Penman-Monteith-Leuning Evapotranspiration In Google Earth Engine
Modeling framework
Penman-Monteith-Leuning model (abbreviated as PML_V1) was proposed by Leuning et al. (2008), and further improved by Zhang et al., (2010, 2016). In PML, evaporation is divided into: transpiration from vegetation (Ec), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from
vegetation (Ei).
PML_V2 was developed by Gan et al., (2018) and Zhang et al., (2019), which coupled ET and gross primary production via canopy conductance theory. They are both in the resolution of 500 m and 8-day, and range from -60°S to 90°N.
Figure 1. Flowchart of global forcing data processing and PML_V2 modeling processes.
| Variable |
Description |
Unit |
| Tmax |
daily maximum temperature |
°C |
| Tmin |
daily minimum temperature |
°C |
| Tavg |
daily mean temperature |
°C |
| Pa |
atmosphere pressure |
kPa |
| U |
wind speed at 10-m height |
m/s |
| q |
specific humidity |
kg/kg |
| Prcp |
precipitation |
mm/d |
| Rln |
inward longwave solar radiation |
W/m2 |
| Rs |
inward shortwave solar radiation |
W/m2 |
| Pi |
the difference of Prcp and Ei |
mm/d |
| Es_eq |
equilibrium evaporation |
mm/d |
| ET_water |
evaporation from water body, snow and ice |
mm/d |
| qc |
quality control variable for albedo and surface emissivity. |
- |
global CO2: https://data.globalchange.gov/dataset/noaa-cmdl-co2_mm_gl
Figure 2. The spatial distribution of yearly sum evapotranspiration (ET) and gross primary product (GPP) in 2018.
Data product
Table 1. PML_V1 and PML_V2 bands information (PML_V1 have no GPP band, other
bands are some).
Note: Only PMLV1 is available currently.
| BandName |
Units |
Scale |
Description |
| GPP |
gC m-2 d-1 |
0.01 |
Gross primary product |
| Ec |
mm d-1 |
0.01 |
Vegetation transpiration |
| Es |
mm d-1 |
0.01 |
Soil evaporation |
| Ei |
mm d-1 |
0.01 |
Interception from vegetation canopy |
| ET_water |
mm d-1 |
0.01 |
Water body, snow and ice evaporation. Penman
evapotranspiration is regarded as actual evaporation for them. |
| qc |
- |
- |
Interpolation information for Albedo and Emissivity.
Bitmask for qc:
Bits 0-2: Emissivity interpolation information
0: good value, no interpolation
1: linear interpolation
2: history 8-day average interpolation
3: history monthly average interpolation
Bits 3-5: Albedo interpolation information
Same as Emissivity. |
Usage
var imgcol_8d = ee.ImageCollection("projects/pml_evapotranspiration/PML/OUTPUT/PML_V2_8day_v016");
var pkg_export = require('users/kongdd/public:pkg_export.js');
var options = {
type: "drive",
range: [-180, -60, 180, 90],
cellsize: 1 / 10,
crs: 'EPSG:4326',
folder: 'PMLV2'
};
imgcol_8d = imgcol_8d.select([0, 1, 2, 3, 4, 5]);
print('latest:', imgcol_8d.filterDate('2020-01-01', '2023-01-01'));
pkg_export.ExportImgCol(imgcol_8d.limit(3), null, options, 'PMLV2_latest');
1.1 Access data
Click the following links to get the access. The corresponding links are:
1.2 Data download
PML products are standard ee.ImageCollection object in GEE.
You can clip regional data by polygon shapefile from ee.ImageCollection.
- For small regions, you can transform
ee.ImageCollection into multiple bands ee.Image. In this way, you can download all the dataset in a time:
- For large regions, you have to download trough
ee.ImageCollection.
Clip and export the regional data you need by the polygon shapefile you uploaded. This is a little example.
Updates
- 2019-08-02: extend the time period to 2018
- 2020-09-11: extend to 2020-05-24, Dongdong Kong
Known issues
- PML_V2 data missing due to LAI images missing: fixed
References:
[1]. Zhang, Y.*, Kong, D.*, Gan, R., Chiew, F.H.S., McVicar, T.R., Zhang, Q., and Yang, Y… (2019) Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182, https://doi:10.1016/j.rse.2018.12.031
[2]. Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing*, 155(May), 13–24. https://doi.org/10.1016/j.isprsjprs.2019.06.014
[3]. Zhang, Y., Peña-Arancibia, J.L., McVicar, T.R., Chiew, F.H.S., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y.Y., Miralles, D.G., Pan, M., 2016. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124. https://doi.org/10.1038/srep19124
[4]. Zhang, Y., Leuning, R., Hutley, L.B., Beringer, J., McHugh, I., Walker, J.P., Using long-term water balances to parameterize surface conductances and calculate evaporation at 0.05°spatial resolution. Water Resour. Res. 46. https://doi.org/10.1029/2009WR008716
[5]. Leuning, R., Zhang, Y.Q., Rajaud, A., Cleugh, H., Tu, K., 2008. A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resour. Res. 44. https://doi.org/10.1029/2007WR006562
[6]. Gan, R., Zhang, Y., Shi, H., Yang, Y., Eamus, D., Cheng, L., Chiew, F.H.S., Yu, Q., 2018. Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems.
Ecohydrology. e1974. https://doi.org/10.1002/eco.1974
[7]. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27.
https://doi.org/10.1016/j.rse.2017.06.031
Acknowledgements
Keep in mind that this repository is released under a GPL2 license, which permits commercial use but requires that the source code (of derivatives) is always open even if hosted as a web service.
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