This dataset contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 9 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 9 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

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.

This dataset contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 7 ETM+ sensor. These images contain 4 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 7 SR products are created with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm (version 3.4.0). 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 Note that Landsat 7's orbit has been drifting to an earlier acquisition time since 2017.

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

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

This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).

Surface reflectance measured by satellite remote sensing can be used to evaluate optical water quality in lakes and reservoirs.

This resource contains surface reflectance data from the Landsat 5 and Landsat 7 sensors at locations throughout the Great Salt Lake, Utah Lake, and Farmington Bay in Utah, USA. Calibration data contain near-coincident samples of surface chlorophyll (from the Utah Division of Water Quality, USGS, and Jordan River-Farmington Bay Water Quality Council), while historical reflectance data contain only surface reflectance data over the historic record of 1984-2016. Reflectance data are provided for each of the visible, near infrared, and shortwave infrared bands (scaled from 0-10000). These data were downloaded via Google Earth Engine, which hosts the surface reflectance products that are produced by the USGS. Cloud Mask bands indicate data with potential cloud/haze/atmospheric interferences (any value >1).

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

This data set includes the monthly composite 30 m × 30 m surface vegetation index products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize the monthly NDVI products on the earth's surface by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.

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

The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset.

These data were compiled to evaluate changes due to mining in the Madre de Dios Region in Peru. Landsat data are from Landsat missions 5 and 7. The Landsat LT1 Spectral Reflectance product data in this compilation were retrieved automatically using Google Earth Engine.

DATA SOURCES:

IMAGERY USGS Landsat LT1 Surface Reflectance, retrieved via Google Earth Engine

Translation from x,y coordinates to latitude and longitude for the "Landsat Satellite Surface Temperature 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-1.6, ACDD-1.3 defaultGraphQuery=Longitude[0:last][0:last][last]&.draw=surface&.vars=X|Y|Longitude 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 Narragansett 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=Rhode Island Data Discovery/United States Geological Survey 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

These data are in three tables each reporting estimated iron mineralization within stream reaches from three arctic rivers in northwestern Alaska on select days between July-August, 1985-2022. The relative magnitude of iron mineralization was measured using a remotely sensed iron index calculated from available Level 2, Collection 2, Tier 1 Landsat Surface Reflectance products derived by the USGS and accessed within Google Earth Engine (https://earthengine.google.com).

Savanna ecosystems across the globe have experienced substantial changes in their vegetation composition. These changes can be attributed to three main processes: (1) encroachment, which refers to the expansion of woody plants into open areas, (2) thicketization, which is characterized by the growth of sub-canopy woody plants, and (3) disturbance, defined here as the removal of woodland cover due to both natural forces and human activities. In this study, we utilized Landsat surface reflectance data and Sentinel-1 SAR data to track the progression of these process from 1996 to 2022 in the significantly modified Post Oak Savannah ecoregion of Central Texas. Our methodology employs an ensemble classification algorithm, which combines the results of multiple models, to develop a more precise predictive model, along with the spectral–temporal segmentation algorithm LandTrendr in Google Engine (GEE). Our ensemble classification algorithms demonstrated high overall accuracies of 94.3 and 96.5% for 1996 and 2022, respectively, while our LandTrendr vegetation map exhibited an overall accuracy of 80.4%. The findings of our study reveal that 9.7% of the overall area experienced encroachment of woody plants into open area, while an additional 6.8% of the overall area has transitioned into a thicketized state due to the growth of sub-canopy woody plants. Furthermore, 5.7% of the overall area encountered woodland disturbance leading to open areas. Our findings suggest that these processes advanced unevenly throughout the region, resulting in the coexistence of three prominent plant communities that appear to have long-term stability: a dense deciduous shrubland in the southern region, as well as a thicketized oak woodland and open area mosaic in the central and northern regions. The successional divergence observed in these plant communities attests to the substantial influence of human modification on the landscape. This study demonstrates the potential of integrating passive optical multispectral data and active SAR data to accurately map large-scale ecological processes.

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 heter..., 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 b..., , # Global overview of cloud-, snow-, and shade-free Landsat (1982-2024) and Sentinel-2 (2015-2024) data

Suggested citation: Lewińska, Katarzyna Ewa, et al. (2024). Global overview of cloud-, snow-, and shade-free Landsat (1982-2024) and Sentinel-2 (2015-2024) data [Dataset]. Dryad. https://doi.org/10.5061/dryad.gb5mkkwxm

Description of the data and file structure

A global overview of usable (i.e., cloud-, snow-, and shade-free) 1982-2024 Landsat and 2015-2024 Sentinel-2 data, derived for a regular 0.18° x 0.18°-point grid.

Dataset featured in Lewińska 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

The complete dataset comprises three .csv files:

• GLOBAL_LND_1982-2024_CSO.csv: Daily data availability derived from Landsat 1982-2024 archives
• GLOBAL_S2_2015-...,

The data are distributed as a csv and GeoTIFF (both formats comprising exactly the same information) and can be open and query using any software able to handle these data formats.

The 30m seamless annual leaf-on Landsat composites from 1985 to 2023 were generated using a comprehensive framework designed to ensure high-quality, consistent data across decades. Starting with preprocessed Level-2 surface reflectance images from multiple Landsat sensors, the dataset is restricted to the Leaf-On season, with rigorous cloud and shadow masking applied based on quality assessment bands. To maintain consistency across sensors, spectral harmonization is conducted, followed by annual composite generation using the medoid method to capture peak vegetation conditions. The resulting composites are structured into a spatially consistent data cube, facilitating efficient analysis and monitoring of vegetation dynamics over time.

The band naming convention follows Landsat TM standards, with bands designated as Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), and SWIR2 (B7). Both qualitative and quantitative evaluations were conducted to validate the data quality. Here, we provide 2023 image data covering southwestern forest regions of China as a sample for testing. For access to the full dataset, please visit Google Earth Engine at this link, and Earth Engine App (Landsat Yearly Composite Viewer) at this link.

Data citation: Yaotong Cai, Peng Zhu, and Xiaoping Liu (2024). China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023. Submitting.

For data-related inquiries, please contact Dr. Yaotong Cai at caiyt33@mail2.sysu.edu.cn.

Many freshwater forested wetlands along the southeastern U.S. coastline are rapidly transitioning from forest to marsh or open water, due to climate change related disturbances, such as saltwater intrusion and increasing flooding frequency. These changes in wetland state are considered a regime shift, and the timing and trajectory of change are not well understood. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands.

In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula, North Carolina, U.S.A. We used 35 years of the Landsat record to examine trends and variance of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts.

We found that NDVI time series trends combined with changes in standard deviation of NDVI allowed us to identify four scenarios of c...

This spreadsheet contains soil carbon (total carbon [%]) data at the site level corresponding to samples extracted via field campaigns for York University during September 2018, August 2019 and August-September 2021 in the District of Cochrane in northern Ontario, Canada. These samples were obtained principally from the 5-15 cm depth layer of mineral soil. This spreadsheet file also contains environmental covariate data at the site level for LiDAR-derived covariates, surface reflectance, SAR, aeromagnetic survey, and tree species data.LiDAR data were obtained from the Ontario Ministry of Natural Resources (MNR) via Land Information Ontario, and contain information licensed under the Open Government licence – Ontario. Surface reflectance data was obtained from Landsat imagery from the United States Geological Survey (USGS) via Google Earth Engine. SAR data from Sentinel with the European Space Agency (ESA), were retrieved via Google Earth Engine. Aeromagnetic survey data was obtained from Natural Resources Canada (NRCan), and tree specie data was obtained from the National Forest Inventory (NFI) of Canada.