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Remote Sens. 2018, 10(11), 1678;

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

SGT Inc., Greenbelt, MD 20770, USA
Rochester Institute of Technology, Center for Imaging Science, Rochester, NY 14623, USA
Author to whom correspondence should be addressed.
Received: 31 August 2018 / Revised: 3 October 2018 / Accepted: 22 October 2018 / Published: 24 October 2018
(This article belongs to the Special Issue Remote Sensing of Forest Cover Change)
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Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products. View Full-Text
Keywords: Landsat; Sentinel; BRDF; defoliation; uncertainty; SBAF; atmospheric correction; forest canopy; simulation Landsat; Sentinel; BRDF; defoliation; uncertainty; SBAF; atmospheric correction; forest canopy; simulation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Rengarajan, R.; Schott, J.R. Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications. Remote Sens. 2018, 10, 1678.

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