N: 90 S: -90 E: 180 W: -180
Description
The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.
Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.
The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.
Known Issues
- Operational and uncertainty issues are provided under Section 3 in the User Guide.
- For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.
Version Description
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 Product Short Name (MOD16A2) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2025201), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h07v05), the Version of the data collection (061), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2025217003056), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
PRODUCT QUALITY ASSESSMENT
SCIENCE DATA PRODUCT VALIDATION
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Bedrock controls vegetation resilience: Dominant role of lithology in | Li, Qian, Yue, Yuemin, Wang, Lu, Qi, Xiangkun, Wang, Kelin | Photosynthesis, Primary Production, Vegetation Productivity, Evapotranspiration, Latent Heat Flux | |
| Assessments of eight ET products based on long-term multi-source water | Fu, Xiangyong, Li, Changming, Yang, Hanbo | Evapotranspiration, Latent Heat Flux | |
| Bayesian machine learning identifies precipitation as a key predictor in | Wang, Zhaoqi, Yu, Sheng, Huang, Xiaotao, Zhou, Ting, Zhang, Haichen, Liu, Yangyang, Wu, Guiling, Zheng, Kai, Xiang, Mingxue, Ma, Tao, Liu, Dan, Lu, Yongkang | Evapotranspiration, Latent Heat Flux | |
| Effect of topographic factors on the spatial distribution of surface | Chu, Yaoyao, Sun, Hao, Gao, Jinhua, Yang, Zhibo, Guo, Wenshuo, Pei, Haoyu | Albedo, Anisotropy, Land Surface Temperature, Emissivity, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps, Evapotranspiration, Latent Heat Flux | |
| Continental-scale mapping of forest tree density in North America using remote sensing and deep learning with uncertainty quantification | Ahmad, Mustak, Tang, Yun, Lister, Andrew J., Gamarra, Javier G.P., Powell, William G., Beane, Nathan R., Choi, Wook Jin, Mitra, Ankita, Kumar, Amit, Cuchietti, Anibal, Paquette, Alain, Searle, Eric, Chen, Jiaxin, Chen, Han Y.H., Bongers, Frans, Meave, Jorge A., Guevara, Mario, Barreras, Aylin, de la Rosa, Jose Armando Alanis, Mayorga Saucedo, Rafael, Cuenca Lara, Rubi Angelica, Moreno Garcia, Cesar, Godinez Valdivia, Carlos Isaias, Delgado Caballero, Carina Edith, de los Angeles Soriano Luna, Maria, Aldrete Leal, Metzli Ileana, Medina Casillas, Sandra Liliana, Romero Correa, Johny, Villela Gaytan, Sergio Armando, Corral Rivas, J. Javier, Vega-Nieva, Jose Daniel, Briseno-Reyes, Jaime, Lopez-Serrano, Pablito Marcelo, Fayle, Tom M., Altman, Jan, Johnson, Daniel J., Liang, Jingjing | Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Evapotranspiration, Latent Heat Flux | |
| Corn-soybean expansion nears threshold for adverse nitrogen effects in the Great Plains | Waterman, Breanna Rivera, Hansen, Amy T, Loecke, Terrance D, Kirk, Matthew F | Evapotranspiration, Latent Heat Flux | |
| Assessing of the Spatio-Temporal Climate and Airborne Pollution Changes and Trends in Ukrainian Steppe Zone | Kharytonov, Mykola, Lakyda, Petro, Matushevych, Liubov, Andreiev, Artem, Kozlova, Anna, Stankevich, Sergey | Land Surface Temperature, Emissivity, Evapotranspiration, Latent Heat Flux, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Environmental Degradation in Iraq: Attribution of Climatic Change and | Alqaraghuli, Akram, North, Peter, Bye, Iain, Rosette, Jacqueline, Los, Sietse | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Evapotranspiration, Latent Heat Flux, Land Surface Temperature, Emissivity | |
| Estimation of actual evapotranspiration from the SEBAL model and comparison with four datasets in an irrigation district of China | Cheng, Huan, Liu, Dengfeng, Ming, Guanghui, Han, Songjun, Khan, Mohd Yawar Ali, Wang, Lei, Li, Qiang | Evapotranspiration, Latent Heat Flux | |
| Identifying Coupled Cropland Abandonment-Land Productivity Trajectories | Zhang, Xuyang, Chen, Xiaoyang, Song, Wei | Evapotranspiration, Latent Heat Flux | |
| Impact of land-use change on ecosystem services in Africa's Great Green | Wang, Yizhuo, Scott, Catherine E., Dallimer, Martin | Land Use/Land Cover Classification, Plant Phenology, Enhanced Vegetation Index (EVI), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Evapotranspiration, Latent Heat Flux, Topographical Relief Maps, Terrain Elevation, Digital Elevation/Terrain Model (DEM) | |
| High-Resolution Downscaling of GRACE-Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data-Scarce Tropical Catchments | Karunarathna, S., Dissanayake, B. C., Gunawardhana, L., Rajapakse, L. | Evapotranspiration, Latent Heat Flux, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff | |
| Gradient-explicit assessment of surface urban heat island intensity in subtropical megacities | Cheng, Zilang, Li, Qinshan, Zhou, Siyu, Bei, Qianyi, Lin, Zhuoyan, Lin, Hongzhang, Xie, Jing, Xue, Desheng | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Evapotranspiration, Latent Heat Flux, Land Surface Temperature, Emissivity, Albedo, Anisotropy | |
| Exploring the Fire Regime in Gile National Park, Zambezia Province, Central Mozambique | Domingos, Joao C., Montfort, Frederique, Lisboa, Sa N., Buramuge, Victorino, Senkoro, Annae, Maquia, Ivete S., Ribeiro-Barros, Ana I., Ribeiro, Natasha S. | Evapotranspiration, Latent Heat Flux | |
| Revisiting the Thornthwaite-Mather method to characterize drought impacts on groundwater recharge in Greece | Zohaib, Muhammad, Stathopoulos, Stavros, Ahrari, Amirhossein, Torabi Haghighi, Ali, Kourtidis, Konstantinos, Gemitzi, Alexandra | Evapotranspiration, Latent Heat Flux | |
| Response of Vegetation Phenology to Hydrothermal Variables on the QTP Using EVI and MSAVI | Zhao, Zhijian, Lin, Hui, Wang, Li, Huang, Min, Wu, Lei, Tang, Linling, Yang, Tao, Xiao, Xin | Albedo, Anisotropy, Evapotranspiration, Latent Heat Flux, Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Reflectance | |
| Restoration Contexts and Trends in the Brazilian Atlantic Forest: | Oliveira, Sofia Corradi, Soares-Filho, Britaldo Silveira, Domingues, Getulio Fonseca, Oliveira, Ubirajara | Evapotranspiration, Latent Heat Flux | |
| Projections of Meteorological Drought and Potential Evapotranspiration | Salehnia, Nasrin, Wolter, Peter T., Chikabvumbwa, Sylvester, Hosseini, Fateme, Kolsoumi, Sohrab, Farid, Ali, Zahedipour, Hashem | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Evapotranspiration, Latent Heat Flux | |
| Sequence and Seasonal Timing of Weather Extremes Influence Ecosystem Stability in Tallgrass Prairie and Juniper Woodland: T. Zhang and others | Zhang, Tian, Zou, Chris B., Potts, Daniel L., Yang, Jia | Evapotranspiration, Latent Heat Flux | |
| Integrating Maximum Entropy Production Theory and Machine Learning to | Xu, Donghui, Ivanov, Valeriy, Tran, Vinh Ngoc, Feng, Dongyu, Bisht, Gautam, Wang, Jingfeng, Leung, L. Ruby | Evapotranspiration, Latent Heat Flux | |
| On the Prediction and Evaluation of Terrestrial and Remote Sensing-Aided Groundwater Level in Tropical Peatlands | Utomo, Waluyo Yogo, Anwar, Syaiful, Tarigan, Suria Darma, Barus, Baba | Evapotranspiration, Latent Heat Flux | |
| Modeling hydrologic response to wildfires in the Pacific Northwest with | Kang, Hyunwoo, Naficy, Cameron E., Bladon, Kevin D. | Evapotranspiration, Latent Heat Flux | |
| PhysicsInformed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events | Song, Yalan, Sawadekar, Kamlesh, Frame, Jonathan M., Pan, Ming, Clark, Martyn P., Knoben, Wouter J. M., Wood, Andrew W., Lawson, Kathryn E., Patel, Trupesh, Shen, Chaopeng | Evapotranspiration, Latent Heat Flux | |
| Sustainable grazing strategies for balancing soil conservation and | Liu, Le, Chen, Yunming, Peng, Shouzhang, Han, Qinggong, Wu, Yang | Evapotranspiration, Latent Heat Flux, Photosynthesis, Primary Production, Vegetation Productivity, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Spatiotemporal prediction of grass curing in Victoria, Australia using | Liao, Liangwei, Zhu, Xuan | 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 |
|---|---|---|---|---|---|---|---|
| ET_500m | Total Evapotranspiration | kg/m²/8day | int16 | 32761 | -32767 to 32700 | 0.1 | N/A |
| ET_QC_500m | Evapotranspiration Quality Control flags | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| LE_500m | Average Latent Heat Flux | J/m²/day | int16 | 32761 | -32767 to 32700 | 10000 | N/A |
| PET_500m | Total Potential Evapotranspiration | kg/m²/8day | int16 | 32761 | -32767 to 32700 | 0.1 | N/A |
| PLE_500m | Average Potential Latent Heat Flux | J/m²/day | int16 | 32761 | -32767 to 32700 | 10000 | N/A |