Relative Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series

Abstract

Multi-temporal images acquired at high spatial and temporal resolution are an important tool for detecting change and analyzing trends, especially in agricultural applications. However, to insure a reliable use of this kind of data, a rigorous radiometric normalization step is required. Normalization can be addressed by performing an atmospheric correction of each image in the time series. The main problem is the difficulty of obtaining an atmospheric characterization at a given acquisition date. In this paper, we investigate whether relative radiometric normalization can substitute for atmospheric correction. We develop an automatic method for relative radiometric normalization based on calculating linear regressions between unnormalized and reference images. Regressions are obtained using the reflectances of automatically selected invariant targets. We compare this method with an atmospheric correction method that uses the 6S model. The performances of both methods are compared using 18 images from of a SPOT 5 time series acquired over Reunion Island. Results obtained for a set of manually selected invariant targets show excellent agreement between the two methods in all spectral bands: values of the coefficient of determination (r2 exceed 0.960, and bias magnitude values are less than 2.65. There is also a strong correlation between normalized NDVI values of sugarcane fields (r2 = 0.959). Despite a relative error of 12.66% between values, very comparable NDVI patterns are observed.

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

“…To limit these effects, we normalized the reflectance values in each image using invariant targets (El Hajj et al 2008) and we based our analysis on the NDVI (Normalized Difference Vegetation Index) values. On the areas of interest, the clouds and cloud shadows were masked by photo interpretation.…”

An investigation into the effectiveness of relative and absolute atmospheric correction for retrieval the TSM concentration in inland waters

“…To improve the use of satellite images to estimate OACs concentrations, relative atmospheric correction can be considered an alternative approach. Several studies using relative atmospheric correction were conducted to investigate land cover change detection and agricultural applications (Ell Hajj et al 2008), but the normalization impacts on TSM estimates were not found in literature. Zhang et al (2010) used MODIS image and normalization process to estimate TSM concentrations in Taihu Lake, China, but did not evaluate the differences among absolute and relative atmospheric correction.…”

“…The IRMAD processing (relative correction) produced a RMSE of 6.44 %, it means, there was a decrease of 80 % in TSM retrieval errors when the radiometric normalization was conducted. The advantage of normalization processing is the temporal matching with time-series that reduces the atmospheric effects and the geometry/illumination variations, which implied to correct radiometric distortions (Ell Hajj et al 2008) that absolute correction did not correct. It is important to highlight that radiometric distortions are not the only responsible for such differences among IRMAD and FLAASH, the assumptions made from each method are also a source of errors.…”

Detecção de plantios de Cannabis sativa no semiárido pernambucano a partir da análise das anomalias de dinâmica temporal dos índices de vegetação derivados do satélite SPOT-5 HRG

“…Os dados SPOT-5 foram corrigidos para os efeitos da atmosfera com o intuito de possibilitar a generalização de parâmetros espectrais extraídos da imagem no espaço e no tempo (SONG et al, 2001;JENSEN, 2005;HOULES et al, 2006;SCHROEDER et al, 2006;EL HAJJ et al, 2008). Neste trabalho, foi aplicada a correção atmosférica absoluta das imagens SPOT-5 mediante o emprego do módulo FLAASH de correção atmosférica, disponível no aplicativo ENVI™ (RSI, 2009).…”

“…Índices de vegetação têm sido usados em estudos de dinâmica da vegetação e monitoramento de safras (EL HAJJ et al, 2008). O Índice de Vegetação por Diferença Normalizada (NDVI), definido como a diferença entre as reflectâncias nas faixas espectrais do infravermelho próximo e do vermelho dividida pela sua soma (TUCKER, 1979), é considerado particularmente útil para o monitoramento de atividade fotossintética e comparações de variações sazonais e interanuais (RATANA et al, 2005;PONZONI & SHIMABUKURO, 2007), tendo sido usado também para mapear lavouras irrigadas no sertão nordestino (CAMILO et al, 2010).…”