV images were classified over different non-self-lighting land surfaces to explore the potential of moonlight remote sensing. The overall accuracies (OA) and kappa coefficients are 79.80% and 0.45, 87.16% and 0.77, 91.49% and 0.85, respectively, indicating a capability to characterize land surface that is very similar to daytime remote sensing. Finally, the characteristics of current moonlight remote sensing are discussed in terms of bands, spatial resolutions, and sensors. The results confirm that moonlight remote sensing has huge potential for Earth observation, which will be of great importance to significantly increase the temporal coverage of optical remote sensing during the whole diurnal cycle. Based on these discussions, we further examined requirements for next-generation nightlight remote sensing satellite sensors.

Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout

Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.