Landsat 9 Geometric Characteristics Using Underfly Data

Abstract

The Landsat program has a long history of providing remotely sensed data to the user community. This history is being extended with the addition of the Landsat 9 satellite, which closely mimics the Landsat 8 satellite and its instruments. These satellites contain two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI is a push-broom sensor that collects visible and near-infrared (VNIR) and short-wave infrared (SWIR) wavelengths at 30 m ground sample distance, along with a panchromatic 15 m band. The TIRS sensor contains two long-wave thermal spectral channels centered at 10.9 and 12 µm. The data from these two instruments, on both satellites, are combined into a single Landsat product. The Landsat 5–9 satellites follow a 16 day repeat cycle designated as the Worldwide Reference System (WRS-2), which provides a global notional gridded mapping for identifying individual Landsat scenes. The Landsat 8 and 9 satellites are flown such that their orbital tracks are separated by 8 days in this 16 day cycle. During the commissioning period of Landsat 9, and during its ascent to its operational WRS-2 orbit, the Landsat 9 satellite’s orbital track went under and crossed over the orbital track of the Landsat 8 satellite. This produced a unique situation where nearly time-coincident imagery could be obtained from the instruments of the two spacecrafts. From a radiometric standpoint, this allowed for near-time cross-calibration between the instruments to be performed. From a geometry perspective, calibration is achieved through high-resolution reference imagery over specific ground locations, thus ensuring calibration of the instruments and for the instruments to be well cross-calibrated geometrically. Although these underfly data do not provide calibration of the instruments between the platforms from a geometric perspective, they allow for the verification of the calibration steps involving the instruments and spacecraft. This paper discusses the co-registration of this unique set of data while also discussing other geometric aspects of these data by looking at and comparing the differences in sensor viewing and sun angles associated with the collections from the two platforms for imagery obtained over common geographic locations. The image-to-image comparisons between Landsat 8 and 9 coincident pairs, where both datasets are precision terrain products, are registered to within 2.2 m with respect to their root-mean-squared radial error (RMSEr). The 2.2 m represents less than 0.1 of a 30 m multispectral pixel in misregistration between the L9 and L8 underfly products that will be available to the user community. This unique dataset will provide well-registered, near-coincident image acquisitions between the two platforms that can be a key to any calibration or application comparisons. The paper also presents that, for images for which one of the image pairs failed precision corrections and became a terrain-corrected only product type, a range of 8–14 m RMSEr could be expected in co-registration, while, in cases where both image pairs failed the precision correction step and both images became a terrain-corrected only product type, a 14 m RMSEr could be expected for co-registration.

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China

“…In this study, Landsat5, Landsat8, and Landsat9 satellite remote sensing images in nine time periods (1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2022) provided by the Google Earth Engine (GEE) cloud platform were used for land-use classification [ 32 ]. The Landsat satellites are among the optical remote sensing satellite series with the longest operating time in orbit for investigating land resources and drawing various thematic maps [ [33] , [34] , [35] ]. The details are presented in Table 1 .…”

Landsat 9 Imagery Based Mangrove Forest Mapping Using Maximum Likelihood Classifier and Support Vector Machine Algorithm

“…Hingga saat ini sudah ada delapan misi Landsat (Landsat 1, 2, 3, 4, 5, 7, 8 dan 9) yang menjadi catatan sejarah sebagai satelit pemantau bumi dengan perekaman data terpanjang selama periode interaksi manusia terhadap lingkungan Prasetyo, A.R., Valentino, N., dan Shabrina, H. dan perubahan iklim (Crawford et al, 2023;Goward et al, 2022). Keberhasilan peluncuran Landsat 9 pada tahun 2021 mempertegas komitmen USGS, NASA, dan Tim Sains Landsat dalam kelangsungan misi Landsat untuk penyediaan rekaman satelit resolusi menengah global (Choate et al, 2022;Crawford et al, 2023;Goward et al, 2022).…”

“…Tahapan pre-processing untuk Landsat 9 OLI-2/TIRS-2 pada penelitian ini meliputi top of atmospheric reflectance, dan surface reflectance yang merupakan bagian dari koreksi atmosferik. Dataset yang digunakan merupakan data landsat level 1(L1TP) yang masih membutuhkan koreksi secara atmosferik (Choate et al, 2022;Prasetyo et al, 2023). Semua tahapan pre-processing ini dilakukan dengan bantuan perangkat lunak QGIS plugin SCP.…”

Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2

“…Proses pengolahan data dilakukan dalam dua proses besar, yaitu pre-processing dan processing. Pre-processing meliputi koreksi atmosferik saja karena pada penelitian ini menggunakan citra satelit landsat tipe L1TP yang telah terkoreksi secara geometrik dan radiometrik (Choate et al, 2022). Proses koreksi atmosferik dilakukan dengan bantuan software QGIS 3.30 dengan menggunakan plugin SCP.…”