ANALYSIS OF LARGE SCALE SOIL SPECTRAL LIBRARIES

Transcription

1 Antoine Stevens (1), Marco Nocita (1,2), & Bas van Wesemael (1) ANALYSIS OF LARGE SCALE SOIL SPECTRAL LIBRARIES 1 Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, Place Louis Pasteur, 3, 1348 Louvain-la-Neuve, Belgium 2 SOIL Action, Land Resource Management Unit, Institute for Environment and Sustainability, Joint Research Centre of the European Commission, Via E. Fermi 2749, Ispra (VA), Italy

2 PART I: Large scale soil spectral libraries State of the Art

3 Large Spectral Libraries: State of the Art Shepherd & Walsh (2002): 1,000 samples from eastern and southern Africa (305 citations!) Brown et al (2006): 3,768 in US and 400 in the rest of the world (top 10 in terms of citations!) ICRAF-ISRIC : 4,436 samples from 785 soil profiles distributed across the five continents Viscarra Rossel & Webster (2012): 21,500 samples from 4,000 profiles in Australia Stevens et al. (2013): LUCAS database containing 20,000 samples collected over 23 countries of the EU

4 Large Spectral Libraries: State of the Art Rapid Carbon Assessment (2013): 144,833 samples in 6,017 locations across conterminous US Africa Soil Information Service (2013): 17,000 so far from 60 sentinel sites of 100 square km in sub- Saharan Africa National/Regional Spectral libraries: France (Goge et al., 2012): 2,200 samples Denmark (Knadel et al., 2012): 2,851 samples Czech Republic (Brodsky et al., 2011): 500+ samples Florida (Vasques et al., 2010): 7,120 samples Many others. Local spectral librairies and spectral librairies made for a specific research objective: impossible to count!

5 Large Spectral Libraries: State of the Art Most of samples have been scanned with an ASD Some of them are based on legacy soil databases and others have been build from scratch, on purpose Soil analytical measures have been obtained with different methods Big spectral libraries are useful to build robust predictions over large areas

6 Large Spectral Libraries: Prediction Performance Often RPD values are high (~2) for properties having a direct link with the soil chromophores However, RMSE are often too high for most applications: World: RMSE = g C kg -1 for OC Europe: RMSE = 4-15 g C kg -1 for OC Florida: RMSE = 6-7 g C kg -1 for OC compared to a SEL of 1-2 g C kg -1 (dry combustion) So, what factors influence model performance of BIG libraries?

7 1/ Laboratory conditions!

8 2/ Reference measurements Brown et al., 2005

9 3/ Nature of soil spectra Ben Dor et al. (1999)

10 3/ Nature of soil spectra Fe ox OM Fe ox H2O Mineralogy CaC03 H2O OM Diff in albedo due to OM

11 3/ Nature of soil spectra Soil samples in the LUCAS database having 2 % C

12 3/ Nature of soil spectra

13 3/ Nature of soil spectra Spectroscopic models relying on cross-correlation with other properties will be highly unstable!

14 4/ A problem of sampling density? Example for the LUCAS database: 250 spectral nearset neighbours of a sample located in France

15 4/ A problem of sampling density? Soil spectral library of the Walloon region (Genot et al 2011) : Selecting neighbours with sufficient correlation Genot et al. (2011)

16 4/ A problem of sampling density? Reported root mean square error (RMSE) of vis NIR based predictions against the standard deviation (of the soil attribute) in the validation sets.

17 Part I : summary Factors affecting model performance of large spectral databases: Variations in measuring conditions within library Variations in soil analytical methods Complexity of the soil spectra-soil properties relationship at large scale Low representativity of the soil diversity All these factors can be better controlled for small scale databases! Is there any solutions? (1) better protocols: garbage in, garbage out! (2) appropriate data mining tools (3) let s share!

18 PART II: Modeling a complex soil spectral Library Predicting OC content in the LUCAS dataset

19 Modeling a complex Soil Spectral Library Collected in the framework of the Land Use/Cover Area frame Statistical Survey under the supervision of the JRC to assess the state of soil across Europe. Current status: 23 European countries Metadata: Clay, silt, sand, OC, ph, CEC, CaCO 3 content Geographical coordinates, land use, etc ~20,000 spectral readings in the vis-nir region ( nm) one of the largest, most diverse and complete soil spectral library

20 Modeling a complex Soil Spectral Library Spectrometer: FOSS XDS Rapid Content Analyzer

21 Modeling a complex Soil Spectral Library Description of the soil properties

22 Modeling a complex Soil Spectral Library Loadings

23 Modeling a complex Soil Spectral Library Scores of the three first PC s in Europe

24 Modeling a complex Soil Spectral Library Model performance as a function of the multivariate calibration method

25 Modeling a complex Soil Spectral Library Model performance as a function of the variables used

26 Effect of sand content Modeling a complex Soil Spectral Library

27 Modeling a complex Soil Spectral Library Here, we used measured sand content to improve prediction accuracy. When not available, legacy data or digital soil maps could be used to assign sand content ranges to the soil samples Texture Land use Mineralogy

28 Modeling a complex Soil Spectral Library Predicted-observed plot

29 Modeling a complex Soil Spectral Library RMSEP 2 bias 2 + SEP b 2

30 Modeling a complex Soil Spectral Library Local regression approach n Xr, Yr = {xr i, yr i } i=1 m Xp, Yp = {xp i, yp i } i=1 (spectral library) (samples to predict) Pseudo-code of a local regression algorithm: 1. for each sample to predict p i i = 1,2,..., m do 2. Compute d i, the distance vector between xp i and Xr 3. Find the most similar samples in Xr as the k ones minimizing d i, i.e the k-nearest neighbours 4. [Optional] Assign weights to the k nearest neighbours 5. Fit a multivariate model with the k nearest neighbours 6. Choose the optimal model parameters for prediction of p i, e.g. appropriate number of Latent Variable (LV) for a PLSR model 7. Predict sample p i and compute squared error 8. end

31 Modeling a complex Soil Spectral Library Effect of combining spectral + covariate distance without sand.

32 Modeling a complex Soil Spectral Library Effect of combining spectral + covariate distance with sand.

33 Modeling a complex Soil Spectral Library Model performance as a function of predictors

34 The relationship between spectra and soil properties is scale-dependent and inherently local Metadata are crucial to partition the data into sub-groups where the relationship between spectra and soil properties are less complex. Level of accuracy of the models may be acceptable for a rough screening of the soil properties but still insufficient for most applications and in particular the spatial or temporal monitoring of SOC. Possible ways for improvement: Data mining tools should be developed that are capable of identifying local patterns of spectral variations with the help of readily available covariates linked with pedogenetic factors such as mineralogy, climate and land cover. Local modeling approach Increase sampling density? Part II: Summary

35 Contact details Antoine Stevens Postdoctoral Researcher Georges Lemaître Centre for Earth and Climate Research Earth and Life Institute UCLouvain Place Pasteur, Louvain-La-Neuve, Belgium antoine.stevens@uclouvain.be