scholarly journals Remote sensing of aerosols over snow using infrared AATSR observations

2011 ◽  
Vol 4 (1) ◽  
pp. 33-71 ◽  
Author(s):  
L. G. Istomina ◽  
W. von Hoyningen-Huene ◽  
A. A. Kokhanovsky ◽  
E. Schultz ◽  
J. P. Burrows
Keyword(s):  
Remote Sensing ◽  
Ground Level ◽  
High Arctic ◽  
Visible Range ◽  
Small Scale ◽  
Aerosol Layer ◽  
Easterly Wind ◽  
Cloud Properties ◽  
Ir Reflectance ◽  
Along Track

Abstract. Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible range of spectrum. In current work, a new method to retrieve AOT over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 μm and utilizes its dual-viewing observation technique implying the forward view with an observation zenith angle around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 μm, interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 μm using a fixed Angström parameter. The presented method has been validated against ground-based Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement. A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilised mineral dust upslope, after its intrusion into the upper branch of the circulation. The performed study of atmospheric reflectance at 3.7 μm also shows possibilities of detection and retrievals of cloud properties over snow surface.

scholarly journals Remote sensing of aerosols over snow using infrared AATSR observations

2011 ◽  
Vol 4 (6) ◽  
pp. 1133-1145 ◽  
Author(s):  
L. G. Istomina ◽  
W. von Hoyningen-Huene ◽  
A. A. Kokhanovsky ◽  
E. Schultz ◽  
J. P. Burrows
Keyword(s):  
Remote Sensing ◽  
Ground Level ◽  
High Arctic ◽  
Visible Spectral Range ◽  
Small Scale ◽  
Aerosol Layer ◽  
Easterly Wind ◽  
Accumulation Mode ◽  
Cloud Properties ◽  
Ir Reflectance

Abstract. Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from the significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible spectral range. In the current work, a new method to retrieve AOT of the coarse and accumulation mode particles over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 μm and utilizes its dual-viewing observation technique, implying the forward view with an observation zenith angle of around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 μm, and interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 μm using a fixed Angström parameter. The presented method has been validated against ground-based Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement. A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq, opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilized mineral dust upslope, after its intrusion into the upper branch of the circulation. The performed study of atmospheric reflectance at 3.7 μm also shows possibilities for the detection and retrievals of cloud properties over snow surfaces.

scholarly journals Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

2019 ◽  
Author(s):  
Daniel J. Miller ◽  
Michal Segal-Rozenhaimer ◽  
Kirk Knobelspiesse ◽  
Jens Redemann ◽  
Brian Cairns ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Aerosol Layer ◽  
Large Dataset ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Neural Network Algorithm ◽  
Cloud Optical Thickness ◽  
Neural Network Input

Abstract. In this study we developed a neural network (NN) that can be used to relate a large dataset of multi-angular and multi-spectral polarimetric remote sensing observations to retrievals of cloud microphysical properties. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to Research Scanning Polarimeter (RSP) data obtained during the ORACLES field campaign (Observations of Aerosols above Clouds and their Interactions). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first-guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For the ORACLES 2016 dataset comparisons of the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re : R = 0.756, RMSE = 1.74 μm) and cloud optical thickness (τ : R = 0.950, RMSE = 1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely the the polarimetric cloud retrieval and the bispectral total reflectance cloud retrieval. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R = 0.54, RMSE = 4.77 μm) and τ (R = 0.785, RMSE = 5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ  <3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

scholarly journals Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

2020 ◽  
Vol 13 (6) ◽  
pp. 3447-3470
Author(s):  
Daniel J. Miller ◽  
Michal Segal-Rozenhaimer ◽  
Kirk Knobelspiesse ◽  
Jens Redemann ◽  
Brian Cairns ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Aerosol Layer ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Neural Network Algorithm ◽  
Cloud Optical Thickness ◽  
Design Requirements ◽  
Neural Network Input

Abstract. In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re: R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

Deep Learning Architecture for Estimating Hourly Ground-Level PM2.5 Using Satellite Remote Sensing

2019 ◽  
Vol 16 (9) ◽  
pp. 1343-1347 ◽  
Author(s):  
Yibo Sun ◽  
Qiaolin Zeng ◽  
Bing Geng ◽  
Xinwen Lin ◽  
Bilige Sude ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Satellite Remote Sensing ◽  
Ground Level

scholarly journals Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations

2021 ◽  
Vol 13 (10) ◽  
pp. 2001
Author(s):  
Antonella Boselli ◽  
Alessia Sannino ◽  
Mariagrazia D’Emilio ◽  
Xuan Wang ◽  
Salvatore Amoruso
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Ground Level ◽  
Large Area ◽  
Fire Event ◽  
Near Surface ◽  
Mean Values ◽  
Microphysical Properties ◽  
Observational Site ◽  
Biomass Burning Aerosol

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius National Park and the smoke was driven by winds towards the city of Naples, causing daily peak values of particulate matter (PM) concentrations at ground level higher than the limit of the EU air quality directive. The smoke plume spreading over the area of Naples in this period was characterized by active (lidar) and passive (sun photometer) remote sensing as well as near-surface (optical particle counter) observational techniques. The measurements allowed us to follow both the PM variation at ground level and the vertical profile of fresh biomass burning aerosol as well as to analyze the optical and microphysical properties. The results evidenced the presence of a layer of fine mode aerosol with large mean values of optical depth (AOD > 0.25) and Ångstrom exponent (γ > 1.5) above the observational site. Moreover, the lidar ratio and aerosol linear depolarization obtained from the lidar observations were about 40 sr and 4%, respectively, consistent with the presence of biomass burning aerosol in the atmosphere.

scholarly journals Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3000
Author(s):  
Georg Zitzlsberger ◽  
Michal Podhorányi ◽  
Václav Svatoň ◽  
Milan Lazecký ◽  
Jan Martinovič
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Urban Change ◽  
Small Scale ◽  
Atmospheric Effects ◽  
Wide Field ◽  
Ground Truth Data ◽  
Automated Method ◽  
Urban Monitoring ◽  
Sar Data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

Sign in / Sign up

Export Citation Format

Share Document