New publication – A methodology to derive global maps of leaf traits using remote sensing and climate data

Text by Lauri Laanisto

We have a lot of robots nowadays. So why not give them fieldwork assignments? It´s not as simple. Remember how much problems Mars rover “Curiosity” has had. And there is no life in Mars! With all the vegetation and animals and destructive humans – no wonder that developing a fully operational field work robot has been a real challenge for people.

Another option is to use “extraterrestrial” robots. We have quite a lot of stuff hovering on the orbit, including remote sensing satellites. But is it possible to measure for example plant traits from satellites? This has been the direction in remote sensing research for quite a while now. And the paper, which is the basis of this blogspot as it includes Ülo as one of the authors, describes one novel way how to get SLA, LNC, LPC, LDMC and other traits from MODIS/Landsat data with spatial resolution of 500 meters.

So – read the paper how to do it! (Or why…)

Citation: Moreno-Martínez, Á., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P., … & Niinemets, Ü. (2018). A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote Sensing of Environment, 218, 69-88. (link to full text)

Abstract:

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson’s correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.

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