Geology topics

Ferran Gascon

Publications and source records attributed to Ferran Gascon.

6 recordsLinked to original sources

USGS2025

The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset

Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 reflectance data showed that the between-sensor VI difference is below 4.5% for most of the indices. These results suggest that the harmonization is robust and the HLS V2.0 data are adequate for quantitative terrestrial applications.

Remote Sensing of Environment↗

USGS2025

Towards seamless global 30-meter terrestrial monitoring: Evaluating 2022 cloud free coverage of harmonized Landsat and Sentinel-2 (HLS) V2.0

Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of 1-3 days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of HLS V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes (>~75°N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or sub-weekly time series applications.

IEEE Geoscience and Remote Sensing Letters↗

USGS2024

The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch

Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.

Remote Sensing of Environment↗

USGS2023

The EnMAP imaging spectroscopy mission towards operations

EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2 nm and 2445.5 nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30 m x 30 m at a swath width of 30 km, allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users.

Remote Sensing of Environment↗

USGS2018

Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites

The Sentinel-2A and Landsat-8 satellites carry on-board moderate resolution multispectral imagers for the purpose of documenting the Earth’s changing surface. Though they are independently built and managed, users will certainly take advantage of the opportunity to have higher temporal coverage by combining the datasets. Thus it is important for the radiometric and geometric calibration of the MultiSpectral Instrument (MSI) and the Operational Land Imager (OLI) to be compatible. Cross-calibration of MSI to OLI has been accomplished using multiple techniques involving the use of pseudo-invariant calibration sites (PICS) using direct comparisons as well as through use of PICS models predicting top-of-atmosphere reflectance. A team from the University of Arizona is acquiring field data under both instruments for vicarious calibration of the sensors. This paper shows that the work done to date by the Landsat and Sentinel-2 calibration teams has resulted in stable radiometric calibration for each instrument and consistency to ~2.5% between the instruments for all the spectral bands that the instruments have in common.

European Journal of Remote Sensing↗

USGS2016

A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery

The Landsat-8 and Sentinel-2 sensors provide multi-spectral image data with similar spectral and spatial characteristics that together provide improved temporal coverage globally. Both systems are designed to register Level 1 products to a reference image framework, however, the Landsat-8 framework, based upon the Global Land Survey images, contains residual geolocation errors leading to an expected sensor-to-sensor misregistration of 38 m (2σ). These misalignments vary geographically but should be stable for a given area. The Landsat framework will be readjusted for consistency with the Sentinel-2 Global Reference Image, with completion expected in 2018. In the interim, users can measure Landsat-to-Sentinel tie points to quantify the misalignment in their area of interest and if appropriate to reproject the data to better alignment.

Remote Sensing of Environment↗