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GIS-based workflows for SAR/InSAR Science Data Systems

Date: Sep 11, 2023
Slides

Copernicus Programme's Sentinel-1 SAR constellation images most of the land masses, with a revisit time of 6-24 days, in the Interferometric Wide (IW) swath Terrain Observation by Progressive Scanning (TOPS) mode. The S1 constellation has generated more than 10PB of Level-1 products since September 2014, and the size of this archive is expected to grow 3-4 fold over the next decade as more instruments are added to the constellation. Despite excellent global coverage and temporal sampling, application scientists and remote sensing data users struggle to work with Level 1 SAR data as the data are distributed in non-Geographic Information System (GIS) compatible map projections and the need for custom processing tools to work with these products. With more SAR missions targeting global coverage like NISAR and ROSE-L expected to be launched in the near future, the challenge of making SAR products usable within GIS frameworks to allow a larger community to benefit from these missions will only get more acute.

In this work, we present workflows developed at Descartes Labs that allow users to perform established SAR and InSAR analysis within GIS frameworks. The presented solution not only improves accessibility to SAR and InSAR data, it also allows end users to work with these datasets within the same frameworks as other remote sensing datasets like optical imagery, weather forecasts etc.

Coregistered, geocoded SLC stack¤

Currently, Level 1 SAR products from various missions are distributed each in their own non-GIS compatible slant range projection systems [1]. Aligning this imagery on a common grid requires specialized processing tools and requires a large amount of computation resources. Distributing coregistered stack of SAR imagery as a Level 2 product will significantly accelerate development of end user analytics workflows and will encourage broader adoption of SAR data in the remote sensing community. We also propose that the coregistered stack is already generated in well known projection systems [1] to allow the large community of users familiar with working on optical datasets to easily adopt standard GIS tools to work with SAR data. We believe a large fraction of end users can easily leverage Level 2 products generated using a DEM chosen for entire missions as is typically done for optical missions like Sentinel-2. Advanced users and experts who require custom processing can always leverage the lower level Level 1 SLC products, as is also the norm in the optical remote sensing community.

Higher level derivative product workflows¤

Using the Level 2 geocoded SLC stacks as a base product, a number of widely used products can be easily derived within standard GIS frameworks. At Descartes Labs, we have implemented these workflows [1, 2, 3] and we describe Sentinel-1 specific implementation details.

  1. Geocoded SLCs for infrastructure monitoring

    For full resolution infrastructure monitoring, we geocode Sentinel-1 bursts to a standardized 10 meter Northing x 2.5 meter Easting grid [1]. The phase of the SLCs are flattened using the same DEM used for geocoding, to simplify further interferometric processing. The real and imaginary values of the complex SLC product are stored as separate bands. This data is accessed in the same manner as bands in optical imagery and time-series InSAR analytics tools have been developed on top of standard GIS frameworks [3].

  2. Geocoded Terrain Corrected (GTC) backscatter products

    GTC products can be derived from geocoded SLCs using an absolute value band math operation and spatial filtering. Within our data system, we generate GTC products on a standardized 10 meter UTM grid [1] globally from Sentinel-1 IW mode data.

  3. On-the-fly Radiometric Terrain Corrected (RTC) backscatter products

    We have also developed a formulation to transform GTC products to RTC products on the fly exploiting imaging baseline information similar to InSAR time-series analysis [2]. In the case of Sentinel-1, we have already shown that this transformation can be reduced to a simple band math operation [2] due to its narrow orbital tube. The same framework can also be used to transform GTC products to other calibration levels like (sigma0E or gamma0E) or other types of terrain corrected products [4] on the fly.

  4. Pairwise wrapped interferogram products

    Pairwise interferograms can be generated from geocoded SLCs by simple cross-multiplication. Interferometric coherence and wrapped phase can be generated from these interferograms using a string of band math and spatial filtering operations on-the-fly. We generate wrapped interferogram products on a standardized 20 meter UTM grid [2] globally from Sentinel-1 IW mode data for all compatible pairs with a temporal baseline of 24 days or less.

We will present some examples of how these derived products can be combined with optical and thermal imagery, on-the-fly to support multi-sensor, multi-modal and multi-temporal analytics.

Mission considerations¤

We have developed our GIS-based SAR and InSAR processing framework using Sentinel-1 mission as the basis. We believe that the same approach can also be adopted for other medium resolution missions like ALOS-2, NISAR, ROSE-L etc. Finally, we will discuss different factors that one must consider before adopting the proposed framework for large scale processing efforts for these missions, including:

  1. Atmospheric propagation delay and its impact on absolute geolocation, particularly for L-band sensors.
  2. Accuracy of the Digital Elevation Models (DEM) as we approach ground resolution of less than 2 meters. Adoption of our proposed workflows to higher resolutions over large areas would require global scale DEMs at higher than 10m resolution with a vertical accuracy of less than a couple of meters to be developed first.

References¤

  • Agram P.S., Warren M.S., Calef M.T., Arko S.A. An Efficient Global Scale Sentinel-1 Radar Backscatter and Interferometric Processing System. Remote Sensing. 2022; 14(15):3524.
  • Agram P.S.; Warren M.S.; Arko S.A.; Calef M.T. Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery. Preprints 2023, 2023020233 (doi: 10.20944/preprints202302.0233.v1).
  • Olsen K.M., Calef M.T., Agram P.S. Contextual uncertainty assessments for InSAR-based deformation retrieval using an ensemble approach, Remote Sensing of Environment. 2023.
  • Navacchi C., Cao S., Bauer-Marschallinger B., Snoeij P., Small D., Wagner W. Utilising Sentinel-1’s orbital stability for efficient pre-processing of sigma nought backscatter, ISPRS Journal of Photogrammetry and Remote Sensing. 2022.