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Abstract: We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by... View more
We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by a modified Fmask code. Surface reflectance is inferred from Tanré's formulation of the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) is estimated over dark objects (DOs) that are identified in a combined database and image-based approach, where information on their temporal persistency is utilized. AOD is inferred with consideration of the actual target reflectance and background contamination effect. In case of absent DOs in bright scenes, a fallback approach with a modeled AOD climatology is used instead. Topographic normalization is performed by a modified C-correction. The data are projected into a single coordinate system and are organized in a gridded data structure for simplified pixel-based access. We based the assessment of the produced data set on an exhaustive analysis of overlapping pixels: 98.8% of the redundant overlaps are in the range of the expected ±2.5% overall radiometric algorithm accuracy. AOD is in very good agreement with Aerosol Robotic Network sunphotometer data (R
2
: 0.72 to 0.79, low intercepts, and slopes near unity). The uncertainty in using the water vapor fallback climatology is approximately ±2.8% for the TM SWIR1 band in the wet season. The topographic correction was considered successful by an investigation of the nonrelationship between the illumination angle and the corrected radiance.
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Landsat data are one of the most valuable resources for Earth observation
[1] due to their long-term data continuity
[2] and their optimal resolution to monitor processes at the landscape level
[3]. With the advent of open data policy
[4], accompanied by technical progress in terms of processing, storing, and
transmission infrastructure and the increasing availability of automated processing routines (e.g.,
[5]), the usage of Landsat data changed fundamentally
[6]. Historically, analyses were either based on a few images and large areas
[7] or on small areas and shorter time steps
[8]. Now, it has become feasible to make use of the full depth of the Landsat
archive, as well as covering very large areas at the same time. Nevertheless, Landsat data are still provided in the
traditional Worldwide Reference System 2 (WRS-2) framework
[9], where the image footprints vary from acquisition to acquisition. This
involves several obstacles for the end user, e.g., the integrated usage of data from different paths requires the
reprojection to a unique coordinate system, which simplifies the adequate usage of the data-rich orbital overlap area.
Even in the case of using only one WRS-2 footprint, all images have to be cropped to the same extent. This is even
mandatory if sophisticated follow-up applications such as time series analyses (TSA) or the derivation of pixel-based
composites (PBC)
[10] are to be addressed. The usage of these dense time series applications
requires the data of a given location to be easily and quickly accessible, regardless of their initial path and row
designation, projection, acquisition time, or sensor. Therefore, preprocessed imagery in a gridded data structure
represents a more suitable structure for TSA or PBC applications
[9], as demonstrated by the Web-enabled Landsat Data project
[11].
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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© Copyright 2023 IEEE - All rights reserved.

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