The normalised Sentinel-1 Global Backscatter Model, mapping Earth’s land surface with C-band microwaves

Abstract

We present a new perspective on Earth’s land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016–17 by the mean C-band radar cross section in VV- and VH-polarisation at a 10 m sampling. We processed 0.5 million Sentinel-1 scenes totalling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Regions covered by only one or two Sentinel-1 orbits remain challenging, owing to insufficient angular variation and not yet perfect sub-swath thermal noise correction. Supporting the design and verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states. Here, we demonstrate, as an example of its potential use, the mapping of permanent water bodies and evaluate against the Global Surface Water benchmark.

Global seasonal Sentinel-1 interferometric coherence and backscatter data set

“…Each possible image combination with repeat intervals up to 48 days was considered as input into estimates of seasonal median coherence at 6-, 12-, 18-, 24-, 36- and 48-days repeat intervals of Sentinel-1’s co-polarized observations (VV and HH polarizations). For additional value in interpreting global seasonal SAR measurement patterns, seasonal means of backscatter amplitudes for all co- and cross-polarized observations (VV, VH, HH, HV) were generated, thus complementing recently published maps of mean annual Sentinel-1 backscatter at 10 m spatial sampling with information on seasonal variations in backscatter 10 . As ancillary data, local incidence angle and layover and shadow maps were produced for all 175 Sentinel-1 imaging swaths.…”

Quantification of Foraging Areas for the Northern Bald Ibis (Geronticus eremita) in the Northern Alpine Foothills: A Random Forest Model Fitted with Optical and Actively Sensed Earth Observation Data

“…Landsat-8 data provides several useful indices which we incorporated into our model, describing the characteristics of vegetation intensity (enhanced vegetation index, EVI), ground tasseled cap brightness (TC brightness), moisture (TC wetness), and water content (normalized difference water index, NDWI). We added the synthetic-aperture radar intensity of Sentinel-1 as an index calculated from the intensity of vertically sent and vertically received radar data (VV polarization), which reflects the ground structure and surface wetness [35][36][37]. Additionally, we applied the fractional grass cover processed by the Copernicus Land Monitoring Service from 2019 [38].…”

High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study

“…SM retrievals at 1 km spatial resolution were obtained by applying a first-order radiative transfer model (RT1) (Quast et al, 2019) to a 1 km Sentinel-1 backscatter (σ 0) dataset sampled at 500 m pixel spacing (Bauer-Marschallinger et al, 2021). RT1 is based on a parametric (first-order) solution to the radiative transfer equation (Quast and Wagner, 2016) in conjunction with a time-series-based nonlinear least-squares regression to optimize the difference between (incidenceangle-dependent) measured and modeled σ 0.…”

“…The condition of high spatial and medium temporal resolution is, for the first time, met by the two S1 satellites currently in orbit. SM measurements with 1 km spatial resolution can be obtained from this mission (Bauer-Marschallinger et al, 2018).…”