Evaluation of the uncertainty in the spectral band adjustment factor (SBAF) for cross-calibration using Monte Carlo simulation

Pinto, Cibele Teixeira; Ponzoni, Flávio Jorge; Castro, R. M.; Leigh, Larry; Kaewmanee, Morakot; Aaron, David; Helder, Dennis L.

doi:10.1080/2150704x.2016.1190474

Cited by 18 publications

(13 citation statements)

References 9 publications

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“…Hyperion profile well calibrated has been proven to be valuable for comparing different instruments with multispectral channels through a simulation strategy [24,25]. The Hyperion hyperspectral spectra have been used in applications for cross-sensor calibration and transformation [23][24][25][26][27][31][32][33].…”

Section: Hyperion Spectra Collectionmentioning

confidence: 99%

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

et al. 2019

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Landsat 4-5, built at the same time and with the same design, carrying the Multispectral Scanner System (MSS) and the Thematic Mapper (TM) simultaneously, jointly provided observation service for about 30 years (1982–2013). Considering the importance of data continuity for time series analyses, investigations on the continuity of the Landsat 4-5 MSS and TM are required. In this paper, characterization differences between the Landsat 4-5 MSS and TM were initially discussed using the synthesized reflectance records generated from a collection of Hyperion hyperspectral profiles which were well calibrated and widely distributed. The difference in near-infrared region mostly contributed to the difference in normalized difference vegetation index (NDVI) between MSS and TM, while the between-sensor difference in red spectrum was relatively minor. Models for transforming MSS NDVI to TM NDVI were proposed, and validated subsequently through cross-validation tests. Furthermore, effectiveness of the transformation models was investigated using eight synchronous observation pairs of the Landsat 5 MSS and TM. On average, the univariate models through ordinary least squares regression (OLS) regression resulted in a decrease about 10% of the median relative difference (MdRD). Meanwhile, the bivariate models improved the NDVI comparability in most cases, especially when the transformation models through ridge regression were implemented. The univariate model through OLS regression could be the only solution for cases when problems of data quality are encountered (e.g., problem in the MSS near-infrared channel (800–1000 nm)). In conclusion, the findings on NDVI transformation models from MSS to TM are valuable for reference, because of the collection of diverse Hyperion hyperspectral profiles used.

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Section: Hyperion Spectra Collectionmentioning

confidence: 99%

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

et al. 2019

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“…), the relative membership degrees of attraction and rejection are

and

, respectively, satisfying

and

= 1. The integrated relative membership degree

to all grades

can be calculated by variable fuzzy recognition model, as Equation (7) [ 38 ]:

…”

Section: Methodsmentioning

confidence: 99%

“…Making sure of all targets in targets set

on importance using the binary comparison method [ 38 ], and getting the ordering results of

targets on the importance degree, which subject to sorting consistency, we suppose

.…”

Section: Index Weights Calculation Methodsmentioning

confidence: 99%

“…The qualitative evaluation method, including the Delphi method [ 31 ] and expert meeting method [ 32 ], and the quantitative evaluation method including the system engineering method [ 33 ], statistical analysis method [ 34 , 35 ], and the operational research method [ 36 ] etc., are the two main comprehensive evaluation methods [ 37 ]. As the appearance of mutual integration from different knowledge areas, new methods including system modeling and simulation method [ 38 ], information theory method [ 39 ], grey theory method [ 40 , 41 ], intelligent method [ 42 , 43 , 44 ], rough set method [ 45 ], matter element analysis method [ 46 ], and a novel MADM approach [ 47 ] for comprehensive evaluation problems emerged. Different evaluation methods suit for different research objects.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Index Evaluation for Flood Disaster from Sustainable Perspective: A Case Study of Xinjiang in China

Dou

Xue

Zhao

et al. 2018

IJERPH

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The floods have undermined the sustainable construction of cities because of their sudden and destruction. To reduce the losses caused by floods, it is necessary to make a reasonable evaluation for historical floods and provide scientific guidance for future precaution. Previous research mainly used subjective/objective weights or barely made static analysis without considering the uncertainty and ambiguity of floods. Therefore, this study proposed a variable fuzzy recognition model, based on combined weights, to evaluate floods, including the determination of index weights and the choice of evaluation model. To make the index weights reflect both subjective experience and objective data, the combined weights were proposed and calculated based on the principle of minimum identification information. Then, the relative membership degree matrix and evaluation results can be worked out by the variable fuzzy recognition model. Conclusions indicated that the combined weights were more convincing than simply subjective or objective weights. Moreover, the variable fuzzy recognition model, by changing model parameters, got stable evaluation results of the sample data. Therefore, the model can improve the credibility of evaluation and the conclusions can provide reasonable suggestions for management departments.

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“…To calculate the total uncertainty, first, uncertainties (i) and (ii) were propagated through Equation (4) using a Monte Carlo simulation method [14], assuming spectral correlation matrix (correlation between the wavelengths) values were decreasing in the subdiagonal, adjacent to the main diagonal, ranging from 0.9 to 0.1, with an interval of 0.1; the remaining correlation value used to fill up the matrix was 0.05 (the correlation in the main diagonal equal to 1). Second, for the uncertainty associated with temporal linear interpolation, is was assumed that the RadCalNet predicted values before and after the sensor over pass time are the variation limitations of the temporal interpolated TOA reflectance, then the reflectance error between before/after values and the interpolated values are calculated, the larger absolute error was indicated as the uncertainty associated with temporal interpolation.…”

Section: Toa Reflectance Comparison Uncertaity Analysismentioning

confidence: 99%

Evaluation of RadCalNet Output Data Using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors

et al. 2019

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In 2013, the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Infrared and Visible Optical Sensors Subgroup (IVOS) established the Radiometric Calibration Network (RadCalNet), consisting of four international test sites providing automated in situ measurements and estimates of propagated top-of-atmosphere (TOA) reflectance. This work evaluates the ‘reliability’ of RadCalNet TOA reflectance data at three of these sites—RVUS, LCFR, and GONA—using Landsat 7 ETM+, Landsat 8 operational land imager (OLI), and Sentinel 2A/2B (S2A/S2B) MSI TOA reflectance data. This work identified a viewing angle effect in the MSI data at the RVUS and LCFR sites; when corrected, the overall standard deviation in relative reflectance differences decreased by approximately 2% and 0.5% at the RVUS and LCFR sites, respectively. Overall, the relative mean differences between the RadCalNet surface data and sensor data for the RVUS and GONA sites are within 5% for ETM+, OLI, and S2A MSI, with an approximately 2% higher difference in the S2B MSI data at the RVUS site. The LCFR site is different from the other two sites, with relative mean differences ranging from approximately -10% to 1%, even after performing the viewing angle effect correction on the MSI data. The data from RadCalNet are easy to acquire and use. More effort is needed to better understand the behavior at LCFR. One significant improvement on the accuracy of the RadCalNet data might be the development of a site-specific BRDF characterization and correction.

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Evaluation of the uncertainty in the spectral band adjustment factor (SBAF) for cross-calibration using Monte Carlo simulation

Cited by 18 publications

References 9 publications

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

Multi-Index Evaluation for Flood Disaster from Sustainable Perspective: A Case Study of Xinjiang in China

Evaluation of RadCalNet Output Data Using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors

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