The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery

Hadjimitsis, Diofantos G.; Clayton, C. R. I.; Retalis, Adrianos

doi:10.1016/j.jag.2009.01.005

Cited by 70 publications

(36 citation statements)

References 33 publications

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“…The PIF method has been tested at several study sites [49][50][51][52][53], especially in water areas with haze contamination [45]. In the present study, the relatively haze-free HJ-1A satellite CCD2 image obtained on 28 August 2012, was used as a reference, and certain pixels were singled out as the pseudo-invariant feature set via the temporally invariant cluster (TIC) method [54].…”

Section: The Pseudo-invariant Features Methodsmentioning

confidence: 99%

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

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Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted on August 29 and October 26, 2012, showed that there was a strong linear relationship (R 2 = 0.9623) between field-surveyed OBS (optical backscatter) measurements (5-31NTU) and laboratory-analyzed TSS concentrations (9.89-35.58 mg/L). The COST image-based atmospheric correction procedure and the pseudo-invariant features (PIF) method were combined to remove the atmospheric effects from the total radiance measurements obtained with different CCDs onboard the HJ-1A/1B satellites. Then, a simple and practical retrieval model was established based on the relationship between the satellite-corrected reflectance band ratio of band 3 and band 2 (Rrs3/Rrs2) and in-situ TSS measurements. The R 2 of the regression relationship was 0.807, and the mean relative error (MRE) was 12.78%, as determined through in-situ data validation. Finally, the influences of tide cycles, wind factors (direction and speed) and other factors on the OPEN ACCESSRemote Sens. 2014, 6 9912 variation of the TSS spatial pattern observed from HJ-1A/1B satellite images from September through November of 2008 are discussed. The results show that HJ-1A/1B satellite CCD images can be used to estimate TSS concentrations under different tides in the study area over synoptic scales without using simultaneous in-situ atmospheric parameters and spectrum data. These findings provide strong informational support for numerical simulation studies on the combined influence of tide cycles and other associated hydrologic elements in Deep Bay.

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Section: The Pseudo-invariant Features Methodsmentioning

confidence: 99%

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

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“…A general approach to the invariant object technique was proposed by Hall et al [42], who intended to obtain a common radiometric response for corrected images that were acquired by different sensors on different dates through "radiometric control sets", which are nonvegetated extremes of the Kauth-Thomas brightness-greenness scattergram; note that these authors point out that "the members of the radiometric control sets may not be the same pixels from image to image". Hadjimitsis et al [21] used pseudoinvariant targets to atmospherically correct time series with high quality results. Pons et al [43] presented a hybrid technique between DOS and pseudoinvariant areas (PIA), fitting spectral TOD (τ 0λ ), and spectral atmospheric radiance (Latm λ ) unknowns to match reference values of radiometrically stable areas, which were extracted from existing 10-year surface reflectance TERRA-MODIS products (MOD09GA).…”

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confidence: 99%

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

et al. 2017

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Abstract:The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r 2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r 2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program's continuity, a goal of great interest for the environmental, scientific, and technical community.

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“…The other subset was used to validate the results of the correction algorithm. The atmospheric correction for the other images in the time-series was cross-checked by observation of pseudo-invariant targets within the study area [19]. A similar procedure was used for the Italian test site and the accuracy of the atmospheric correction was checked against a set of reference pixels containing identifiable pseudo-invariant ground targets (i.e., asphalt, sea water, concrete and sand) with known reflectance values from a spectral library [20].…”

Section: Satellite Datamentioning

confidence: 99%

“…A prerequisite for the quantitative analysis of time-series of satellite sensor data is to perform radiometric and atmospheric corrections [18], if possible using reliable instantaneous atmospheric measurements (such as aerosol optical thickness, water vapor content) and/or the spectral reflectance of known ground targets either derived from ground measurements (surface and/or atmospheric conditions) or from consolidated library data [19,20]. Several approaches have been proposed for performing atmospheric corrections.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

Vuolo

Neugebauer

Bolognesi³

et al. 2013

Remote Sensing

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Abstract:This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R 2 ) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.

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The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery

Cited by 70 publications

References 33 publications

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

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