Penman-Monteith-Leuning Evapotranspiration In Google Earth Engine

仅开源模型核心代码部分，其余部分转为闭源，如果合作需求，请私信。

Known issues

• PML_V2 data missing due to LAI images missing: fixed
• ET_water resample is incorrect in the product of PML_V2 0.1 deg (25 Aug, 2021)

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.

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.

Usage

// available from 2000-02-26 to 2020-05-24
// Update at 2020-09-11, Dongdong Kong
var imgcol_8d = ee.ImageCollection("projects/pml_evapotranspiration/PML/OUTPUT/PML_V2_8day_v016");

/**
 * Copyright (c) 2019 Dongdong Kong. All rights reserved.
 * This work is licensed under the terms of the MIT license.
 * For a copy, see <https://opensource.org/licenses/MIT>.
 */
var pkg_export = require('users/kongdd/pkgs:pkg_export.js');
// var pkg_trend  = require('users/kongdd/public:Math/pkg_trend.js');
// export parameters
var options = {
    type: "drive",
    range: [-180, -60, 180, 90], // [73, 25, 105, 40], 
    cellsize: 1 / 10,
    // crsTransform : [463.312716528, 0, -20015109.354, 0, -463.312716527, 10007554.677], // prj.crsTransform;
    // scale        : 463.3127165275, // prj.scale
    crs: 'EPSG:4326', // 'SR-ORG:6974', // 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), 'PMLV2_latest', options);

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.

• Upload your polygon shapefile to GEE https://developers.google.com/earth-engine/importing
• Download data from GEE
  https://developers.google.com/earth-engine/exporting

1. 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:
2. 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

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.