Leaf Trait Estimation for Global Land Modeling
At the organism level, plant traits are measurable morphological, anatomical, physiological, and phenological traits that can affect individual establishment, fitness, and survival. These measurable features provide fundamental information for interpreting long-term (eg, annual) patterns of global carbon, water, energy fluxes, and biodiversity. We provide the only remote sensing-based map of leaf characteristics at a global 1 km spatial resolution. In particular, we present global maps of specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen content per dry weight (LNC) and leaf phosphorus content per dry weight (LPC). The approach combines MODIS and Landsat data, climatological data (Worldclim), the largest trait database (TRY), and machine learning algorithms.
The figure below is a flowchart of our method for providing trait estimation. The numbered boxes represent three main parts of the method: (1) fill in the gaps in the trait database; (2) calculate the community weighted mean trait values of the MODIS pixels in the canopy with nearby trait observation data; (3) spatialize the CWMs to 500 Global trait maps at meter resolution.
Full information on the method can be found here. Users can also use the following applications in GEE to explore the dataset.
The data is also available in two spatial resolutions, 3 km and 1 km. It can be downloaded from these links 1, 2. Global Trait Maps with GEE
Additional information about v3¶.
Version 3.0 of the processing chain prevents extrapolation and uses an updated taxonomic trait table. To prevent extrapolation, this updated version of the processing chain uses a random forest algorithm (RF) with surrogates to estimate trait values. The Random Forest algorithm with surrogates allows to obtain an ensemble of models within the convex hull of the input data for prediction. In addition, using an up-to-date and broader table of taxonomic traits can increase the training sample size for map generation.
references
inson, N., Reichstein, M., Bodegom, P. V., Kramer, K., Cornelissen, J. H. C., Reich, P. B.,
Bahn, M., Niinemets, Ü., Peñuelas, J., Craine, J., Cerabolini, B., Minden, V., Laughlin, D. C., Sack, L., Allred, B., Baraloto, C., Byun, C.,
Soudzilovskaia, N. A., Running, S. W. (2018). A methodology to derive global maps of leaf traits using remote sensing and climate data.
Remote Sensing of Environment, 218, 69-88. [doi](https://doi.org/10.1016/j.rse.2018.09.006)
Earth Engine Snippet¶
// SLA (mm2/g)
var SLA=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/SLA_1km_v3').select([0],['SLA']);
var SLA_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/SLA_sd_1km_v3').select([0],['SLA_sd']);
// LNC (mg/g)
var LNC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LNC_1km_v3').select([0],['LNC']);
var LNC_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LNC_sd_1km_v3').select([0],['LNC_sd']);
// LPC (mg/g)
var LPC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LPC_1km_v3').select([0],['LPC']);
var LPC_SD=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LPC_sd_1km_v3').select([0],['LPC_sd']);
// Leaf dry matter content LDMC (g/g)
var LDMC=ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LDMC_1km_v3').select([0],['LDMC']);
var LDMC_SD = ee.Image('projects/sat-io/open-datasets/GLOBAL-LEAF-TRAITS/LDMC_sd_1km_v3').select([0],['LDMC_sd']);
//let's mask unprocessed data (Positive values correspond with natural vegetated areas)
SLA = SLA.mask(SLA.gt(0));
LNC = LNC.mask(LNC.gt(0));
LPC = LPC.mask(LPC.gt(0));
LDMC = LDMC.mask(LDMC.gt(0));
var vis_vi = {min:7 , max: 22, palette: ["ffffd9", "edf8b1", "c7e9b4", "7fcdbb", "41b6c4", "1d91c0", "225ea8", "253494", "081d58"]};
Map.addLayer(SLA, vis_vi, 'SLA (mm2 / g)',true)
License¶
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Contact information¶
If you have any further questions or doubts, please don't hesitate to contact us.
Curated by: Alvaro Moreno-Martínez, Gustau Camps-Valls, Jens Kattge, Nathaniel Robinson, Markus Reichstein, Peter van Bodegom, Josep Peñuelas, Brady Allred, Steve W. Running
Curated copy in GEE by: Samapriya Roy
Keywords: Plant traits, Machine learning, Remote sensing, Plant ecology, Climate, MODIS, Landsat
Last updated: Nov 2021
Last updated in GEE: 2022-12-18