ASTER TIR Radiometric Calibration and Atmospheric Correction

2010 ◽  
pp. 117-132 ◽  
Author(s):  
Hideyuki Tonooka
Keyword(s):  
Atmospheric Correction ◽  
Radiometric Calibration

scholarly journals Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data – ValCalHyp airborne hyperspectral campaign under the EUFAR framework

2014 ◽  
Vol XL-7 ◽  
pp. 51-53
Author(s):  
A. Brook ◽  
E. Ben Dor
Keyword(s):  
Atmospheric Correction ◽  
Ground Truth ◽  
Critical Issue ◽  
Radiometric Calibration ◽  
Vicarious Calibration ◽  
Flight Direction ◽  
Novel Approach ◽  
Novel Method ◽  
General Goal ◽  
Cross Calibration

A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

scholarly journals IMAGENS DO LANDSAT- 8 NO MAPEAMENTO DE SUPERFÍCIES EM ÁREA IRRIGADA

Irriga ◽  
2015 ◽  
Vol 1 (2) ◽  
pp. 30-36
Author(s):  
JANNAYLTON EVERTON OLIVIERA SANTOS ◽  
Donizeti Aparecido Pastori Nicolete ◽  
Roberto Filgueiras ◽  
Victor Costa Leda ◽  
Célia Regina Lopes Zimback
Keyword(s):  
Vegetation Index ◽  
Atmospheric Correction ◽  
Growth Stages ◽  
Landsat 8 ◽  
Land Uses ◽  
Radiometric Calibration ◽  
Estimation Of Parameters ◽  
Irrigated Area ◽  
Dark Object ◽  
Center Pivot

IMAGENS DO LANDSAT- 8 NO MAPEAMENTO DE SUPERFÍCIES EM ÁREA IRRIGADA  JANNAYLTON ÉVERTON OLIVEIRA SANTOS¹; DONIZETI APARECIDO PASTORI NICOLETE¹; ROBERTO FILGUEIRAS¹; VICTOR COSTA LEDA² E CÉLIA REGINA LOPES ZIMBACK¹ [1] Departamento de Ciência do Solo e Recursos Ambientais da UNESP - campus Botucatu – SP,Programa de Irrigação e Drenagem UNESP/FCA. Email:[email protected], [email protected], [email protected], [email protected] Departamento de Ciência do Solo e Recursos Ambientais da UNESP - campus Botucatu – SP, Programa de Energia na agricultura UNESP/FCA. Email: [email protected]  1 RESUMO O trabalho tem como objetivo analisar os parâmetros NDVI (Normalized Difference Vegetation Index) e SAVI (Soil Adjusted Vegetation Index) para dois períodos, chuvoso e seco, em área irrigada. A área de estudo apresenta constante expansão na irrigação por pivô central, sendo localizada nas proximidades do município de Paranapanema – SP. As imagens foram processadas utilizando o programa QGIS 2.2. Para a obtenção dos índices realizou-se a calibração radiométrica, que consiste na transformação dos números digitais para correspondentes físicos, radiância e reflectância, e correção atmosférica por meio do método DOS 1 (Dark Object Substraction). Após os processamentos computou-se os índices de vegetação, os quais deram subsídio para o monitoramento das culturas agrícolas nos diferentes manejos (irrigado e sequeiro) e épocas de análise (chuvoso e seco). Como auxílio para o monitoramento das áreas, fusionou-se uma composição RGB 432, com a banda pancromática, o que permitiu uma pré-análise das condições e dos tipos de uso do solo na área de estudo. As cartas obtidas de NDVI e SAVI permitiram inferir sobre as condições fisiológicas e estádios fenológicos da vegetação nos diferentes usos do solo. No período de estiagem os índices médios obtiveram valores inferiores ao do período chuvoso, tendo isto ocorrido, principalmente, devido as condições de estresse hídrico característico da época. Desse modo, o cômputo dos parâmetros para a área de estudo foram de extrema valia na análise das condições da vegetação nos diferentes cenários, pois por meio desses foi possível inferir sobre as diferenças encontradas nos períodos e nos diferentes usos do solo, o que auxilia os agricultores em tomadas de decisão com relação ao manejo de suas áreas, no que tange as questões relacionadas a necessidades hídrica das culturas.Palavras-chave: Sensoriamento remoto, monitoramento agrícola, pivô central.  SANTOS, J. E. O.; NICOLETE, D. A. P.; FILGUEIRAS, R.; LEDA, V. C.; ZIMBACK, C. R. L.IMAGES OF LANDSAT-8 TO MONITOR THE SURFACES ON IRRIGATED AREA    2 ABSTRACT The study aims to analyze NDVI (Difference Vegetation Index Normalized) and SAVI (Soil Adjusted Vegetation Index) for two periods (rainy and dry) on irrigated area. The study area has constant expansion on irrigation center pivot, it is located near the Paranapanema ­- SP county. For this study we used two images of Landsat ­8 orbital platform. The images were processed using QGIS 2.2 program. To obtain the indexes, it was held radiometric calibration, which is the transformation of digital numbers in corresponding physical, radiance and reflectance, and atmospheric correction using the DOS method (Dark Object Substraction). These procedures were performed on semi automatic classification plugin. After appropriate calibrations and corrections, it were computed the vegetation indexes. These gave allowance for monitoring agricultural crops in different management systems (irrigated and rainfed) and analysis of seasons (wet and dry). As an aid for monitoring areas, we merged a RGB ­432 composition, with a panchromatic band. This product allowed a pre - analysis of conditions and types of land use in the study area. The maps obtained from NDVI and SAVI, allowed to infer about the physiological conditions and growth stages vegetation in different land uses. During the dry season, we found average rates which has lower values than the rainy season. This occurred, mainly, due to water stress conditions, which is characteristic of that season. Thus, the estimation of parameters for the study area were extremely valuable in analysis of vegetation conditions, on different scenarios, because through these, became possible to infer about the differences in seasons analized and different land uses. Then, these analisys served as an aid for farmers in decision­ making, regard the management of their areas, which is related to water requirements of crops. Keywords: Remote sensing, agriculture monitoring, center pivot.

scholarly journals CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 339-341
Author(s):  
R. Kaczynski ◽  
A. Rylko
Keyword(s):  
Supervised Classification ◽  
Local Population ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Landsat Tm ◽  
Aerial Photographs ◽  
Radiometric Calibration ◽  
Topographic Map ◽  
Ground Truth Data ◽  
Color Composite

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.

scholarly journals MODIS Aqua Reflective Solar Band Calibration for NASA’s R2018 Ocean Color Products

2019 ◽  
Vol 11 (19) ◽  
pp. 2187 ◽  
Author(s):  
Lee ◽  
Meister ◽  
Franz
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Chlorophyll Concentration ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Radiometric Calibration ◽  
Coherent Noise ◽  
Blue Band ◽  
Global Trends ◽  
Moderate Resolution Imaging Spectroradiometer

Remote-sensing ocean color products have stringent requirements on radiometric calibration stability. To address a calibration deficiency in Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua in recent years, the NASA Ocean Biology Processing Group (OBPG) developed a new calibration for reflective solar bands. Prior to the reprocessing of NASA’s ocean color products for 2018 (R2018), the OBPG MODIS products had been based on calibration provided by the MODIS Calibration Support Team (MCST). Several modifications were made to the MCST calibration approach to improve the calibration accuracy for ocean color products. These include 1) applying 936-nm detector normalization to solar diffuser stability monitor (SDSM) data to reduce coherent noise; 2) modeling solar diffuser (SD) degradation wavelength dependency to determine SD degradation in near-infrared and shortwave infrared wavelengths; 3) computing detector gains using SD screen-closed data to better match ocean radiance levels in all bands; 4) performing a simple atmospheric correction to reduce bidirectional reflectance distribution function (BRDF) effects in desert trends; 5) estimating and using modulated relative spectral response (RSR) impact on ocean data to adjust the calibration coefficients; 6) using smoothing to characterize the temporal change in calibration; and characterizing response versus scan angle (RVS) changes using 2nd-order polynomials to improve spatial/temporal calibration stability. Relative to the previous R2014 ocean color products, the R2018 calibration removed the suspect late-mission global trends in blue-band water-leaving reflectance and some anomalously large short-term variability (spikes) in the temporal trend of chlorophyll concentration. This paper will describe the OBPG calibration with a focus on the differences between the MCST and OBPG approaches.

Overview of atmospheric correction and radiometric calibration efforts during FIFE

1992 ◽  
Vol 97 (D17) ◽  
pp. 18731 ◽  
Author(s):  
Rangasayi N. Halthore ◽  
Brian L. Markham
Keyword(s):  
Atmospheric Correction ◽  
Radiometric Calibration

scholarly journals Open-Source Analysis of Submerged Aquatic Vegetation Cover in Complex Waters Using High-Resolution Satellite Remote Sensing: An Adaptable Framework

2022 ◽  
Vol 14 (2) ◽  
pp. 267
Author(s):  
Arthur de Grandpré ◽  
Christophe Kinnard ◽  
Andrea Bertolo
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
High Resolution ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Spatial Scales ◽  
Atmospheric Correction ◽  
Source Analysis ◽  
Radiometric Calibration ◽  
Ecological Data

Despite being recognized as a key component of shallow-water ecosystems, submerged aquatic vegetation (SAV) remains difficult to monitor over large spatial scales. Because of SAV’s structuring capabilities, high-resolution monitoring of submerged landscapes could generate highly valuable ecological data. Until now, high-resolution remote sensing of SAV has been largely limited to applications within costly image analysis software. In this paper, we propose an example of an adaptable open-sourced object-based image analysis (OBIA) workflow to generate SAV cover maps in complex aquatic environments. Using the R software, QGIS and Orfeo Toolbox, we apply radiometric calibration, atmospheric correction, a de-striping correction, and a hierarchical iterative OBIA random forest classification to generate SAV cover maps based on raw DigitalGlobe multispectral imagery. The workflow is applied to images taken over two spatially complex fluvial lakes in Quebec, Canada, using Quickbird-02 and Worldview-03 satellites. Classification performance based on training sets reveals conservative SAV cover estimates with less than 10% error across all classes except for lower SAV growth forms in the most turbid waters. In light of these results, we conclude that it is possible to monitor SAV distribution using high-resolution remote sensing within an open-sourced environment with a flexible and functional workflow.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MONITORAMENTO DE MACRÓFITAS AQUÁTICAS NO RESERVATÓRIO DE BARRA BONITA, SP

Irriga ◽  
2017 ◽  
Vol 22 (2) ◽  
pp. 330-342
Author(s):  
Renata Teixeira de Almeida Minhoni ◽  
Mírian Paula Medeiros André Pinheiro ◽  
Roberto Filgueiras ◽  
Celia Regina Lopes Zimback
Keyword(s):  
Supervised Classification ◽  
Aquatic Macrophytes ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Boundary Definition ◽  
E Mail

SENSORIAMENTO REMOTO APLICADO AO MONITORAMENTO DE MACRÓFITAS AQUÁTICAS NO RESERVATÓRIO DE BARRA BONITA, SP  RENATA TEIXEIRA DE ALMEIDA MINHONI1; MÍRIAN PAULA MEDEIROS ANDRÉ PINHEIRO2; ROBERTO FILGUEIRAS3 E CÉLIA REGINA LOPES ZIMBACK4 1 Eng. Ambiental, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrônoma, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrícola e Ambiental, Doutorando em Engenharia Agrícola – UFV. Avenida Peter Henry Rolfs, s/n - Campus Universitário, CEP 36570-900, Viçosa - MG, e-mail: [email protected] Eng. Agrônoma, Professora. Doutora do Departamento de Solos e Recursos Ambientais - FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected]  1 RESUMO Macrófitas aquáticas são organismos fotossintéticos, com tamanho suficiente para serem vistos a olho nu, que crescem submersas, flutuando ou sobre a superfície da água. A ação antrópica no represamento de corpos hídricos tem ocasionado a eutrofização dos recursos hídricos, e dentre os desequilíbrios que esta ação gera no meio aquático está à elevada proliferação de macrófitas. Devido a esse fato, essa pesquisa foi desenvolvida com o objetivo de realizar uma estimativa da área ocupada por macrófitas aquáticas no reservatório da Usina Hidrelétrica de Barra Bonita (SP), nos anos de 2013, 2014 e 2015. O estudo foi realizado na estação seca (mês de agosto), por meio do uso do NDVI (Normalized Difference Vegetation Index) e classificação supervisionada MAXVER (Máxima Verossimilhança). Para obtenção dos mapas e gráficos, foram realizadas as seguintes ações: seleção das imagens do satélite LANDSAT-8/OLI, calibração radiométrica, correção atmosférica, reprojeção, definição do limite, recorte da área, NDVI e classificação supervisionada. Os mapas obtidos por meio da classificação supervisionada, auxiliada pelos mapas de NDVI, apontaram para um aumento de aproximadamente 50% na área ocupada por macrófitas aquáticas de 2013 a 2015. Palavras-chave: classificação supervisionada, eutrofização, índice NDVI, landsat-8.  MINHONI, R. T. A.; PINHEIRO, M. P. M. A.; FILGUEIRAS, R.; ZIMBACK, C. R. L.REMOTE SENSING APPLIED TO THE MONITORING OF AQUATIC MACROPHYTES AT BARRA BONITA RESERVOIR, SP  2 ABSTRACT Aquatic macrophytes are photosynthetic organisms, large enough to be seen with naked eye, which grow submerged, floating or on the surface of the water. The anthropic action in the damming of water bodies has caused eutrophication of water resources, and among the imbalances that this action generates in the aquatic environment is the high proliferation of macrophytes. Due to this fact, this research was developed with the aim of estimating the area occupied by aquatic macrophytes in the reservoir of Barra Bonita Hydroelectric Power Plant (SP), in the years of 2013, 2014 and 2015. The study was carried out in the dry season (August), through the use of NDVI (Normalized Difference Vegetation Index) and supervised classification MAXVER (Maximum Likelihood). To obtain the maps and graphs, the following actions were taken: selection of LANDSAT-8 / OLI satellite images, radiometric calibration, atmospheric correction, reprojection, boundary definition, NDVI and supervised classification. The maps obtained through supervised classification, aided by NDVI maps, pointed to an increase of approximately 50% in the area occupied by aquatic macrophytes from 2013 to 2015. Keywords: supervised classification, eutrophication, NDVI index, landsat-8.

scholarly journals Multispectral aerial photography as a supplemental technique in agricultural research.

1988 ◽  
Vol 36 (1) ◽  
pp. 75-90
Author(s):  
J.G.P.W. Clevers
Keyword(s):  
Agricultural Research ◽  
Spring Barley ◽  
Atmospheric Correction ◽  
Sowing Date ◽  
Field Trials ◽  
Quantitative Information ◽  
Radiometric Calibration ◽  
Agricultural Field ◽  
N Nutrition ◽  
Spectral Bands

Narrow spectral bands in the visible and near IR were tested for use in remote sensing of agricultural field trials. Recordings of high spectral (25-100 nm bandwith), temporal (fortnightly) and spatial (+or-1msuperscript 2) resolution, were obtained using an airborne multispectral photographic (MSP) system. Calibrated reflectance factors of spring barley and spring wheat crops were obtained using atmospheric correction and radiometric calibration for reference targets in the field. LAI were estimated from spectral reflectance characteristics of cereals during the growing season for various treatments such as N nutrition and sowing date. Quantitative information was obtained in an objective and non-destructive mannner with greater precision by MSP than by conventional field sampling. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals The method for water body information extraction in complex environment using GF-1 WFV images

2020 ◽  
Vol 213 ◽  
pp. 03024
Author(s):  
Chao Chen ◽  
Liyan Wang ◽  
Yanli Chu ◽  
Xinyue He
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Water Body ◽  
Atmospheric Correction ◽  
Hunan Province ◽  
Human Society ◽  
Radiometric Calibration ◽  
Remote Sensing Imagery ◽  
Tasseled Cap ◽  
Tasseled Cap Transformation

Water body is one of the most active and important earth resources, and which has a profound impact on the natural system and human society. In order to acquire surface water body information quickly, accurately and efficiently, the method of water body information extraction using remote sensing imagery has attracted the attention of many searchers. On the basis of sorting out relevant research results of water body information extraction using remote sensing imagery, this paper proposed the method of water body information extraction based on the tasseled cap transformation for complex environments such as shadow and dense vegetation. First, radiometric calibration and atmospheric correction were carried out for remote sensing images. Then, the tasseled cap transformation was performed to obtain the greenness component and wetness component. Finally, the model of water body information extraction based on the tasseled cap transformation was constructed, and the water body information was extracted. In a region of Hunan province, China, the experiment using GF-1 WFV remote sensing image shows that the extracted water body information has a clear boundary and complete shape, and the Kappa coefficient, overall accuracy and user accuracy are 0.89, 92.72%, and 88.04%, respectively.

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