openet-geesebal 0.2.2

Earth Engine based GEESEBAL Model

<img src=”https://github.com/et-brasil/EESEBAL/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 de energy balance equation (LE + H = Rn - G) to estimate Daily Evapotranspiration (ET) by using Landsat images (L4, L5, L7 and L8) and meteorological data (air temperature, relative humidity, global radiation and wind speed).

Image Collections IDs |

:——————–: |

LANDSAT/LC08/C01/T1_SR |

LANDSAT/LE07/C01/T1_SR |

LANDSAT/LT05/C01/T1_SR |

LANDSAT/LT04/C01/T1_SR |

LANDSAT/LC08/C02/T1_L2 |

LANDSAT/LE07/C02/T1_L2 |

LANDSAT/LT05/C02/T1_L2 |

LANDSAT/LT04/C02/T1_L2 |

## Input Collections

The following Earth Engine image collection are use in geeSEBAL:

## 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 1 SR Input Image

• Select Image.from_landsat_c1_sr() method to instantiate the class for a Landsat Collection 1 SR image. Image must have the following bands and properties:

SPACECRAFT_ID | Band Names |

————- | —————————————– |

LANDSAT_4 | B1, B2, B3, B4, B5, B7, B6, pixel_qa |

LANDSAT_5 | B1, B2, B3, B4, B5, B7, B6, pixel_qa |

LANDSAT_7 | B1, B2, B3, B4, B5, B7, B6, pixel_qa |

LANDSAT_8 | B1, B2, B3, B4, B5, B6, B7, B10, pixel_qa |

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) |

SOLAR_ZENITH_ANGLE - Solar zenith angle in degrees |

---

#### 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 |

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) |

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/C01/T1_SR/LC08_044033_20170801’) model_obj = geesebal.from_landsat_c1_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-beta>`

## 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)