1. The Application of Spectroscopy in Soil Science Qianlong Wang Zhejiang University, China Email: wangqianlong@zju.edu.cn June 17,2014, UIUC

2. I.Introduction II.Soil Spectral Database III.Discussion & Conclusion OUTLINE

3. Soil properties organic matter total nitrogen organic carbon cation exchange capacity pH P、KP、K …… I.Introduction

4. using Vis-NIR diffuse reflectance spectroscopy to predict soil total nitrogen I.Introduction

5. Motivation “ON-THE-GO” Digital mapping of soil organic carbon

6. I.Introduction Stoner and Baumgradner,1981 five classical reflectance spectra curve forms

7. I.Introduction

8. The soil scientists and researchers research results from around the global

9. I.Introduction II.Soil Spectral Database III.Discussion & Conclusion OUTLINE

10. II.Soil Spectral Database Brown et al.,2006 Viscarra Rossel et al.,2008 Goge et al.,2012 Zhou Shi et al.,2013

11. II.Soil Spectral Database Soil samples distribution looks like a rooster 1661 soil samples representing 17 soil types from 13 provinces of China

12. II.Soil Spectral Database mechanism Correlation of soil total nitrogen with the first derivatives of the reflectance at visible (vis), first, second, third overtone (OT) and combination range.

13. II.Soil Spectral Database mechanism

14. II.Soil Spectral Database Model building for data mining partial least squares regression(PLSR) fuzzy k-mean(FKM) local weighted regression(LWR)

15. II.Soil Spectral Database PLSR FKM LWR

16. II.Soil Spectral Database Model building for data mining Model prediction accuracy R2R2 RMSERPD PLSR0.640.0591.4 FKM0.820.0352.4 LWR0.760.0322.1 the determination coefficient (R 2 ), the root-mean-square error (RMSE) and the ratio of performance to deviation (RPD)

17. I.Introduction II.Soil Spectral Database III.Discussion & Conclusion OUTLINE

18. III.Discussion & Conclusion c)The idea of classification or local weighted regression plays a bridge role to improve prediction accuracy by soil reflectance spectral database. b)Because of the complex chemical constituents in soils, no matter what kinds of model, it must have the capability to find the useful information predicting soil properties. a)It’s possible to establish robust and universal models for soil TN prediction using large soil spectral libraries.

19. Thanks!