OpenET - DisALEXI
This repository provides Google Earth Engine Python API based implementation of the DisALEXI ET model.
DisALEXI is the disaggregation component of a multi-scale system for modeling actual evapotranspiration (ETa) at field to global scales. DisALEXI spatially downscales regional gridded ET output from the Atmosphere-Landsat Exchange Inverse (ALEXI) model to finer scales using moderate to high resolution remotely sensed land-surface temperature data. Both ALEXI and DisALEXI are based on the Two Source Energy Balance (TSEB) land-surface representation originally developed by Norman et al., (1995).
Input Collections
DisALEXI ET can currently be computed for Landsat Collection 2 Level 2 (surface reflectance) images from the following Earth Engine image collections:
LANDSAT/LC09/C02/T1_L2
LANDSAT/LC08/C02/T1_L2
LANDSAT/LE07/C02/T1_L2
LANDSAT/LT05/C02/T1_L2
Note that this version of DisALEXI can only be run over the conterminous United States (CONUS)
Model Design
The primary component of the DisALEXI model is the Image() class. The Image class should generally be instantiated from an Earth Engine Landsat image using the collection specific methods listed below. ET image collections can be built by computing ET in a function that is mapped over a collection of input images. Please see the Example Notebooks for more details.
Landsat Collection 2 SR Input Image
To instantiate the class for a Landsat Collection 2 level 2 image, use the Image.from_landsat_c2_sr() method.
The input Landsat image must have the following bands and properties:
SPACECRAFT_ID |
Band Names |
|---|---|
LANDSAT_5 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL |
LANDSAT_7 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL |
LANDSAT_8 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, ST_B10, QA_PIXEL |
LANDSAT_9 |
SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, ST_B10, QA_PIXEL |
Property |
Description |
|---|---|
system:id |
Earth Engine Asset ID (e.g. LANDSAT/LC08/C02/T1_L2/LC08_044033_20170716) |
system:index |
|
system:time_start |
Image datetime in milliseconds since 1970 |
SPACECRAFT_ID |
|
Model Output
The primary output of the DisALEXI model is daily ETa. Internally this is partitioned to contributions from soil evaporation (E) and canopy transpiration (T).
Examples
Jupyter notebooks are provided in the “examples” folder that show various approaches for calling the OpenET DisALEXI model.
Example Notebooks
Jupyter notebooks are provided in the “examples” folder that show various approaches for calling the OpenET DisALEXI model.
computing daily ET for a single Landsat SR image
Installation
The OpenET DisALEXI python module can be installed via pip:
pip install openet-disalexiDependencies
Modules needed to run the model:
OpenET Namespace Package
Each OpenET model should be stored in the “openet” folder (namespace). The benefit of the namespace package is that each ET model can be tracked in separate repositories but called as a “dot” submodule of the main openet module.
import openet.disalexi as disalexiReferences
Anderson, M. C., R. G. Allen, A. Morse, W. P. Kustas (2012a), Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources, Remote Sens. Environ. 122, 50-65. https://doi.org/10.1016/j.rse.2011.08.025
Anderson, M. C., F. Gao, K. Knipper, C. Hain, W. Dulaney, D. D. Baldocchi, E. Eichelmann, K. S. Hemes, Y. Yang, J. Medellin-Azuara, W. P. Kustas (2018), Field-scale assessment of land and water use change over the California Delta using remote sensing. Remote Sens. 10:889. https://doi.org/10.3390/rs10060889
Norman, J. M., W. P. Kustas, K. S. Humes (1995), A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric. For. Meteorol. 77:263-293. https://doi.org/10.1016/0168-1923(95)02265-Y
Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas (2007), A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation, J. Geophys. Res., 112, D10117. https://doi.org/10.1029/2006JD007506
Anderson, M. C., J. M. Norman, G. R. Diak, W. P. Kustas, J. R. Mecikalski (1997), A two-source time integrated model for estimating surface fluxes using thermal infrared remote sensing, Remote Sens. Environ. 60, 195-216. https://doi.org/10.1029/2006JD007507
Anderson, M. C., J. M. Norman, J. R. Mecikalski, R. D. Torn, W. P. Kustas, J. B. Basara (2004), A multiscale remote sensing model for disaggregating regional fluxes to micrometeorological scales, J. Hydrometeorol. 5, 343-363. https://doi.org/10.1175/1525-7541(2004)005<0343:AMRSMF>2.0.CO;2
Anderson, M. C., W.P. Kustas, J. G. Alfieri, F. Gao, C. Hain, J. H. Prueger, S. Evett, P. Colaizzi, T. Howell, J. L. Chavez (2012b), Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign (2012b), Adv. Water Resour, 50, 162-177. https://doi.org/10.1016/j.advwatres.2012.06.005
Cammalleri, C., M.C. Anderson, F. Gao, C.R. Hain, W.P. Kustas, Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion, Agricultural and Forest Meteorology, Volume 186, 2014, Pages 1-11, ISSN 0168-1923, https://doi.org/10.1016/j.agrformet.2013.11.001.
Semmens, K. A., M. C. Anderson, W. P. Kustas, F. Gao, J. G. Alfieri, L. McKee, J. H. Prueger, C. R. Hain, C. Cammalleri, Y. Yang and T. Xia (2016), Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach, Remote Sens. Environ., 185, 155–170.
Yang, Y., M. C. Anderson, F. Gao, C. R. Hain, K. A. Semmens, W. P. Kustas, A. Noormets, R. H. Wynne, V. A. Thomas, and G. Sun (2017), Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA using multi-satellite data fusion, Hydrol. Earth Syst. Sci., 21, 1017–1037. doi:doi:10.5194/hess-21-1017-2017
Anderson, M., G. Diak, F. Gao, K. Knipper, C. Hain, E. Eichelmann, K.S. Hemes, D. Baldocchi, W. Kustas, Y. Yang (2018), Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta, Remote Sensing, 11(3): 216, doi: 10.3390/rs11030216
Yang, Y., M. Anderson, F. Gao, C. Hain, A. Noormets, G. Sun, R. Wynne and V. Thomas (2020), Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data, Remote Sensing of Environment, 238, p. 111018
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