Abstract
There is an ever-increasing need to use an accurate and consistent geometric ground reference in the processing of remotely sensed data products, as this reduces the burden on the end-users to account for the differences between the data products from different missions. In this regard, the U.S. Geological Survey (USGS) initiated an effort to harmonize the Landsat ground reference with the Sentinel-2 Global Reference Image (GRI) to improve the co-registration between the data products of the two global medium-resolution missions. In this paper, we discuss the process, results, and the improvements expected from this harmonization of two ground references using space-triangulation-based bundle adjustment techniques. The ground coordinates of the Landsat reference library, consisting of five million Ground Control Points (GCPs) were adjusted in a series of four simultaneous bundle block adjustments using thousands of Landsat-8 (L8) scenes anchored with more than 300,000 control points extracted from the GRI dataset. The net adjustments to each of the four blocks, namely, Australia, Americas, Eurasia, and Islands, varied anywhere from 1 to 13 m, depending on the accuracy of the GCPs in these blocks. The use of the GRI dataset in our bundle adjustment not only improved the absolute accuracy of the Landsat ground reference, but will also improve the co-registration between Sentinel-2 and Landsat terrain corrected products, as the European Space Agency plans to process the Sentinel-2 products using the GRI dataset. Independent validation of the Landsat products processed using harmonized GCPs with the GRI dataset indicated a global misregistration error of less than 8 m Circular Error Probable at 90 % (CE90), an improvement from the 25 m prior to harmonization. The improvements to the Landsat products using the harmonized GCPs are expected to be available to the public as part of Landsat Collection-2 processing by the end of 2020.
Highlights
The increasing availability of moderate-resolution remote sensing data from a variety of sources at little to no cost to the user has made the assembly of dense time-series datasets ever more practical to support a wide range of applications, including global climate change studies, water management, monitoring land cover change, agriculture and forest management, homeland security, and disaster management [1,2,3,4,5]
The remainder of this paper describes the data used in the triangulation process, the geometry of the four triangulation blocks used to perform the global adjustment, the outputs of the triangulation processes, and the results of the validation techniques used to assess the performance of the triangulation and to predict the Landsat–Sentinel registration accuracy to be expected in the future
Using the Global Reference Image (GRI) dataset as a source for ground reference in the bundle adjustment procedure, we improved the absolute accuracy of the Ground Control Points (GCPs) derived from the GLS2000 and Operational Land Imager (OLI) images
Summary
The increasing availability of moderate-resolution remote sensing data from a variety of sources at little to no cost to the user has made the assembly of dense time-series datasets ever more practical to support a wide range of applications, including global climate change studies, water management, monitoring land cover change, agriculture and forest management, homeland security, and disaster management [1,2,3,4,5]. Implicit in the ability to create and utilize these multi-sensor time series is the ability to register these data to a common geometric reference. The S2 and Landsat missions create products that nominally use the same geodetic reference system and map projection, differences in the geometric reference data used to create these products lead to residual misregistration between S2 and Landsat data products [6]. This residual misregistration places the burden of checking and, if necessary, refining the S2-Landsat registration on the user. This paper describes an effort to remedy this situation by improving the consistency of the S2 and Landsat geometric reference data
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