scholarly journals A physics-based approach to oversample multi-satellite, multi-species observations to a common grid

2018 ◽  
Author(s):  
Kang Sun ◽  
Lei Zhu ◽  
Karen Cady-Pereira ◽  
Christopher Chan Miller ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Response Function ◽  
Gaussian Function ◽  
Satellite Observation ◽  
Atmospheric Composition ◽  
Response Functions ◽  
Spatial Response ◽  
Sensitivity Distribution ◽  
Level 3 ◽  
Spatial Response Function ◽  
Level 2

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (Level 2) to a regular grid (Level 3). When averaging Level 2 data over a long time period to a target Level 3 grid significantly finer than Level 2 pixels, this process is referred to as "oversampling'". An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous approaches. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOV) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid resolution, the ground size of FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for high resolution oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grid resolutions finer than ~16 km. The generalized 2-D super Gaussian function also enables smoothing of the Level 3 results by decreasing the shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO2 products and IASI NH3 products, showing substantially improved visualization of trace gas distribution and local gradients.

scholarly journals A physics-based approach to oversample multi-satellite, multispecies observations to a common grid

2018 ◽  
Vol 11 (12) ◽  
pp. 6679-6701 ◽  
Author(s):  
Kang Sun ◽  
Lei Zhu ◽  
Karen Cady-Pereira ◽  
Christopher Chan Miller ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Response Function ◽  
Gaussian Function ◽  
Computational Time ◽  
Response Functions ◽  
Fine Grid ◽  
Spatial Response ◽  
Sensitivity Distribution ◽  
Level 3 ◽  
Spatial Response Function ◽  
Level 2

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as “oversampling”. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.

scholarly journals The CMEMS GlobColour <i>Chlorophyll-a</i> Product Based on Satellite Observation

2019 ◽  
Author(s):  
Philippe Garnesson ◽  
Antoine Mangin ◽  
Odile Fanton d'Andon ◽  
Julien Demaria ◽  
Marine Bretagnon
Keyword(s):  
Satellite Observation ◽  
Global Level ◽  
Marine Strategy Framework Directive ◽  
Spatial Coverage ◽  
Long Time ◽  
Level 3 ◽  
Monitoring Service ◽  
Marine Environmental ◽  
Level 2 ◽  
The Eu

Abstract. This work concerns the chlorophyll products based on Satellite Observation and disseminated in the frame of the Copernicus Marine Environmental Monitoring Service (CMEMS). This work highlights the main advantages provided by the Copernicus Globcolour processor which is used to serve the CMEMS with a long time series from 1997 to present with level 3 &amp; 4 products at Global level (4 km of spatial resolution) and for the Atlantic level 4 product (1 km). It discusses the different ways to merge data coming from different sensors and it is shown that the GlobColour processor approach provide a better flexibility. At present, it is the only one CMEMS processor able to ingest the OLCI-S3A in the merged product (OLCI-S3A data are ingested in the operational CMEMS products since the April 2018 release). Behind the merging, the flagging strategy to go from level 2 provided by spatial agencies to the level 3 CMEMS products is also discussed. A better spatial coverage is demonstrated, including the coastal area which is of particular interest for many users involved in the EU Water Framework and Marine Strategy Framework Directive.

A Parameterized ASCAT Measurement Spatial Response Function

2016 ◽  
Vol 54 (8) ◽  
pp. 4570-4579 ◽  
Author(s):  
Richard D. Lindsley ◽  
Craig Anderson ◽  
Julia Figa-Saldana ◽  
David G. Long
Keyword(s):  
Response Function ◽  
Spatial Response ◽  
Spatial Response Function

scholarly journals Integration model of POSP measurement spatial response function

2020 ◽  
Vol 28 (17) ◽  
pp. 25480
Author(s):  
Xuefeng Lei ◽  
Shuangshuang Zhu ◽  
Zhenyang Li ◽  
Jin Hong ◽  
Zhenhai Liu ◽  
...  
Keyword(s):  
Response Function ◽  
Integration Model ◽  
Spatial Response ◽  
Spatial Response Function

scholarly journals Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel

2010 ◽  
Vol 10 (20) ◽  
pp. 9761-9772 ◽  
Author(s):  
H. M. Deneke ◽  
R. A. Roebeling
Keyword(s):  
High Resolution ◽  
Response Function ◽  
Spatial Response ◽  
High Resolution Images ◽  
Spatial Response Function ◽  
Standard Deviations ◽  
Narrowband Channels ◽  
A Minor ◽  
Frequency Variations ◽  
Spectral Channels

Abstract. An algorithm is introduced to downscale the 0.6 and 0.8 μm spectral channels of the METEOSTAT SEVIRI satellite imager from 3×3 km2 (LRES) to 1×1 km2 (HRES) resolution utilizing SEVIRI's high-resolution visible channel (HRV). Intermediate steps include the coregistration of low- and high-resolution images, lowpass filtering of the HRV channel with the spatial response function of the narrowband channels, and the estimation of a least-squares linear regression model for linking high-frequency variations in the HRV and narrowband images. The importance of accounting for the sensor spatial response function for matching reflectances at different spatial resolutions is demonstrated, and an estimate of the accuracy of the downscaled reflectances is provided. Based on a 1-year dataset of Meteosat SEVIRI images, it is estimated that on average, the reflectance of a HRES pixel differs from that of an enclosing LRES pixel by standard deviations of 0.049 and 0.052 in the 0.6 and 0.8 μm channels, respectively. By applying our downscaling algorithm, explained variance of 98.2 and 95.3 percent are achieved for estimating these deviations, corresponding to residual standard deviations of only 0.007 and 0.011 for the respective channels. For this dataset, a minor misregistration of the HRV channel relative to the narrowband channels of 0.36±0.11 km in East and 0.06±0.10 km in South direction is observed and corrected for, which should be negligible for most applications.

Reduction of voxel bleeding in highly accelerated parallel1H MRSI by direct control of the spatial response function

2014 ◽  
Vol 73 (2) ◽  
pp. 469-480 ◽  
Author(s):  
Thomas Kirchner ◽  
Ariane Fillmer ◽  
Jeffrey Tsao ◽  
Klaas Paul Pruessmann ◽  
Anke Henning
Keyword(s):  
Response Function ◽  
Direct Control ◽  
Spatial Response ◽  
Spatial Response Function

scholarly journals Downscaling of METEOSAT SEVIRI 0.6 and 0.8 micron channel radiances utilizing the high-resolution visible channel

2010 ◽  
Vol 10 (4) ◽  
pp. 10707-10740 ◽  
Author(s):  
H. M. Deneke ◽  
R. Roebling
Keyword(s):  
High Resolution ◽  
Response Function ◽  
Spatial Response ◽  
High Resolution Images ◽  
Spatial Response Function ◽  
Standard Deviations ◽  
Narrowband Channels ◽  
A Minor ◽  
Frequency Variations ◽  
Spectral Channels

Abstract. An algorithm is introduced to downscale the 0.6 and 0.8 micron spectral channels of the METEOSTAT SEVIRI satellite imager from 3×3 km2 (LRES) to 1×1 km2 (HRES) resolution utilizing SEVIRI's high-resolution visible channel (HRVIS). Intermediate steps include the coregistration of low- and high-resolution images, lowpass filtering of the HRVIS channel with the spatial response function of the narrowband channels, and the estimation of a least-squares linear regression model for linking high-frequency variations in the HRVIS and narrowband images. The importance of accounting for the sensor spatial response function for matching reflectances at different spatial resolutions is demonstrated, and an estimate of the accuracy of the downscaled reflectances is provided. Based on a 1-year dataset of Meteosat SEVIRI images, it is estimated that on average, the reflectance of a HRES pixel differs from that of an enclosing LRES pixel by standard deviations of 0.049 and 0.052 in the 0.6 and 0.8 micron channels, respectively. By applying our downscaling algorithm, explained variance of 98.2 and 95.3 percent are achieved for estimating these deviations, corresponding to residual standard deviations of only 0.007 and 0.011 for the respective channels. For this dataset, a minor misregistration of the HRVIS channel relative to the narrowband channels of 0.36±0.11 km in East and 0.06±0.10 km in South direction is observed and corrected for, which should be negligible for most applications.

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