scholarly journals A method to extract cyanobacteria blooms from satellite imagery with no requirements for any prior atmospheric correction or cloud-masking

2021 ◽  
Author(s):  
Haiqiu Liu ◽  
hangzhou Li ◽  
ren kui ◽  
jinxiu Hu
Keyword(s):  
Satellite Imagery ◽  
Atmospheric Correction ◽  
Cyanobacteria Blooms

scholarly journals Atmospheric correction of HJ1-A/B images and the effects on remote sensing monitoring of cyanobacteria bloom

2015 ◽  
Vol 368 ◽  
pp. 69-74
Author(s):  
H. Ma ◽  
S. Guo ◽  
X. Hong ◽  
Y. Zhou
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
Quantum Effect ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Accurate Information ◽  
Correction Model ◽  
Cyanobacteria Bloom ◽  
Donghu Lake ◽  
Cyanobacteria Blooms

Abstract. The HJ-1A/B satellite offers free images with high spatial and temporal resolution, which are effective for dynamically monitoring cyanobacteria blooms. However, the HJ-1A/B satellite also receives distorted signals due to the influence of atmosphere. To acquire accurate information about cyanobacteria blooms, atmospheric correction is needed. HJ-1A/B images were atmosphere corrected using the FLAASH atmospheric correction model. Considering the quantum effect within a certain wavelength range, a spectral response function was included in the process. Then the model was used to process HJ-1A/B images, and the NDVI after atmospheric correction was compared with that before correction. The standard deviation improved from 0.13 to 0.158. Results indicate that atmospheric correction effectively reduces the distorted signals. Finally, NDVI was utilized to monitor the cyanobacteria bloom in Donghu Lake. The accuracy was enhanced compared with that before 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

scholarly journals Elevation-Dependent Removal of Cirrus Clouds in Satellite Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 494
Author(s):  
Daniel Schläpfer ◽  
Rudolf Richter ◽  
Peter Reinartz
Keyword(s):  
Satellite Imagery ◽  
Atmospheric Correction ◽  
Cirrus Clouds ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Detection Algorithms ◽  
Digital Elevation ◽  
Variable Nature ◽  
Critical Issues ◽  
Elevation Model

Masking of cirrus clouds in optical satellite imagery is an important step in automated processing chains. Firstly, it is a prerequisite to a subsequent removal of cirrus effects, and secondly, it affects the atmospheric correction, i.e., aerosol and surface reflectance retrievals. Cirrus clouds can be detected with a narrow bandwidth channel near 1.38 μ m and operational detection algorithms have been developed for Landsat-8 and Sentinel-2 images. However, concerning cirrus removal in the case of elevated surfaces, current methods do not separate the ground reflected signal from the cirrus signal in the 1.38 μ m channel when performing an atmospheric correction, often resulting in an overcorrection of the cirrus influence. We propose a new operational algorithm using a Digital Elevation Model (DEM) to estimate the surface and cirrus cloud contributions in the 1.38 μ m channel and to remove cirrus effects during the surface reflectance retrieval. Due to the highly variable nature of cirrus clouds and terrain conditions, no generic quantitative results could be derived. However, results for typical cases and the achieved improvement in cirrus removal are given for selected scenes and critical issues and limitations of the approach are discussed.

scholarly journals LAND USE AND LAND COVER CLASSIFICATION OF MULTISPECTRAL LANDSAT-8 SATELLITE IMAGERY USING DISCRETE WAVELET TRANSFORM

2018 ◽  
Vol XLII-5 ◽  
pp. 703-706
Author(s):  
J. Sharma ◽  
R. Prasad ◽  
V. N. Mishra ◽  
V. P. Yadav ◽  
R. Bala
Keyword(s):  
Land Use ◽  
Wavelet Transform ◽  
Land Cover ◽  
Discrete Wavelet Transform ◽  
Satellite Imagery ◽  
Classification Accuracy ◽  
Atmospheric Correction ◽  
Discrete Wavelet ◽  
Landsat 8

<p><strong>Abstract.</strong> Land use and land cover (LULC) classification of satellite imagery is an important research area and studied exclusively in remote sensing. However, accurate and appropriate land use/cover detection is still a challenge. This paper presents a wavelet transform based LULC classification using Landsat 8-OLI data. The study area for the present work is a small part of Varanasi district, Uttar Pradesh, India. The atmospheric correction of the image was performed using Quick Atmospheric Correction (QUAC) method. The image was decomposed into its approximation and detail coefficients up to eight levels using discrete wavelet transform (DWT) method. The approximation images were layer stacked with the original image. The minimum distance classifier was used for classifying the image into six LULC classes namely water, agriculture, urban, fallow land, sand, and vegetation. The classification accuracy for all decomposition levels was compared with that of classified product based on original multispectral image. The classification accuracy for multi-spectral image was found to be 75.27%. Whereas, the classification accuracies were found to improve up to 85.97%, 88.87%, 93.47%, 95.03%, 93.01, 92.32% and 90.80% for second, third, fourth, fifth, six, seventh and eight level decomposition, respectively. The significantly improved accuracy was found for images decomposed at level five. Thus, the approach of DWT for LULC classification can be used to increase the classification accuracy significantly.</p>

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