The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge. In 2021, all Landsat data were processed into Landsat Collection 2. Collection 2 data are the newest and currently the preferred Landsat products. Landsat Collection 1 data and products were removed from public access on December 30, 2022.

This dataset contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 8 OLI/TIRS sensors. These images contain 5 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and one thermal infrared (TIR) band processed to orthorectified surface temperature. They also contain intermediate bands used in calculation of the ST products, as well as QA bands. Landsat 8 SR products are created with the Land Surface Reflectance Code (LaSRC). All Collection 2 ST products are created with a single-channel algorithm jointly created by the Rochester Institute of Technology (RIT) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Strips of collected data are packaged into overlapping "scenes" covering approximately 170km x 183km using a standardized reference grid. Some assets have only SR data, in which case ST bands are present but empty. For assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'. For assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'. Additional documentation and usage examples. Data provider notes: Data products must contain both optical and thermal data to be successfully processed to surface temperature, as ASTER NDVI is required to temporally adjust the ASTER GED product to the target Landsat scene. Therefore, night time acquisitions cannot be processed to surface temperature. A known error exists in the surface temperature retrievals relative to clouds and possibly cloud shadows. The characterization of these issues has been documented by Cook et al., (2014). ASTER GED contains areas of missing mean emissivity data required for successful ST product generation. If there is missing ASTER GED information, there will be missing ST data in those areas. The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. While this dataset has a global spatial extent, there are areas missing mean emissivity information due to persistent cloud contamination in the ASTER measurements. The USGS further screens unphysical values (emissivity < 0.6) in ASTER GED to remove any emissivity underestimation due to undetected clouds. For any given pixel with no ASTER GED input or unphysical emissivity value, the resulting Landsat ST products have missing pixels. The missing Landsat ST pixels will be consistent through time (1982-present) given the static nature of ASTER GED mean climatology data. For more information refer to landsat-collection-2-surface-temperature-data-gaps-due-missing

This document describes relevant characteristics of the Collection 1 (C1) Landsat 8 (L8) Level 2 (L2) Surface Reflectance (SR), Top of Atmosphere (TOA) Reflectance, and TOA Brightness Temperature (BT) products to facilitate their use in the land remote sensing community. SR and TOA Reflectance are derived from C1 L8 Operational Land Imager (OLI) Level 1 (L1) data; TOA BT is derived from C1 L8 OLI and Thermal Infrared Sensor (TIRS) L1 data using the Landsat Collection 1 Land Surface Reflectance Code (LaSRC). Information about SR processing of C1 Landsat 4-5 Thematic Mapper (TM) and C1 Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data can be found in the Landsat 4-7 Collection 1 Ecosystem Disturbance Adaptive Processing System (LEDAPS) Product Guide. Other processing options, such as spectral indices, format conversion, spatial subset, and/or coordinate system reprojection are described in other product guides and web pages. This document is under Landsat Satellites Data System (LSDS) Configuration Control Board (CCB) control. Please submit changes to this document, as well as supportive material justifying the proposed changes, via Change Request (CR) to the Process and Change Management Tool.

Landsat 8 Operational Land Imager (OLI)Landsat 8 OLI Collection 1 Surface Reflectance are generated using the Land Surface Reflectance Code (LaSRC) (version 1.4.1), which makes use of the coastal aerosol band to perform aerosol inversion tests, uses auxiliary climate data from MODIS, and uses a unique radiative transfer model. (Vermote et al., 2016).LaSRC hardcodes the view zenith angle to “0”, and the solar zenith and view zenith angles are used for calculations as part of the atmospheric correction. The Landsat 8 Collection 1 Land Surface Reflectance Code (LaSRC) Product Guide contains details about the LaSRC algorithm and the Landsat 8 Surface Reflectance data products created from it. Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+)Landsat 4-5 TM and Landsat 7 ETM+ Collection 1 Surface Reflectance are generated using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm (version 3.4.0), a specialized software originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006).The software applies Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric correction routines to Level-1 data products. Water vapor, ozone, atmospheric pressure, aerosol optical thickness, and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA) reflectance, surface reflectance, TOA brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water.The Landsat 4-7 Collection 1 Surface Reflectance Product Guide contains details about the LEDAPS algorithm and the Surface Reflectance data products created from it.

Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely on these data for historical study of land surface change, but shoulder the burden of post-production processing to create applications-ready data sets. In compliance with guidelines established through the Global Climate Observing System, USGS has embarked on production of higher-level Landsat data products to support land surface change studies. Terrestrial variables such as surface reflectance are being prototyped as fundamental input to higher level products such as Burned Area, Fractional Snow Covered Area, and Dynamic Surface Water Extent. These products are intended to provide a framework for producing long-term Landsat science data collections suited for monitoring, assessing, and predicting land surface change over time.

這個資料集包含經過大氣校正的表面反射率和地表溫度，這些資料是根據Landsat 8 OLI/TIRS 感應器產生的資料所得。這些圖像包含5 個可見光和近紅外線(VNIR) 波段和2 個短波紅外線(SWIR) 波段，經過處理後可轉換為正射表面反射率，以及經過處理的熱紅外線(TIR) 波段，可轉換為正射表面溫度。這些檔案也包含用於計算ST 產品的中間頻帶，以及品質管理頻帶。 Landsat 8 SR 產品是使用Land Surface Reflectance Code (LaSRC) 建立。所有 Collection 2 ST 產品都是使用Rochester Institute of Technology (RIT) 和美國國家航空暨太空總署(NASA) 噴射推進實驗室(JPL) 共同開發的單一管道演算法建立。 收集的資料會以標準化參考格線分割成重疊的「影像」，涵蓋約170 公里 x 183 公里的範圍。 部分素材資源只有SR 資料，在這種情況下，ST 頻帶會顯示，但內容為空白。如果素材資源同時包含ST 和 SR 頻帶，則「PROCESSING_LEVEL」會設為「L2SP」。如果素材資源只有SR 頻帶，則「PROCESSING_LEVEL」會設為「L2SR」。 其他說明文件和使用範例。 資料提供者附註： 資料產品必須同時包含光學和熱像資料，才能成功將其處理為地表溫度，因為需要ASTER NDVI 才能將ASTER GED 產品與目標Landsat 影像進行時間調整。因此，夜間的資料擷取作業無法轉換為表面溫度。 在雲層和可能的雲影中，地表溫度擷取作業會發生已知錯誤。Cook 等人(2014)。 ASTER GED 包含缺少平均發射率資料的區域，而這項資料是產生成功的ST 產品所需。如果缺少ASTER GED 資訊，這些地區就會缺少ST 資料。 ASTER GED 資料集是根據2000 年至2008 年間取得的ASTER 場景中，所有無雲的天空像素所建立。雖然這個資料集涵蓋全球空間範圍，但由於ASTER 測量資料中持續有雲層污染，因此部分區域缺少平均發射率資訊。 USGS 會進一步篩選ASTER GED 中的非物理值(發射率< 0.6)，以移除因未偵測到的雲層而導致的任何發射率低估值。對於任何沒有ASTER GED 輸入值或非物理發射率值的像素，產生的Landsat ST 產品都會缺少像素。由於 ASTER GED 平均氣候資料具有靜態性質，因此缺少的Landsat ST 像素會在1982 年至今的時間範圍內保持一致。詳情請參閱landsat-collection-2-surface-temperature-data-gaps-due-missing

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) 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 HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI 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 HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 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.

• NDVI generation spike difference: There is a spike difference in HLSL30 and HLSS30 when generating NDVI index from granules after 2021 which was resolved with the integration of Landsat 9 in January 2023; however, it was not back processed. The HLS team is aware of this issue and is currently working on a fix.

• 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. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low.

• 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

ชุดข้อมูลนี้มีค่าการสะท้อนแสงพื้นผิวและอุณหภูมิพื้นผิวดินที่ปรับบรรยากาศแล้ว ซึ่งมาจากข้อมูลที่ได้จากเซ็นเซอร์ OLI/TIRS ของ Landsat 8 รูปภาพเหล่านี้ประกอบด้วยแถบคลื่นที่มองเห็นได้และอินฟราเรดใกล้ (VNIR) 5 แถบ และแถบอินฟราเรดคลื่นสั้น (SWIR) 2 แถบที่ได้รับการประมวลผลเพื่อหาค่าการสะท้อนแสงของพื้นผิวที่ผ่านการออโตรเรคทิไฟ และแถบอินฟราเรดความร้อน (TIR) 1 แถบที่ได้รับการประมวลผลเพื่อหาอุณหภูมิของพื้นผิวที่ผ่านการออโตรเรคทิไฟ รวมถึงมีย่านความถี่กลางที่ใช้ในการคำนวณผลิตภัณฑ์ ST และย่านความถี่ QA ด้วย ผลิตภัณฑ์ Landsat 8 SR สร้างขึ้นด้วยรหัสการสะท้อนแสงของพื้นดิน (LaSRC) ผลิตภัณฑ์ ST ของคอลเล็กชัน 2 ทั้งหมดสร้างขึ้นด้วยอัลกอริทึมแบบช่องทางเดียวที่สถาบันเทคโนโลยีแห่งโรเชสเตอร์ (RIT) และห้องปฏิบัติการขับเคลื่อนด้วยไอพ่น (JPL) ขององค์การนาซาร่วมกันพัฒนาขึ้น แถบข้อมูลที่รวบรวมจะจัดแพ็กเกจเป็น "ฉาก" ที่ซ้อนทับกันครอบคลุมพื้นที่ประมาณ 170 x 183 กิโลเมตรโดยใช้ตารางอ้างอิงมาตรฐาน ชิ้นงานบางรายการมีเฉพาะข้อมูล SR ในกรณีนี้จะมีแถบ ST อยู่แต่ว่างเปล่า สำหรับชิ้นงานที่มีทั้งย่านความถี่ ST และ SR ระบบจะตั้งค่า "PROCESSING_LEVEL" เป็น "L2SP" สําหรับชิ้นงานที่มีเฉพาะย่านความถี่ SR ระบบจะตั้งค่า "PROCESSING_LEVEL" เป็น "L2SR" เอกสารประกอบและตัวอย่างการใช้งานเพิ่มเติม หมายเหตุของผู้ให้บริการข้อมูล ผลิตภัณฑ์ข้อมูลต้องมีทั้งข้อมูลเชิงแสงและข้อมูลความร้อนจึงจะประมวลผลเป็นอุณหภูมิพื้นผิวได้สําเร็จ เนื่องจากต้องใช้ ASTER NDVI เพื่อปรับผลิตภัณฑ์ ASTER GED ตามช่วงเวลาให้เข้ากับภาพ Landsat เป้าหมาย ดังนั้น ระบบจึงประมวลผลข้อมูลที่ได้รับในเวลากลางคืนเป็นอุณหภูมิพื้นผิวไม่ได้ มีข้อผิดพลาดที่ทราบในการดึงข้อมูลอุณหภูมิพื้นผิวที่เกี่ยวข้องกับเมฆและอาจรวมถึงเงาของเมฆ Cook และคณะได้บันทึกลักษณะของปัญหาเหล่านี้ไว้ (2014) ASTER GED มีบริเวณที่ไม่มีข้อมูลการแผ่รังสีความร้อนเฉลี่ยซึ่งจําเป็นสําหรับการสร้างผลิตภัณฑ์ ST ที่ประสบความสําเร็จ หากข้อมูล ASTER GED ขาดหายไป ก็จะไม่มีข้อมูล ST ในพื้นที่เหล่านั้น ชุดข้อมูล GED ของ ASTER สร้างขึ้นจากพิกเซลท้องฟ้าแจ่มใสทั้งหมดของภาพ ASTER ที่รวบรวมตั้งแต่ปี 2000 ถึง 2008 แม้ว่าชุดข้อมูลนี้จะมีขอบเขตเชิงพื้นที่ทั่วโลก แต่ก็มีบางพื้นที่ที่ไม่มีข้อมูลการแผ่รังสีความร้อนเฉลี่ยเนื่องจากมีเมฆปกคลุมอย่างต่อเนื่องในการวัดของ ASTER USGS จะกรองค่าที่ไม่สมจริงเพิ่มเติม (การแผ่รังสีความร้อน < 0.6) ใน ASTER GED เพื่อนำการแผ่รังสีความร้อนที่ประเมินต่ำเกินไปออกเนื่องจากเมฆที่ตรวจไม่พบ สำหรับพิกเซลใดก็ตามที่ไม่มีอินพุต GED ของ ASTER หรือค่าการแผ่รังสีความร้อนที่ไม่สมจริง ผลิตภัณฑ์ Landsat ST ที่ออกมาจะมีพิกเซลที่ขาดหายไป พิกเซล Landsat ST ที่ขาดหายไปจะมีความสอดคล้องกันตลอดช่วงเวลา (1982-ปัจจุบัน) เนื่องจากลักษณะของข้อมูลภูมิอากาศเฉลี่ย GED ของ ASTER เป็นแบบคงที่ ดูข้อมูลเพิ่มเติมได้ที่ landsat-collection-2-surface-temperature-data-gaps-due-missing

Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface.DE Africa contains Landsat Collection 1, Level 2 surface reflectance products over five countries (Tanzania, Senegal, Sierra Leone, Ghana, and Kenya). Landsat Collection 1 consists of products generated from the Landsat 8 Operational Land Imager (OLI) / Thermal Infrared Sensor (TIRS), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 4-5 Thematic Mapper (TM), and Landsat 1-5 Multispectral Scanner (MSS) instruments. The implementation of collections ensures consistent and known radiometric and geometric quality through time and across instruments and improves control in the calibration and processing parameters.This product has a spatial resolution of 30 m and a temporal coverage of 2013 to 2019. The surface reflectance values are scaled to be between 0 and 10,000. It is provided by United States Geological Survey (USGS).For more information on the Landsat surface reflectance product, see https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance

Tập dữ liệu này chứa độ phản chiếu bề mặt được hiệu chỉnh theo khí quyển và nhiệt độ bề mặt đất bắt nguồn từ dữ liệu do cảm biến OLI/TIRS của Landsat 8 tạo ra. Những hình ảnh này chứa 5 dải ánh sáng nhìn thấy và gần hồng ngoại (VNIR) và 2 dải hồng ngoại sóng ngắn (SWIR) được xử lý để phản xạ bề mặt được chỉnh sửa theo phương pháp orthorectification và một dải hồng ngoại nhiệt (TIR) được xử lý để …

CANUE staff developed annual estimates of maximum mean warm-season land surface temperature (LST) recorded by LandSat 8 at 30m resolution. To reduce the effect of missing data/cloud cover/shadows, the highest mean warm-season value reported over three years was retained - for example, the data for 2021 represent the maximum of the mean land surface temperature at a pixel location between April 1st and September 30th in 2019, 2020 and 2021. Land surface temperature was calculated in Google Earth Engine, using a public algorithm (see supplementary documentation). In general, annual mean LST may not reflect ambient air temperatures experienced by individuals at any given time, but does identify areas that are hotter during the day and therefore more likely to radiate excess heat at night - both factors that contribute to heat islands within urban areas.

Cet ensemble de données contient la réflectance de surface corrigée par l'atmosphère et la température de surface des sols dérivées des données produites par les capteurs OLI/TIRS de Landsat 8. Ces images contiennent cinq bandes visible et proche infrarouge (VNIR) et deux bandes infrarouge à ondes courtes (SWIR) traitées pour obtenir la réflectance de la surface orthorectifiée, ainsi qu'une bande infrarouge thermique (TIR) traitée pour obtenir la température de la surface orthorectifiée. Elles contiennent également des bandes intermédiaires utilisées pour le calcul des produits ST, ainsi que des bandes de contrôle qualité. Les produits Landsat 8 SR sont créés avec le code de réflectance de la surface terrestre (LaSRC). Tous les produits ST de la collection 2 sont créés à l'aide d'un algorithme à canal unique créé conjointement par l'Institut de technologie de Rochester (RIT) et le Jet Propulsion Laboratory (JPL) de la NASA. Les bandes de données collectées sont empaquetées dans des "scènes" qui se chevauchent et couvrent environ 170 km x 183 km à l'aide d'une grille de référence standardisée. Certains composants ne comportent que des données SR, auquel cas les bandes ST sont présentes, mais vides. Pour les composants avec des bandes ST et SR, la valeur "PROCESSING_LEVEL" est définie sur "L2SP". Pour les composants qui ne comportent que des bandes SR, la valeur de "PROCESSING_LEVEL" est définie sur "L2SR". Documentation supplémentaire et exemples d'utilisation Remarques du fournisseur de données: Les produits de données doivent contenir à la fois des données optiques et thermiques pour être correctement traités en température de surface, car l'indice NDVI ASTER est nécessaire pour ajuster le produit GED ASTER de manière temporelle à la scène Landsat cible. Par conséquent, les acquisitions nocturnes ne peuvent pas être traitées pour obtenir la température de surface. Une erreur connue existe dans la récupération de la température de surface par rapport aux nuages et aux ombres des nuages. La caractérisation de ces problèmes a été documentée par Cook et al., (2014). Le GED ASTER contient des zones où les données d'émissivité moyenne requises pour générer des produits ST sont manquantes. Si des informations GED ASTER sont manquantes, des données ST seront manquantes dans ces zones. L'ensemble de données GED ASTER est créé à partir de tous les pixels de ciel dégagé des scènes ASTER acquises entre 2000 et 2008. Bien que cet ensemble de données ait une étendue spatiale globale, il manque des informations sur l'émissivité moyenne dans certaines zones en raison de la contamination persistante des nuages dans les mesures ASTER. L'USGS filtre également les valeurs non physiques (émissivité < 0,6) dans ASTER GED pour supprimer toute sous-estimation de l'émissivité due à des nuages non détectés. Pour tout pixel donné sans entrée GED ASTER ou valeur d'émissivité non physique, les produits ST Landsat qui en résultent comportent des pixels manquants. Les pixels ST Landsat manquants seront cohérents au fil du temps (1982 à aujourd'hui) en raison de la nature statique des données climatologiques moyennes de GED ASTER. Pour en savoir plus, consultez landsat-collection-2-surface-temperature-data-gaps-due-missing.

Leaf Area Index (LAI) is a fundamental vegetation structural variable that drives energy and mass exchanges between the plant and the atmosphere. Moderate-resolution (300m – 7km) global LAI data products have been widely applied to track global vegetation changes, drive Earth system models, monitor crop growth and productivity, etc. Yet, cutting-edge applications in climate adaptation, hydrology, and sustainable agriculture require LAI information at higher spatial resolution (< 100m) to model and understand heterogeneous landscapes. This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine. Each record/sample/row includes a MODIS LAI value, corresponding Landsat surface reflectance in green, red, NIR, SWIR1 bands, a land cover (biome) type, geographic location, and other auxiliary information. Each sample represents a MODIS LAI pixel (500m) within which a single biome type dominates 90% of the area. The spatial homogeneity of the samples was further controlled by a screening process based on the coefficient of variation of the Landsat surface reflectance. In total, there are approximately 1.6 million samples, stratified by biome, Landsat sensor, and saturation status from the MODIS LAI algorithm. This dataset can be used to train machine learning models and generate LAI maps for Landsat 5, 7, 8 surface reflectance images within CONUS. Detailed information on the sample generation and quality control can be found in the related journal article. Resources in this dataset:Resource Title: README. File Name: LAI_train_samples_CONUS_README.txtResource Description: Description and metadata of the main datasetResource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab Resource Title: LAI_training_samples_CONUS. File Name: LAI_train_samples_CONUS_v0.1.1.csvResource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang (kangyanghui@gmail.com)
Column description

UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE
Landsat_ID: Landsat image ID Date: Landsat image date in "YYYYMMDD" Latitude: Latitude (WGS84) of the MODIS LAI pixel center Longitude: Longitude (WGS84) of the MODIS LAI pixel center MODIS_LAI: MODIS LAI value in "m2/m2" MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2" MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation NLCD_class: Majority class code from the National Land Cover Dataset (NLCD) NLCD_frequency: Percentage of the area cover by the majority class from NLCD Biome: Biome type code mapped from NLCD (see below for more information) Blue: Landsat surface reflectance in the blue band Green: Landsat surface reflectance in the green band Red: Landsat surface reflectance in the red band Nir: Landsat surface reflectance in the near infrared band Swir1: Landsat surface reflectance in the shortwave infrared 1 band Swir2: Landsat surface reflectance in the shortwave infrared 2 band Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value. Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value. NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance EVI: Enhanced Vegetation Index computed from Landsat surface reflectance NDWI: Normalized Difference Water Index computed from Landsat surface reflectance GCI: Green Chlorophyll Index = Nir/Green - 1

Biome code

1 - Deciduous Forest
2 - Evergreen Forest
3 - Mixed Forest
4 - Shrubland
5 - Grassland/Pasture
6 - Cropland
7 - Woody Wetland
8 - Herbaceous Wetland

Reference Dataset: All data was accessed through Google Earth Engine 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. MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006 Landsat 5/7/8 Collection 1 Surface Reflectance Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey. Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. National Land Cover Dataset (NLCD) Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006 Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

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.

Landsat-derived water surface temperature in Mount Hope Bay with bias correction from RI DEM buoys. For translation from the x,y coordinates to latitude and longitude see the "Landsat Satellite Coordinates version 2, Narragansett Bay" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2022, and the mean bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was added to or subtrcted from all scenes by satellite. The standard deviation between the buoys and the satellite after adding the bias is 1.9 degrees C for Landsat 5, 1.9 degrees C for Landsat 7 and 1.3 degrees C for Landsat 8. The standard deviation is considered the uncertainty of the satellite measurements. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.10, ACDD-1.3 defaultGraphQuery=temperature[0:last][0:last][last]&.draw=surface&.vars=X|Y|temperature description=Landsat-derived water surface temperature in Narragansett Bay with bias correction from RI DEM buoys. history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Mount Hope Bay (2003-2022) to each satellite was added to or subtracted from all scenes from the corresponding satellite. For Landsat 5, 0.45 degrees C was subtracted from all scenes; similarly, 1.094 was subtracted for Landsat 7, and 0.178 was added for Landsat 8. The errors were determined by averaging the five closest temporal buoy readings for each satellite image capture and spatially averaging buoy locations within a 200-square-meter zone. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=United States Geological Survey keywords_vocabulary=GCMD Science Keywords publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 time_coverage_end=2022-09-29T00:00:00Z time_coverage_start=1984-05-02T00:00:00Z

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for longer-term time series analysis, cloudless imagery and statistical accuracy.

GeoMAD has two main components: Geomedian and Median Absolute Deviations (MADs).

The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms.

For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas.

Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface and understand change over time.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 2013 – 2020*Spatial Resolution: 30 x 30 meterUpdate frequency: Annual from 2013 - 2020Product Type: Surface Reflectance (SR)Product Level: Analysis Ready (ARD)Number of Bands: 10 BandsParent Dataset: Landsat Collection 2 Level-2 Surface ReflectanceSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone.ApplicationsGeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:Longer-term time series analysisCloud-free imageryStatistical accuracyAvailable BandsBand IDDescriptionValue rangeData typeNo data valueSR_B2Geomedian SR_B2 (Blue)1 - 10000uint160SR_B3Geomedian SR_B3 (Green)1 - 10000uint160SR_B4Geomedian SR_B4 (Red)1 - 10000uint160SR_B5Geomedian SR_B5 (NIR)1 - 10000uint160SR_B6Geomedian SR_B6 (SWIR 1)1 - 10000uint160SR_B7Geomedian SR_B7 (SWIR 2)1 - 10000uint160SMADSpectral Median Absolute Deviation0 - 1float32NaNEMADEuclidean Median Absolute Deviation0 - 31623float32NaNBCMADBray-Curtis Median Absolute Deviation0 - 1float32NaNCOUNTNumber of clear observations1 - 65535uint160Bands have been subdivided as follows:Geomedian - 6 bands: The geomedian is calculated using the spectral bands of data collected during the specified time period. Surface reflectance values have been scaled between 1 and 10000 to allow for more efficient data storage as unsigned 16-bit integers (uint16). Note parent datasets often contain more bands, some of which are not used in GeoMAD.Median Absolute Deviations (MADs) - 3 bands: Deviations from the geomedian are quantified through median absolute deviation calculations. The GeoMAD service utilises three MADs, each stored in a separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and Bray-Curtis MAD (BCMAD). Each MAD is calculated using the same ten bands as in the geomedian. SMAD and BCMAD are normalized ratios, therefore they are unitless and their values always fall between 0 and 1. EMAD is a function of surface reflectance but is neither a ratio nor normalized, therefore its valid value range depends on the number of bands used in the geomedian calculation - ten in GeoMAD.Count - 1 band: The number of clear satellite measurements of a pixel for that calendar year. This is around 20 for Landsat 8 annually, but doubles at areas of overlap between scenes. “Count” is not incorporated in either the geomedian or MADs calculations. It is intended for metadata analysis and data validation.ProcessingAll clear observations for the given time period are collated from the parent dataset. Cloudy pixels are identified and excluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version 0.2.Known LimitationsThe Landsat 8 (& 9) GeoMAD has a known issue with data quality over marine regions. The GeoMAD algorithm uses pixel quality information from the input data to identify and mask pixels with poor quality obervations. Landsat 8 & 9 analysis ready satellite images over the ocean often contain negative surface reflectance values, and the GeoMAD masking procedures remove pixels where any negative values occur. Thus, in regions where pixels are persistently negative throughout the year, the GeoMAD product will contain a no-data value. An example of this can be seen in Image 7 below where a shallow marine system contains no-data values in the GeoMAD because the NIR band values in the input data are persistently negative.More details on this dataset can be found here.

This dataset contains the European Coverage of Landsat 8 Collection 2 data, both Level 1 and Level 2, since the beginning of the mission. Landsat 8 Collection 2 is the result of reprocessing effort on the archive and on fresh products with significant improvement with respect to Collection 1 on data quality, obtained by means of advancements in data processing, algorithm development. The primary characteristic is a relevant improvement in the absolute geolocation accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2 Global Reference Image, GRI) but includes also updated digital elevation modelling sources, improved Radiometric Calibration (even correction for the TIRS striping effect), enhanced Quality Assessment Bands, updated and consistent metadata files, usage of Cloud Optimized Georeferenced (COG) Tagged Image File Format. Landsat 8 level 1 products combine data from the 2 Landsat instruments, OLI and TIRS. The level 1 products generated can be either L1TP or L1GT: • L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. • L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level1 products • Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data is made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). • Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of ≦ 12-meter radial root mean square error (RMSE). • Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: • L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. • L2SR - Level 2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated Two different categories of Level 1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). For the Level 2 data, only LC combined products are generated

このデータセットには、Landsat 8 OLI/TIRS センサーによって生成されたデータから得られた、大気補正済みの地表反射率と地表温度が含まれています。これらの画像には、オルソ補正された地表反射率に処理された 5 つの可視近赤外線（VNIR）バンドと 2 つの短波赤外線（SWIR）バンド、および … に処理された 1 つの熱赤外線（TIR）バンドが含まれています。

Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely on these data for historical study of land surface change, but shoulder the burden of post-production processing to create applications-ready data sets. In compliance with guidelines established through the Global Climate Observing System, USGS has embarked on production of higher-level Landsat data products to support land surface change studies. Terrestrial variables such as surface reflectance are being prototyped as fundamental input to higher level products such as Burned Area, Fractional Snow Covered Area, and Dynamic Surface Water Extent. These products are intended to provide a framework for producing long-term Landsat science data collections suited for monitoring, assessing, and predicting land surface change over time.

Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely on these data for historical study of land surface change, but shoulder the burden of post-production processing to create applications-ready data sets. In compliance with guidelines established through the Global Climate Observing System, USGS has embarked on production of higher-level Landsat data products to support land surface change studies. Terrestrial variables such as surface reflectance are being prototyped as fundamental input to higher level products such as Burned Area, Fractional Snow Covered Area, and Dynamic Surface Water Extent. These products are intended to provide a framework for producing long-term Landsat science data collections suited for monitoring, assessing, and predicting land surface change over time.