N: 54 S: -54 E: 180 W: -180
Description
ECO3ETPTJPL Version 1 was deprecated on September 30, 2025. Users are encouraged to use the ECO_L3G_JET and ECO_L3T_JET Version 2 data products.
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes.
ECO3ETPTJPL Version 1 is a Level 3 (L3) product that provides evapotranspiration (ET) generated from data acquired by the ECOSTRESS radiometer instrument according to the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the Algorithm Theoretical Basis Document (ATBD). The ET product is generated from the Level 2 data products for surface temperature and emissivity, the Level 1 geolocation information, and a significant number of ancillary data inputs from other sources. ET is set by various controls, including radiative and atmospheric demand, and environmental sensitivity, productivity, vegetation physiology, and phenology. PT-JPL is best utilized for natural ecosystems. The L3 ET product is used for creating the Level 4 products, Evaporative Stress Index (ESI) and Water Use Efficiency (WUE).
The ECO3ETPTJPL Version 1 data product contains variables of instantaneous ET, daily ET, canopy transpiration, soil evaporation, ET uncertainty, and interception evaporation.
Known Issues
- Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.
- Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.
- Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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File Naming Convention
The file name begins with the Sensor (ECOSTRESS) followed by the Processing Level (L3_ET), Geophysical Parameter (PT-JPL), Orbit Number (26313), Scene Identifier (001), Date and Time of Acquisition designated as YYYYMMDDTHHMMSS (20230225T154024), Build Identifier of product generation software (0601), Product Iteration Number (01), and the Data Format (h5).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Advancing point-to-grid scale ET mapping: comparative assessment of ECOSTRESS and MODIS LST and ET across heterogeneous landscapes | Park, Kijin, Baik, Jongjin, Kim, Kiyoung, Park, Jongmin | Evapotranspiration, Latent Heat Flux, Geolocation, Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Clouds | |
| ECOSTRESSderived semiarid forest temperature and evapotranspiration estimates demonstrate drought and thinning impacts | Sankey, Temuulen Tsagaan, Kyaw, Thu Ya, Tatum, Julia, Koch, George W., Kolb, Thomas, Lewis, Rayni, Poulos, Helen M., Barton, Andrew M., LaSala, Blase, Thode, Andrea | Evapotranspiration | |
| Diffuse Fertilization or Lack Thereof: A Multisite Synthesis of Water | Schwartz, E., KeppelAleks, G., Steiner, A. L. | Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Precipitation Rate, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, Land Use/Land Cover Classification, Evapotranspiration, Aerosol Backscatter, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Aerosol Radiance, Carbonaceous Aerosols, Cloud Condensation Nuclei, Dust/Ash/Smoke, Nitrate Particles, Organic Particles, Particulate Matter, Sulfate Particles, Trace Gases/Trace Species, Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Atmospheric Stability, Humidity, Total Precipitable Water, Water Vapor Profiles, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Rain Storms, Atmospheric Ozone | |
| Spatial heterogeneity of agricultural evapotranspiration as quantified | Goodwell, Allison, Zahan, Mushfika, Cao, Jiaze, URycki, Dawn R. | Evapotranspiration | |
| Modeling wildland fire burn severity in California using a spatial Super Learner approach | Simafranca, Nicholas, Willoughby, Bryant, ONeil, Erin, Farr, Sophie, Reich, Brian J., Giertych, Naomi, Johnson, Margaret C., Pascolini-Campbell, Madeleine A. | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Reflectance, Land Use/Land Cover Classification, Evapotranspiration, Land Surface Temperature, Emissivity, Potential Evapotranspiration, Plant Characteristics | |
| Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota | Jain, Khushboo, John, Ranjeet, Torbick, Nathan, Kolluru, Venkatesh, Saraf, Sakshi, Chandel, Abhinav, Henebry, Geoffrey M., Jarchow, Meghann | Land Surface Temperature, Emissivity, Evapotranspiration, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps | |
| Risk Analysis of Soil Erosion Using Remote Sensing, GIS, and Machine Learning Models in Imbabura Province, Ecuador | Garrido, Fernando, Granda, Pedro | Topographical Relief Maps, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Evapotranspiration, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Soil Moisture Profiles of Ecosystem Water Use Revealed With ECOSTRESS | Feldman, Andrew F., Koster, Randal D., CawseNicholson, Kerry, Crow, Wade T., Holmes, Thomas R. H., Poulter, Benjamin | Land Surface Temperature, Emissivity, Evapotranspiration | |
| On the Potential of Active and Passive Microwave Remote Sensing for Tracking Seasonal Dynamics of Evapotranspiration | Jagdhuber, T., Fluhrer, A., Chaparro, D., Dubois, C., Hellwig, F. M., Bayat, B., Montzka, C., Baur, M. J., Ramati, M., Kubert, A., Mueller, M. M., Schellenberg, K., Boehm, M., Jonard, F., Steele-Dunne, S., Piles, M., Entekhabi, D. | Vegetation Water Content, Soil Moisture/Water Content, Skin Temperature, Evapotranspiration, Latent Heat Flux | |
| A global 30-m ET model (HSEB) using harmonized Landsat and Sentinel-2, MODIS and VIIRSComparison to ECOSTRESS ET and LST | Jaafar, Hadi, Mourad, Roya, Schull, Mitch | Land Surface Temperature, Emissivity, Evapotranspiration | |
| Integrating remote sensing with ecology and evolution to advance | Cavender-Bares, Jeannine, Schneider, Fabian D., Santos, Maria Joao, Armstrong, Amanda, Carnaval, Ana, Dahlin, Kyla M., Fatoyinbo, Lola, Hurtt, George C., Schimel, David, Townsend, Philip A., Ustin, Susan L., Wang, Zhihui, Wilson, Adam M. | Evapotranspiration | |
| ECOSTRESS reveals prefire vegetation controls on burn severity for Southern California wildfires of 2020 | PascoliniCampbell, Madeleine, Lee, Christine, Stavros, Natasha, Fisher, Joshua B. | Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Evapotranspiration, Potential Evapotranspiration, Plant Characteristics | |
| Evaluation of remotely-sensed evapotranspiration datasets at different spatial and temporal scales at forest and grassland sites in Italy | De Santis, Domenico, D'Amato, Concetta, Bartkowiak, Paulina, Azimi, Shima, Castelli, Mariapina, Rigon, Riccardo, Massari, Christian | Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux | |
| Assessing evapotranspiration observed from ECOSTRESS using flux measurements in agroecosystems | Wu, Jie, Feng, Yu, Liang, Lili, He, Xinyue, Zeng, Zhenzhong | Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Evapotranspiration | |
| Detecting Hot Spots of Methane Flux Using FootprintWeighted Flux Maps | ReySanchez, Camilo, AriasOrtiz, Ariane, Kasak, Kuno, Chu, Housen, Szutu, Daphne, Verfaillie, Joseph, Baldocchi, Dennis | Evapotranspiration | |
| Toward estimation of seasonal water dynamics of winter wheat from | Jagdhuber, Thomas, Jonard, Francois, Fluhrer, Anke, Chaparro, David, Baur, Martin J., Meyer, Thomas, Piles, Maria | Evapotranspiration | |
| A knowledge-based, validated classifier for the identification of aliphatic and aromatic plastics by WorldView-3 satellite data | Zhou, Shanyu, Kuester, Theres, Bochow, Mathias, Bohn, Niklas, Brell, Maximilian, Kaufmann, Hermann | Evapotranspiration | |
| Mapping daily evapotranspiration at field scale using the Harmonized Landsat and Sentinel-2 dataset, with sharpened VIIRS as a Sentinel-2 thermal proxy | Xue, Jie, Anderson, Martha C., Gao, Feng, Hain, Christopher, Yang, Yun, Knipper, Kyle R., Kustas, William P., Yang, Yang | Evapotranspiration, Land Surface Temperature, Emissivity, Reflectance, Albedo, Anisotropy, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Evaluation of atmospheric boundary layer height from wind profiling radar and slab models and its responses to seasonality of land cover, subsidence, and advection | ReySanchez, Camilo, Wharton, Sonia, VilaGuerau de Arellano, Jordi, Paw U, Kyaw Tha, Hemes, Kyle S., Fuentes, Jose D., Osuna, Jessica, Szutu, Daphne, Ribeiro, Joao Vinicius, Verfaillie, Joseph, Baldocchi, Dennis | Evapotranspiration | |
| GRACE-FO and ECOSTRESS synergies constrain fine-scale impacts on the water balance | PascoliniCampbell, Madeleine, Fisher, Joshua B., Reager, John T. | Evapotranspiration | |
| An evaluation of ECOSTRESS products of a temperate montane humid forest in a complex terrain environment | Liu, Ning, Oishi, A. Christopher, Miniat, Chelcy Ford, Bolstad, Paul | Land Surface Temperature, Emissivity, Evapotranspiration | |
| Challenges and opportunities in precision irrigation decision-support | Zhang, Jingwen, Guan, Kaiyu, Peng, Bin, Jiang, Chongya, Zhou, Wang, Yang, Yi, Pan, Ming, Franz, Trenton E, Heeren, Derek M, Rudnick, Daran R, Abimbola, Olufemi, Kimm, Hyungsuk, Caylor, Kelly, Good, Stephen, Khanna, Madhu, Gates, John, Cai, Yaping | Photosynthetically Active Radiation, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Area Index (LAI), Evapotranspiration, Latent Heat Flux, Land Surface Temperature, Emissivity, Leaf Characteristics | |
| Wildfire severity and vegetation recovery drive post-fire evapotranspiration in a southwestern pine-oak forest, Arizona, USA | Poulos, Helen M., Barton, Andrew M., Koch, George W., Kolb, Thomas E., Thode, Andrea E. | Evapotranspiration | |
| Ecostress and cimisA comparison of potential and reference evapotranspiration in Riverside County, California | Kohli, Gurjot, Lee, Christine M., Fisher, Joshua B., Halverson, Gregory, Variano, Evan, Jin, Yufang, Carney, Daniel, Wilder, Brenton A., Kinoshita, Alicia M. | Evapotranspiration, Potential Evapotranspiration | |
| Applying tipping point theory to remote sensing science to improve early warning drought signals for food security | Krishnamurthy R, P. Krishna, Fisher, Joshua B., Schimel, David S., Kareiva, Peter M. | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Evapotranspiration, Latent Heat Flux |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| ETcanopy | Canopy Evaporation | Percent | float32 | N/A | 0 to 100 | N/A | N/A |
| ETdaily | Daily Latent Heat Flux | W/m² | float32 | N/A | 0 to 2000 | N/A | N/A |
| ETinst | Instantaneous Latent Heat Flux | W/m² | float32 | N/A | 0 to 2000 | N/A | N/A |
| ETinstUncertainty | ETinst Uncertainty | W/m² | float32 | N/A | 0 to 2000 | N/A | N/A |
| ETinterception | Interception Evaporation | Percent | float32 | N/A | 0 to 100 | N/A | N/A |
| ETsoil | Soil Evaporation | Percent | float32 | N/A | 0 to 100 | N/A | N/A |