Landsat 9 Collection 2 Tier 1 calibrated top-of-atmosphere (TOA) reflectance. Calibration coefficients are extracted from the image metadata. See Chander et al. (2009) for details on the TOA computation. Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series processing analysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data that have well-characterized radiometry and are inter-calibrated across the different Landsat sensors. The georegistration of Tier 1 scenes will be consistent and within prescribed tolerances [<=12 m root mean square error (RMSE)]. All Tier 1 Landsat data can be considered consistent and inter-calibrated (regardless of sensor) across the full collection. See more information in the USGS docs. The T1_RT collection contains both Tier 1 and Real-Time (RT) assets. Newly-acquired Landsat 7 ETM+ and Landsat 8 OLI/TIRS data are processed upon downlink but use predicted ephemeris, initial bumper mode parameters, or initial TIRS line of sight model parameters. The data is placed in the Real-Time tier and made available for immediate download. Once the data have been reprocessed with definitive ephemeris, updated bumper mode parameters and refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier. The transition delay from Real-Time to Tier 1 or Tier 2 is between 14 and 26 days.

Landsat 9 Collection 2 Tier 1 DN values, representing scaled, calibrated at-sensor radiance. Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series processing analysis. Tier 1 includes Level-1 Precision Terrain (L1TP) processed data that have well-characterized radiometry and are inter-calibrated across the different Landsat sensors. The georegistration of Tier 1 scenes will be consistent and within prescribed tolerances [<=12 m root mean square error (RMSE)]. All Tier 1 Landsat data can be considered consistent and inter-calibrated (regardless of sensor) across the full collection. See more information in the USGS docs.

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free mean growing season composites, and mask water features, then export the resulting band data. Growing season is defined as May 1st through August 31st. NDVI indices were then calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Tập dữ liệu này chứa hệ số phản xạ bề mặt đã được điều chỉnh theo khí quyển và nhiệt độ bề mặt đất được lấy từ dữ liệu do các cảm biến OLI/TIRS của Landsat 9 tạo ra. Những hình ảnh này chứa 5 dải tần nhìn thấy được và cận hồng ngoại (VNIR) và 2 dải tần hồng ngoại sóng ngắn (SWIR) được xử lý thành độ phản xạ bề mặt được chỉnh sửa hình học, và một dải tần hồng ngoại nhiệt (TIR) được xử lý thành …

Esse conjunto de dados contém refletância da superfície corrigida atmosfericamente e temperatura da superfície derivadas dos dados produzidos pelos sensores OLI/TIRS do Landsat 9. Essas imagens contêm cinco bandas visíveis e de infravermelho próximo (VNIR) e duas bandas de infravermelho de ondas curtas (SWIR) processadas para refletância de superfície ortorretificada, além de uma banda de infravermelho térmico (TIR) processada para…

Landsat and Sentinel-2 acquisitions are among the most frequently used medium-resolution (i.e., 10-30 m) optical data. The data are extensively used in terrestrial vegetation applications, including but not limited to, land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. While the Landsat archives alone provide over 40 years, and counting, of continuous and consistent observations, since mid-2015 Sentinel-2 has enabled a revisit frequency of up to 2-days. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of usable (i.e., cloud-, snow-, and shade-free) observations at the pixel level needs to be explored for each study to ensure correct parametrization of used algorithms, thus robustness of subsequent analyses. However, a priori data exploration is time and resource‑consuming, thus is rarely performed. As a result, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present a global dataset comprising precomputed daily availability of usable Landsat and Sentinel-2 data sampled at a pixel-level in a regular 0.18°-point grid. We based the dataset on the complete 1982-2024 Landsat surface reflectance data (Collection 2) and 2015-2024 Seninel-2 top-of-the-atmosphere reflectance scenes (pre‑Collection-1 and Collection-1). Derivation of cloud-, snow-, and shade-free observations followed the methodology developed in our recent study on data availability over Europe (Lewińska et al., 2023; https://doi.org/10.20944/preprints202308.2174.v2). Furthermore, we expanded the dataset with growing season information derived based on the 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6). As such, our dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much-needed a priori information aiding the identification of appropriate methods and challenges for terrestrial vegetation analyses at the local to global scales. The dataset can be viewed using the dedicated GEE App (link in Related Works). As of February 2025 the dataset has been extended with the 2024 data. Methods We based our analyses on freely and openly accessible Landsat and Sentinel-2 data archives available in Google Earth Engine (Gorelick et al., 2017). We used all Landsat surface reflectance Level 2, Tier 1, Collection 2 scenes acquired with the Thematic Mapper (TM) (Earth Resources Observation And Science (EROS) Center, 1982), Enhanced Thematic Mapper (ETM+) (Earth Resources Observation And Science (EROS) Center, 1999), and Operational Land Imager (OLI) (Earth Resources Observation And Science (EROS) Center, 2013) scanners between 22nd August 1982 and 31st December 2024, and Sentinel-2 TOA reflectance Level-1C scenes (pre‑Collection-1 (European Space Agency, 2015, 2021) and Collection-1 (European Space Agency, 2022)) acquired with the MultiSpectral Instrument (MSI) between 23rd June 2015 and 31st December 2024. We implemented a conservative pixel-quality screening to identify cloud-, snow-, and shade-free land pixels. For the Landsat time series, we relied on the inherent pixel quality bands (Foga et al., 2017; Zhu & Woodcock, 2012) excluding all pixels flagged as cloud, snow, or shadow as well as pixels with the fill-in value of 20,000 (scale factor 0.0001; (Zhang et al., 2022)). Furthermore, due to the Landsat 7 orbit drift (Qiu et al., 2021) we excluded all ETM+ scenes acquired after 31st December 2020. Because Sentinel-2 Level-2A quality masks lack the desired scope and accuracy (Baetens et al., 2019; Coluzzi et al., 2018), we resorted to Level-1C scenes accompanied by the supporting Cloud Probability product. Furthermore, we employed a selection of conditions, including a threshold on Band 10 (SWIR-Cirrus), which is not available at Level‑2A. Overall, our Sentinel-2-specific cloud, shadow, and snow screening comprised:

exclusion of all pixels flagged as clouds and cirrus in the inherent ‘QA60’ cloud mask band; exclusion of all pixels with cloud probability >50% as defined in the corresponding Cloud Probability product available for each scene; exclusion of cirrus clouds (B10 reflectance >0.01); exclusion of clouds based on Cloud Displacement Analysis (CDI<‑0.5) (Frantz et al., 2018); exclusion of dark pixels (B8 reflectance <0.16) within cloud shadows modelled for each scene with scene‑specific sun parameters for the clouds identified in the previous steps. Here we assumed a cloud height of 2,000 m. exclusion of pixels within a 40-m buffer (two pixels at 20-m resolution) around each identified cloud and cloud shadow object. exclusion of snow pixels identified with a snow mask branch of the Sen2Cor processor (Main-Knorn et al., 2017).

Through applying the data screening, we generated a collection of daily availability records for Landsat and Sentinel-2 data archives. We next subsampled the resulting binary time series with a regular 0.18° x 0.18°‑point grid defined in the EPSG:4326 projection, obtaining 475,150 points located over land between ‑179.8867°W and 179.5733°E and 83.50834°N and ‑59.05167°S. Owing to the substantial amount of data comprised in the Landsat and Sentinel-2 archives and the computationally demanding process of cloud-, snow-, and shade-screening, we performed the subsampling in batches corresponding to a 4° x 4° regular grid and consolidated the final data in post-processing. We derived the pixel-specific growing season information from the 2001-2019 time series of the yearly 500‑m MODIS land cover dynamics product (MCD12Q2; Collection 6) available in Google Earth Engine. We only used information on the start and the end of a growing season, excluding all pixels with quality below ‘best’. When a pixel went through more than one growing cycle per year, we approximated a growing season as the period between the beginning of the first growing cycle and the end of the last growing cycle. To fill in data gaps arising from low-quality data and insufficiently pronounced seasonality (Friedl et al., 2019), we used a 5x5 mean moving window filter to ensure better spatial continuity of our growing season datasets. Following (Lewińska et al., 2023), we defined the start of the season as the pixel-specific 25th percentile of the 2001-2019 distribution for the start of the season dates, and the end of the season as the pixel-specific 75th percentile of the 2001-2019 distribution for end of the season dates. Finally, we subsampled the start and end of the season datasets with the same regular 0.18° x 0.18°-point grid defined in the EPSG:4326 projection. References:

Baetens, L., Desjardins, C., & Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sensing, 11(4), 433. https://doi.org/10.3390/rs11040433 Coluzzi, R., Imbrenda, V., Lanfredi, M., & Simoniello, T. (2018). A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses. Remote Sensing of Environment, 217, 426–443. https://doi.org/10.1016/j.rse.2018.08.009 Earth Resources Observation And Science (EROS) Center. (1982). Collection-2 Landsat 4-5 Thematic Mapper (TM) Level-1 Data Products [Other]. U.S. Geological Survey. https://doi.org/10.5066/P918ROHC Earth Resources Observation And Science (EROS) Center. (1999). Collection-2 Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1 Data Products [dataset]. U.S. Geological Survey. https://doi.org/10.5066/P9TU80IG Earth Resources Observation And Science (EROS) Center. (2013). Collection-2 Landsat 8-9 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) Level-1 Data Products [Other]. U.S. Geological Survey. https://doi.org/10.5066/P975CC9B European Space Agency. (2015). Sentinel-2 MSI Level-1C TOA Reflectance [dataset]. European Space Agency. https://doi.org/10.5270/S2_-d8we2fl European Space Agency. (2021). Sentinel-2 MSI Level-1C TOA Reflectance, Collection 0 [dataset]. European Space Agency. https://doi.org/10.5270/S2_-d8we2fl European Space Agency. (2022). Sentinel-2 MSI Level-1C TOA Reflectance [dataset]. European Space Agency. https://doi.org/10.5270/S2_-742ikth Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Joseph Hughes, M., & Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. https://doi.org/10.1016/j.rse.2017.03.026 Frantz, D., Haß, E., Uhl, A., Stoffels, J., & Hill, J. (2018). Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment, 215, 471–481. https://doi.org/10.1016/j.rse.2018.04.046 Friedl, M., Josh, G., & Sulla-Menashe, D. (2019). MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 [dataset]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD12Q2.006 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031Lewińska K.E., Ernst S., Frantz D., Leser U., Hostert P., Global Overview of Usable Landsat and Sentinel-2 Data for 1982–2023. Data in Brief 57, (2024) https://doi.org/10.1016/j.dib.2024.111054 Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., & Gascon, F. (2017). Sen2Cor for Sentinel-2. In L. Bruzzone, F. Bovolo,

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.

The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.

The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.

Known Issues

• Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the User Guide); the corresponding spectral data should be discarded from analysis.

• Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.

• Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.

• Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.

• Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.

• Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.

• Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.

• Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads.

• Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers.

• Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare.

• Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare.

• Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions.

• The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed.

• Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error.

• False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.

• L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.

• The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of

Harmonized Landsat Sentinel is a NASA initiative to produce a Virtual Constellation of surface reflectance (SR) data from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard the Landsat 8-9 and Sentinel-2 remote sensing satellites, respectively. The combined measurement enables global observations of the land every 2–3 days. Input products are Landsat 8-9 Collection 2 Level 1 top-of-atmosphere reflectance and Sentinel-2 L1C top-of-atmosphere reflectance, which NASA radiometrically harmonizes to the maximum extent, resamples to common 30-meter resolution, and grids using the Sentinel-2 Military Grid Reference System (MGRS) UTM grid. Because of this, the products are different from Landsat 8-9 Collection 2 Level 2 surface reflectance and Sentinel-2 L2A surface reflectance.

Remote sensing and GPS tracking reveal temporal shifts in habitat use in nonbreeding Black-tailed Godwits

https://doi.org/10.5061/dryad.4tmpg4fm3

Description of the data and file structure

Authors:

Taylor B. Craft, Theunis Piersma, Jos C.E.W. Hooijmeijer, Bing-Run Zhu, Malaika D’Souza, Eoghan O’Reilly, Rienk W. Fokkema, Marie Stessens, Heinrich Belting, Christopher Marlow, Jürgen Ludwig, Johannes Melter, José A. Alves, Arturo Esteban-Pineda, Jorge S. Gutiérrez, José A. Masero, Afonso D. Rocha, Camilla Dreef, Ruth A. Howison

Data Description:

This dataset contains godwit GPS location data and habitat classification related to the nonbreeding behaviour of Black-tailed Godwits (Limosa limosa). The tracking data spans between June 2022 and March 2023, focusing on the movements and habitat use of godwits within the Senegal Delta. The study aims to investigate habitat use patterns across the nonbreeding period.

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ค่า DN ของ Landsat 9 Collection 2 Tier 1 ซึ่งแสดงถึงการแผ่รังสีที่เซ็นเซอร์ที่ปรับเทียบและปรับขนาดแล้ว ฉาก Landsat ที่มีคุณภาพข้อมูลสูงสุด จะอยู่ในระดับ 1 และถือว่าเหมาะสำหรับการวิเคราะห์ การประมวลผลอนุกรมเวลา ระดับที่ 1 ประกอบด้วยข้อมูลที่ประมวลผลแล้วของภูมิประเทศที่มีความแม่นยำระดับ 1 (L1TP) ซึ่งมีรังสีวิทยาที่กำหนดลักษณะไว้อย่างดีและได้รับการปรับเทียบร่วมกันใน เซ็นเซอร์ Landsat ต่างๆ การลงทะเบียนทางภูมิศาสตร์ของฉากระดับ 1 จะสอดคล้องกันและอยู่ภายในค่าความคลาดเคลื่อนที่กำหนด [<=12 ม. ค่าเฉลี่ยความคลาดเคลื่อนกำลังสอง (RMSE)] ถือได้ว่าข้อมูล Landsat ระดับ 1 ทั้งหมดมีความสอดคล้องกันและ ได้รับการปรับเทียบร่วมกัน (ไม่ว่าจะเป็นเซ็นเซอร์ใดก็ตาม) ในคอลเล็กชันทั้งหมด ดูข้อมูลเพิ่มเติมในเอกสารของ USGS