scholarly journals 3D-VAR multilayer assimilation of X-band SAR data into a detailed snowpack model

2013 ◽  
Vol 7 (5) ◽  
pp. 4881-4912 ◽  
Author(s):  
X. V. Phan ◽  
L. Ferro-Famil ◽  
M. Gay ◽  
Y. Durand ◽  
M. Dumont ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Remote Sensing Data ◽  
Backscattering Coefficient ◽  
Snow Density ◽  
X Band ◽  
Dense Media ◽  
Sar Data ◽  
Electromagnetic Backscattering ◽  
Assimilation Scheme ◽  
Structure Properties

Abstract. We introduce a variational data assimilation scheme to assimilate X-band Synthetic Aperture Radar (SAR) data into a snowpack evolution model. The structure properties of a snowpack, such as snow density and grain optical diameter of each layer, are simulated over a period of time by the snow metamorphism model Crocus, fed by the local reanalysis SAFRAN at a French alpine location. These parameters are used as inputs of an Electromagnetic Backscattering Model (EBM) based on Dense Media Radiative Transfer (DMRT) theory, which calculates the simulated total backscattering coefficient. Next, 3D-VAR data assimilation is implemented in order to minimize the discrepancies between model simulations and observations obtained from SAR acquisitions, by modifying the parameters of a multilayer snowpack calculated by Crocus. The algorithm then reinitializes Crocus with the optimized snowpack structure properties, and therefore allows it to continue the simulation of snowpack evolution where adjustments based on remote sensing data has been taken into account. Results obtained using TerraSAR-X acquisitions on Argentière Glacier (Mont-Blanc massif, French Alps) show the high potential of this method for improving snow cover simulation.

scholarly journals 1D-Var multilayer assimilation of X-band SAR data into a detailed snowpack model

2014 ◽  
Vol 8 (5) ◽  
pp. 1975-1987 ◽  
Author(s):  
X. V. Phan ◽  
L. Ferro-Famil ◽  
M. Gay ◽  
Y. Durand ◽  
M. Dumont ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Meteorological Data ◽  
Physical Parameters ◽  
Data Assimilation Scheme ◽  
X Band ◽  
Sar Data ◽  
Synthetic Aperture Radars ◽  
Snowpack Properties ◽  
Assimilation Scheme

Abstract. The structure and physical properties of a snowpack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential divergences and accumulated errors, to a limited spatial resolution, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and improve its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parameters, like its depth, snow grain size and density. SAR acquisitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectivity of a snowpack from a set of physical descriptors, in order to constrain a physical snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the snowpack evolution model Crocus. The physical properties of a snowpack, such as snow density and optical diameter of each layer, are simulated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snowpack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensitivity of the EBM to snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observations obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated snowpack. The algorithm then re-initializes Crocus with the modified snowpack physical parameters, allowing it to continue the simulation of snowpack evolution, with adjustments based on remote sensing information. This method is evaluated using multi-temporal TerraSAR-X images acquired over the specific site of the Argentière glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of snowpack simulated by Crocus.

scholarly journals Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads

2020 ◽  
Vol 12 (9) ◽  
pp. 1507 ◽  
Author(s):  
Franz J. Meyer ◽  
Olaniyi A. Ajadi ◽  
Edward J. Hoppe
Keyword(s):  
High Resolution ◽  
Remote Sensing Data ◽  
Ride Quality ◽  
International Roughness Index ◽  
Pavement Roughness ◽  
X Band ◽  
The Us ◽  
Network Scale ◽  
Sar Data ◽  
Satellite Radar

The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial.

scholarly journals A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

2021 ◽  
Vol 13 (3) ◽  
pp. 360
Author(s):  
Wensheng Wang ◽  
Martin Gade ◽  
Kerstin Stelzer ◽  
Jörn Kohlus ◽  
Xinyu Zhao ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Wadden Sea ◽  
Relative Difference ◽  
Polarimetric Synthetic Aperture Radar ◽  
Intertidal Flats ◽  
Sediment Types ◽  
X Band ◽  
Good Potential ◽  
L Band ◽  
Sar Data

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

scholarly journals Snow dielectric properties: from DC to microwave X-band

1994 ◽  
Vol 19 ◽  
pp. 92-96 ◽  
Author(s):  
TH. Achammer ◽  
A. Denoth
Keyword(s):  
Grain Size ◽  
Dielectric Properties ◽  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Frequency Range ◽  
Snow Density ◽  
Cold Room ◽  
X Band ◽  
Grain Size And Shape

Broadband measurements of dielectric properties of natural snow samples near or at 0°C are reported. Measurement quantities are: dielectric permittivity, loss factor and complex propagation factor for electromagnetic waves. X-band measurements were made in a cold room in the laboratory; measurements at low and intermediate frequencies were carried out both in the field (Stubai Alps, 3300 m; Hafelekar near Innsbruck, 2100 m) and in the cold room. Results show that in the different frequency ranges the relative effect on snow dielectric properties of the parameters: density, grain-size and shape, liquid water content, shape and distribution of liquid inclusions and content of impurities, varies significantly. In the low-frequency range the influence of grain-size and shape and snow density dominates; in the medium-frequency range liquid water content and density are the dominant parameters. In the microwave X-band the influence of the amount, shape and distribution of liquid inclusions and snow density is more important than that of the remaining parameters.

An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

2021 ◽  
Author(s):  
Bingyu Zhao ◽  
Meiling Liu ◽  
Jiianjun Wu ◽  
Xiangnan Liu ◽  
Mengxue Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Growth Model ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Growth Conditions ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Parallel Method ◽  
Rice Growth

<p>It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.</p>

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