scholarly journals Airborne lidar reflectance measurements at 1.57 μm in support of the A-SCOPE mission for atmospheric CO<sub>2</sub>

2009 ◽  
Vol 2 (3) ◽  
pp. 1487-1536 ◽  
Author(s):  
A. Amediek ◽  
A. Fix ◽  
G. Ehret ◽  
J. Caron ◽  
Y. Durand
Keyword(s):  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Western Europe ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Airborne Lidar ◽  
Measurement Sensitivity ◽  
Airborne Measurements ◽  
Wide Range ◽  
Scope Project

Abstract. The characteristics of the lidar reflectance of the Earth's surface is an important issue for the IPDA lidar technique (integrated path differential absorption lidar) which is the proposed method for the spaceborne measurement of atmospheric carbon dioxide within the framework of ESA's A-SCOPE project. Both, the absolute reflectance of the ground and its variations have an impact on the measurement sensitivity. The first aspect influences the instrument's signal to noise ratio, the second one can lead to retrieval errors, if the ground reflectance changes are strong on small scales. The investigation of the latter is the main purpose of this study. Airborne measurements of the lidar ground reflectance at 1.57 μm wavelength were performed in Central and Western Europe, including many typical land surface coverages as well as the open sea. The analyses of the data show, that the lidar ground reflectance is highly variable on a wide range of spatial scales. However, by means of the assumption of laser footprints on the order of several tens of meters, as planned for spaceborne systems, and by means of an averaging of the data it was shown, that this specific retrieval error is compatible with the sensitivity requirements of spaceborne CO2 measurements.

scholarly journals Airborne lidar reflectance measurements at 1.57 μm in support of the A-SCOPE mission for atmospheric CO<sub>2</sub>

2009 ◽  
Vol 2 (2) ◽  
pp. 755-772 ◽  
Author(s):  
A. Amediek ◽  
A. Fix ◽  
G. Ehret ◽  
J. Caron ◽  
Y. Durand
Keyword(s):  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Western Europe ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Airborne Lidar ◽  
Measurement Sensitivity ◽  
Airborne Measurements ◽  
Wide Range ◽  
Scope Project

Abstract. The characteristics of the lidar reflectance of the Earth's surface is an important issue for the IPDA lidar technique (integrated path differential absorption lidar) which is the proposed method for the spaceborne measurement of atmospheric carbon dioxide within the framework of ESA's A-SCOPE project. Both, the absolute reflectance of the ground and its variations have an impact on the measurement sensitivity. The first aspect influences the instrument's signal to noise ratio, the second one can lead to retrieval errors, if the ground reflectance changes are strong on small scales. The investigation of the latter is the main purpose of this study. Airborne measurements of the lidar ground reflectance at 1.57 μm wavelength were performed in Central and Western Europe, including many typical land surface coverages as well as the open sea. The analyses of the data show, that the lidar ground reflectance is highly variable on a wide range of spatial scales. However, by means of the assumption of laser footprints in the order of several tens of meters, as planned for spaceborne systems, and by means of an averaging of the data it was shown, that this specific retrieval error is well below 1 ppm (CO2 column mixing ratio), and so compatible with the sensitivity requirements of spaceborne CO2 measurements. Several approaches for upscaling the data in terms of the consideration of larger laser footprints, compared to the one used here, are shown and discussed. Furthermore, the collected data are compared to MODIS ground reflectance data.

Fractal Distribution of Snow Depth from Lidar Data

2006 ◽  
Vol 7 (2) ◽  
pp. 285-297 ◽  
Author(s):  
Jeffrey S. Deems ◽  
Steven R. Fassnacht ◽  
Kelly J. Elder
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Multiple Scales ◽  
Driving Forces ◽  
Spatial Scales ◽  
Fractal Dimensions ◽  
Airborne Lidar ◽  
Surface Elevation ◽  
Fractal Distribution ◽  
Wide Range

Abstract Snowpack properties vary dramatically over a wide range of spatial scales, from crystal microstructure to regional snow climates. The driving forces of wind, energy balance, and precipitation interact with topography and vegetation to dominate snow depth variability at horizontal scales from 1 to 1000 m. This study uses land surface elevation, vegetation surface elevation, and snow depth data measured using airborne lidar at three sites in north-central Colorado. Fractal dimensions are estimated from the slope of a log-transformed variogram and demonstrate scale-invariant, fractal behavior in the elevation, vegetation, and snow depth datasets. Snow depth and vegetation topography each show two distinct fractal distributions over different scale ranges (multifractal behavior), with short-range fractal dimensions near 2.5 and long-range fractal dimensions around 2.9 at all locations. These fractal ranges are separated by a scale break at 15–40 m, depending on the site, which indicates a process change at that scale. Terrain has a fractal distribution over nearly the entire range of scales available in the data. Directional differences in the fractal dimensions for each parameter are also present at multiple scales, and are related to the wind direction frequency distributions at each site. The results indicate that different sampling resolutions may yield different results and allow rescaling in specific scale ranges. Resolutions of 10 m and finer are consistently self-similar, as are resolutions greater than 30 m, though the coarser resolutions show nearly random distributions.

scholarly journals A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

2014 ◽  
Vol 7 (5) ◽  
pp. 2091-2105 ◽  
Author(s):  
G. S. H. Pau ◽  
G. Bisht ◽  
W. J. Riley
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Approximation Error ◽  
Reduced Order Modeling ◽  
Data Set ◽  
Reduced Order ◽  
Coarse Resolution ◽  
Wide Range ◽  
Study Sites

Abstract. Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO2, CH4) exchanges with the atmosphere range from the molecular scale (pore-scale O2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" that reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 103) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.

scholarly journals Analysis and Reduction of Systematic Errors through a Seamless Approach to Modeling Weather and Climate

2010 ◽  
Vol 23 (22) ◽  
pp. 5933-5957 ◽  
Author(s):  
G. M. Martin ◽  
S. F. Milton ◽  
C. A. Senior ◽  
M. E. Brooks ◽  
S. Ineson ◽  
...  
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Full Range ◽  
Model Development ◽  
Model Performance ◽  
Systematic Errors ◽  
Wide Range

Abstract The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.

Terrestrial Biological Nitrogen Fixation in CMIP6 Models

2020 ◽  
Author(s):  
Taraka Davies-Barnard ◽  
Johannes Meyerholt ◽  
Sönke Zaehle ◽  
Pierre Friedlingstein ◽  
Victor Brovkin ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Nitrogen Fixation ◽  
Land Surface ◽  
Biological Nitrogen Fixation ◽  
Atmospheric Carbon Dioxide ◽  
Meta Analysis ◽  
Field Measurements ◽  
Carbon Dioxide Concentrations ◽  
Wide Range ◽  
Biological Nitrogen

&lt;p&gt;Biological nitrogen fixation (BNF) is a key contributor to sustaining the terrestrial carbon cycle, providing nitrogen input that plants require. This is particularly salient for projections of carbon uptake under increased atmospheric carbon dioxide concentrations, which may allow for so-called &amp;#8216;carbon dioxide fertilisation&amp;#8217; if other plant requirements, such as nitrogen, do not prevent increases in productivity. The amount, processes, and global distribution of BNF is highly disputed and consequently land surface models represent it in varying ways. Looking at the latest generation of CMIP6 earth system models with terrestrial nitrogen cycles, we consider their performance with regard to BNF. We assess models against a new comprehensive meta-analysis of BNF field measurements that gives a global range and site-specific values. We find that compared to the wide range of upscaled observations, the models still have a larger range, with under and overestimates.&lt;/p&gt;

scholarly journals A reduced order modeling approach to represent subgrid-scale hydrological dynamics for regional- and climate-scale land-surface simulations: application in a polygonal tundra landscape

2014 ◽  
Vol 7 (2) ◽  
pp. 2125-2172 ◽  
Author(s):  
G. S. H. Pau ◽  
G. Bisht ◽  
W. J. Riley
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Approximation Error ◽  
Reduced Order Modeling ◽  
Training Dataset ◽  
Reduced Order ◽  
Coarse Resolution ◽  
Wide Range ◽  
Study Sites

Abstract. Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO2, CH4) exchanges with the atmosphere range from molecular scale (pore-scale O2 consumption) to tens of kilometer scale (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a particular reduced-order modeling (ROM) technique known as "Proper Orthogonal Decomposition mapping method" that reconstructs temporally-resolved fine-resolution solutions based on coarse-resolution solutions. We applied this technique to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface-subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the four study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 103) with very small relative approximation error (< 0.1%) for two validation years not used in training the ROM. We also demonstrated that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training dataset with relatively good accuracy (< 1.5% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. This method has the potential to efficiently increase the resolution of land models for coupled climate simulations, allowing LSMs to be used at spatial scales consistent with mechanistic physical process representation.

scholarly journals Detectivity optimization to detect of ultraweak light fluxes with an EM-CCD as binary photon counter array

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ibtissame Khaoua ◽  
Guillaume Graciani ◽  
Andrey Kim ◽  
François Amblard
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Detection Methods ◽  
Strong Nonlinearity ◽  
Wide Range ◽  
Depth Analysis ◽  
Spatially Extended ◽  
Extended Sources ◽  
Photon Counting Mode

AbstractFor a wide range of purposes, one faces the challenge to detect light from extremely faint and spatially extended sources. In such cases, detector noises dominate over the photon noise of the source, and quantum detectors in photon counting mode are generally the best option. Here, we combine a statistical model with an in-depth analysis of detector noises and calibration experiments, and we show that visible light can be detected with an electron-multiplying charge-coupled devices (EM-CCD) with a signal-to-noise ratio (SNR) of 3 for fluxes less than $$30\,{\text{photon}}\,{\text{s}}^{ - 1} \,{\text{cm}}^{ - 2}$$ 30 photon s - 1 cm - 2 . For green photons, this corresponds to 12 aW $${\text{cm}}^{ - 2}$$ cm - 2 ≈ $$9{ } \times 10^{ - 11}$$ 9 × 10 - 11 lux, i.e. 15 orders of magnitude less than typical daylight. The strong nonlinearity of the SNR with the sampling time leads to a dynamic range of detection of 4 orders of magnitude. To detect possibly varying light fluxes, we operate in conditions of maximal detectivity $${\mathcal{D}}$$ D rather than maximal SNR. Given the quantum efficiency $$QE\left( \lambda \right)$$ Q E λ of the detector, we find $${ \mathcal{D}} = 0.015\,{\text{photon}}^{ - 1} \,{\text{s}}^{1/2} \,{\text{cm}}$$ D = 0.015 photon - 1 s 1 / 2 cm , and a non-negligible sensitivity to blackbody radiation for T > 50 °C. This work should help design highly sensitive luminescence detection methods and develop experiments to explore dynamic phenomena involving ultra-weak luminescence in biology, chemistry, and material sciences.

Modal frequencies of bridges from GNSS (GPS) monitoring data: Experimental, statistical evidence

2021 ◽  
Vol 17 (1-2) ◽  
pp. 3-14
Author(s):  
Stathis C. Stiros ◽  
F. Moschas ◽  
P. Triantafyllidis
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Axis ◽  
Bridge Monitoring ◽  
Low Snr ◽  
True Value ◽  
Gps Satellites ◽  
Wide Range ◽  
Spectral Peaks ◽  
Monitoring Survey ◽  
True Frequency

GNSS technology (known especially for GPS satellites) for measurement of deflections has proved very efficient and useful in bridge structural monitoring, even for short stiff bridges, especially after the advent of 100 Hz GNSS sensors. Mode computation from dynamic deflections has been proposed as one of the applications of this technology. Apart from formal modal analyses with GNSS input, and from spectral analysis of controlled free attenuating oscillations, it has been argued that simple spectra of deflections can define more than one modal frequencies. To test this scenario, we analyzed 21 controlled excitation events from a certain bridge monitoring survey, focusing on lateral and vertical deflections, recorded both by GNSS and an accelerometer. These events contain a transient and a following oscillation, and they are preceded and followed by intervals of quiescence and ambient vibrations. Spectra for each event, for the lateral and the vertical axis of the bridge, and for and each instrument (GNSS, accelerometer) were computed, normalized to their maximum value, and printed one over the other, in order to produce a single composite spectrum for each of the four sets. In these four sets, there was also marked the true value of modal frequency, derived from free attenuating oscillations. It was found that for high SNR (signal-to-noise ratio) deflections, spectral peaks in both acceleration and displacement spectra differ by up to 0.3 Hz from the true value. For low SNR, defections spectra do not match the true frequency, but acceleration spectra provide a low-precision estimate of the true frequency. This is because various excitation effects (traffic, wind etc.) contribute with numerous peaks in a wide range of frequencies. Reliable estimates of modal frequencies can hence be derived from deflections spectra only if excitation frequencies (mostly traffic and wind) can be filtered along with most measurement noise, on the basis of additional data.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

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