A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery

• Published in: Environmental monitoring and assessment 2012-06, Vol.184 (6), p.3813-3829
• Main Authors: Tan, Kok Chooi, Lim, Hwee San, MatJafri, Mohd Zubir, Abdullah, Khiruddin
• Format: Article
• Language: English
• Subjects: Accuracy; Atmosphere; Atmosphere - chemistry; Atmospheric Protection/Air Quality Control/Air Pollution; Calibration; Coastal plains; Computer programs; Correlation coefficient; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; Land surface temperature; Landsat; Malaysia; Methods; Monitoring/Environmental Analysis; Radiometry; Regression analysis; Remote sensing; Remote Sensing Technology - methods; Sensors; Software; Spacecraft; Studies; Temperature; Vegetation
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-011-2226-0

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.