| Name | openet-geesebal JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | Earth Engine implementation of the GEESEBAL model |
| upload_time | 2024-05-05 15:55:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2021 et-brasil Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
geesebal
openet
earth engine
evapotranspiration
landsat
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
OpenET - geeSEBAL
=================
<img src="https://github.com/et-brasil/geeSEBAL/blob/master/Images/geeSEBAL_logo_update_cut.png?raw=true" width="140">
## Estimating Evapotranspiration using SEBAL model in Google Earth Engine platform.
* The Google Earth Engine Surface Energy Balance for Land (geeSEBAL) solves the energy balance equation (LE + H = Rn - G) to estimate Daily Evapotranspiration (ET) by using Landsat images (L4, L5, L7, L8, and L9) and meteorological data (air temperature, relative humidity, global radiation and wind speed).
## Input Collections
* The following Earth Engine image collection are use in geeSEBAL:
| Image Collections IDs |
| :--------------------: |
| LANDSAT/LC09/C02/T1_L2 |
| LANDSAT/LC08/C02/T1_L2 |
| LANDSAT/LE07/C02/T1_L2 |
| LANDSAT/LT05/C02/T1_L2 |
| LANDSAT/LT04/C02/T1_L2 |
## Model Description
* Surface Energy Balance Algorithm for Land (SEBAL) was developed and validated by Bastiaanssen (Bastiaanssen et al., 1998a, 1998b) to estimate evapotranspiration (ET) from energy balance equation (Rn – G = LE + H), where LE, Rn, G and H are Latent Heat Flux, Net Radiation, Soil Heat Flux and Sensible Heat Flux, respectively. SEBAL estimates LE as a residual of others energy fluxes (LE = Rn - LE - G).
* SEBAL algorithm has an internal calibration, assuming a linear relationship between dT and LST across domain area, where dT is designed as a vertical air temperature (Ta) floating over the land surface, considering two extreme conditions. At the hot and dry extreme condition, LE is zero and H is equal to the available energy, whereas at the cold and wet extreme condition, H is zero and LE is equal to the available energy.
* Workflow of geeSEBAL, demonstrating remote sensing and global meteorological inputs, as well as data processing to estimate daily evapotranspiration.
## Model Design
### Image()
* Compute Daily ET or ET fraction for a single input image.
* Allow to obtain ET image collections by mapping over Landsat collections.
#### Landsat Collection 2 Input Image
* Select Image.from_landsat_c2_sr() method to instantiate the class for a Landsat Collection 2 SR image. Image must have the following bands and properties:
| SPACECRAFT_ID | Band Names |
| ------------- | ----------------------------------------------------------------- |
| **LANDSAT_4** | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL |
| **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 |
| PROPERTIES |
| --------------------------------------------------------------------------------------------- |
| **system: index** - Landsat scene ID (ex: LC08_044033_20170801) |
| **system: time_start** - Time start of the image in epoch time |
| **SPACECRAFT_ID** - Landsat Satellite (LANDSAT_4, LANDSAT_5, LANDSAT_7, LANDSAT_8, LANDSAT_9) |
| **SUN_ELEVATION** - Solar elevation angle in degrees |
## Model Output
The general outputs of the geeSEBAL are ndvi (normalized difference vegetation index), lst (land surface temperature), et_fraction and et. They can be selected as example below:
### Example
import openet.geesebal as geesebal
ls_img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044033_20170801')
model_obj = geesebal.from_landsat_c2_sr(ls_img)
ndvi = model_obj.ndvi
lst = model_obj.lst
et_fraction = model.et_fraction
et = model_obj.et
## Examples Notebooks
Examples of how to use geeSEBAL model are detailed in *examples* folder:
[geeSEBAL examples.](https://github.com/et-brasil/openet-geesebal/blob/main/examples "Examples")
## Installation
pip install openet-geesebal
### Depedencies
* `earthengine-api` <https://github.com/google/earthengine-api>`
* `openet-core` <https://github.com/Open-ET/openet-core>`
## References
[[2021] Laipelt, L., Kayser, R. H. B., Fleischmann A., Ruhoff, A., Bastiaanssen, W., Erickson, T., Melton, F. Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing.](https://doi.org/10.1016/j.isprsjprs.2021.05.018)
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"description": "OpenET - geeSEBAL\n=================\n\n<img src=\"https://github.com/et-brasil/geeSEBAL/blob/master/Images/geeSEBAL_logo_update_cut.png?raw=true\" width=\"140\">\n\n## Estimating Evapotranspiration using SEBAL model in Google Earth Engine platform.\n\n* The Google Earth Engine Surface Energy Balance for Land (geeSEBAL) solves the energy balance equation (LE + H = Rn - G) to estimate Daily Evapotranspiration (ET) by using Landsat images (L4, L5, L7, L8, and L9) and meteorological data (air temperature, relative humidity, global radiation and wind speed).\n\n## Input Collections\n\n* The following Earth Engine image collection are use in geeSEBAL:\n\n| Image Collections IDs |\n| :--------------------: |\n| LANDSAT/LC09/C02/T1_L2 |\n| LANDSAT/LC08/C02/T1_L2 |\n| LANDSAT/LE07/C02/T1_L2 |\n| LANDSAT/LT05/C02/T1_L2 |\n| LANDSAT/LT04/C02/T1_L2 | \n\n## Model Description\n\n* Surface Energy Balance Algorithm for Land (SEBAL) was developed and validated by Bastiaanssen (Bastiaanssen et al., 1998a, 1998b) to estimate evapotranspiration (ET) from energy balance equation (Rn \u2013 G = LE + H), where LE, Rn, G and H are Latent Heat Flux, Net Radiation, Soil Heat Flux and Sensible Heat Flux, respectively. SEBAL estimates LE as a residual of others energy fluxes (LE = Rn - LE - G).\n* SEBAL algorithm has an internal calibration, assuming a linear relationship between dT and LST across domain area, where dT is designed as a vertical air temperature (Ta) floating over the land surface, considering two extreme conditions. At the hot and dry extreme condition, LE is zero and H is equal to the available energy, whereas at the cold and wet extreme condition, H is zero and LE is equal to the available energy.\n* Workflow of geeSEBAL, demonstrating remote sensing and global meteorological inputs, as well as data processing to estimate daily evapotranspiration.\n\n\n\n## Model Design\n\n### Image()\n\n* Compute Daily ET or ET fraction for a single input image.\n* Allow to obtain ET image collections by mapping over Landsat collections.\n\n#### Landsat Collection 2 Input Image\n\n* Select Image.from_landsat_c2_sr() method to instantiate the class for a Landsat Collection 2 SR image. Image must have the following bands and properties:\n\n| SPACECRAFT_ID | Band Names |\n| ------------- | ----------------------------------------------------------------- |\n| **LANDSAT_4** | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL |\n| **LANDSAT_5** | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL | \n| **LANDSAT_7** | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B7, ST_B6, QA_PIXEL | \n| **LANDSAT_8** | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, ST_B10, QA_PIXEL | \n| **LANDSAT_9** | SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7, ST_B10, QA_PIXEL | \n\n| PROPERTIES |\n| --------------------------------------------------------------------------------------------- |\n| **system: index** - Landsat scene ID (ex: LC08_044033_20170801) |\n| **system: time_start** - Time start of the image in epoch time |\n| **SPACECRAFT_ID** - Landsat Satellite (LANDSAT_4, LANDSAT_5, LANDSAT_7, LANDSAT_8, LANDSAT_9) |\n| **SUN_ELEVATION** - Solar elevation angle in degrees |\n\n## Model Output\n\nThe general outputs of the geeSEBAL are ndvi (normalized difference vegetation index), lst (land surface temperature), et_fraction and et. They can be selected as example below:\n\n### Example\n\n\timport openet.geesebal as geesebal\n\t\n\tls_img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044033_20170801')\n\tmodel_obj = geesebal.from_landsat_c2_sr(ls_img)\n\n\tndvi = model_obj.ndvi\n\tlst = model_obj.lst\n\tet_fraction = model.et_fraction\n\tet = model_obj.et\n\n## Examples Notebooks\n\nExamples of how to use geeSEBAL model are detailed in *examples* folder:\n\n[geeSEBAL examples.](https://github.com/et-brasil/openet-geesebal/blob/main/examples \"Examples\")\n\n## Installation\n\n\tpip install openet-geesebal\n\n### Depedencies\n\n * `earthengine-api` <https://github.com/google/earthengine-api>`\n * `openet-core` <https://github.com/Open-ET/openet-core>`\n\n## References\n\n[[2021] Laipelt, L., Kayser, R. H. B., Fleischmann A., Ruhoff, A., Bastiaanssen, W., Erickson, T., Melton, F. Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing.](https://doi.org/10.1016/j.isprsjprs.2021.05.018)\n",
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