scholarly journals Sensitivity analysis of the dark spectrum fitting atmospheric correction for metre- and decametre-scale satellite imagery using autonomous hyperspectral radiometry

2020 ◽  
Vol 28 (20) ◽  
pp. 29948 ◽  
Author(s):  
Quinten Vanhellemont
Keyword(s):  
Sensitivity Analysis ◽  
Satellite Imagery ◽  
Atmospheric Correction ◽  
Spectrum Fitting

scholarly journals Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1923 ◽  
Author(s):  
Jochem Verrelst ◽  
Jorge Vicent ◽  
Juan Pablo Rivera-Caicedo ◽  
Maria Lumbierres ◽  
Pablo Morcillo-Pallarés ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Concept Maps ◽  
Global Sensitivity Analysis ◽  
Dominant Role ◽  
Atmospheric Correction ◽  
Total Contribution ◽  
Area Index ◽  
Global Sensitivity ◽  
Radiance Data ◽  
Leaf Canopy

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm − 1 (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.

scholarly journals The methods and automatic technology aimed at imagery georeferencing, cloud screening, atmospheric and radiometric correction of KMSS-M satellite data

2021 ◽  
Vol 333 ◽  
pp. 01006
Author(s):  
Pavel Kolbudaev ◽  
Dmitry Plotnikov ◽  
Evgeny Loupian ◽  
Andrey Proshin ◽  
Alexey Matveev
Keyword(s):  
Satellite Imagery ◽  
Spectral Band ◽  
Atmospheric Correction ◽  
Processing Method ◽  
Reference Image ◽  
Radiometric Correction ◽  
Observation Systems ◽  
High Flexibility ◽  
Cloud Screening ◽  
Wide Swath

In this study we present methods and automatic technology developed for routine processing of satellite imagery acquired by cameras MSU-201 and MSU-202 (KMSS-M) onboard Meteor-M №2. The developed methods were aimed at imagery georeferencing issues fixing, clouds and shadows detection as well as atmospheric and radiometric correction. Basing on these methods we built an automatic technology and complete KMSS-M data processing chain which provided analysis ready dataset for Russian grain belt and adjacent areas of neighboring countries for the year 2020. Method for imagery georeferencing was based on Pearson’s correlation localized maximization when compared to the georefenced and cloudfree coarse-resolution reference image produced in IKI RAS through MOD09 product time series processing. Method for clouds and shadows detection was based both on the spatial analysis of outputs from geocorrection step and auxiliary image, characterizing georeferenced KMSS-M image values relative accordance with the IKI reference image. The atmospheric correction was based on localized histogram matching of KMSS-M and IKI reference date-corresponding imagery, and thereby concurrently performed radiometric correction of KMSS-M data, compensating effects of varying viewing and illumination geometry which explicitly manifest across 960-km-wide swath area. The developed methods are noticeably minimalistic, requiring only one target spectral band to perform properly. Due to high flexibility and robustness, they also may be applied to raw satellite imagery acquired from various Earth observation systems, including Russian systems of high and moderate spatial resolution. The technology is currently being deployed in an operative mode for several test sites of Russia since the year 2021 onwards.

Atmospheric Correction Method for Sentinel-2 Satellite Imagery

2018 ◽  
Vol 38 (1) ◽  
pp. 0128001 ◽  
Author(s):  
苏伟 Su Wei ◽  
张明政 Zhang Mingzheng ◽  
蒋坤萍 Jiang Kunping ◽  
朱德海 Zhu Dehai ◽  
黄健熙 Huang Jianxi ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Atmospheric Correction ◽  
Correction Method ◽  
Sentinel 2
Sign in / Sign up

Export Citation Format

Share Document