the application of spectroscopy in soil science qianlong wang zhejiang university, china email:...
TRANSCRIPT
The Application of Spectroscopy in Soil Science
Qianlong WangZhejiang University, China
Email: [email protected]
June 17,2014, UIUC
I. Introduction
II. Soil Spectral Database
III. Discussion & Conclusion
OUTLINE
Soilproperties
organic matter total nitrogen
organic carbon cation exchange capacity
pH P、 K
……
I. Introduction
using Vis-NIR diffuse reflectance spectroscopy to predict soil total nitrogen
I. Introduction
I. Introduction
Motivation
“ON-THE-GO”
Digital mapping of soil organic carbon
I. IntroductionStoner and Baumgradner,1981
five classical reflectance spectra curve forms
I. Introduction
I. IntroductionThe soil scientists and researchers research results from around the global
I. Introduction
II. Soil Spectral Database
III. Discussion & Conclusion
OUTLINE
II. Soil Spectral DatabaseBrown et al.,2006Viscarra Rossel et al.,2008Goge et al. ,2012Zhou Shi et al.,2013
II. Soil Spectral DatabaseSoil samples distribution
looks like
a rooste
r
1661 soil samples representing 17 soil types from 13 provinces of China
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.
II. Soil Spectral Databasemechanism
II. Soil Spectral Database
Model building for data mining
partial least squares regression(PLSR)
fuzzy k-mean(FKM)
local weighted regression(LWR)
II. Soil Spectral Database
0.0 0.1 0.2 0.3 0.40.0
0.1
0.2
0.3
0.4
RP1
2 = 0.64
RMSEP1
= 0.059
RPDP1
= 1.4
RPIQP1
= 1.8
n = 104
Pre
dic
ted
(%)
Measured (%)
1:1 line
0.0 0.1 0.2 0.3 0.40.0
0.1
0.2
0.3
0.4
RP3
2 = 0.82
RMSEP3
= 0.035
RPDP3
= 2.4
RPIQP3
= 3.0
n = 104
Pre
dic
ted
(%)
Measured (%)
1:1 line
0.0 0.1 0.2 0.3 0.40.0
0.1
0.2
0.3
0.4
RP2
2 = 0.76
RMSEP2
= 0.032
RPDP2
= 2.1
RPIQP2
= 2.7
n = 104
Pre
dict
ed (
%)
Measured (%)
1:1 line
PLSR FKM
LWR
II. Soil Spectral Database
Model building for data mining
Modelprediction accuracyR2 RMSE RPD
PLSR 0.64 0.059 1.4FKM 0.82 0.035 2.4LWR 0.76 0.032 2.1
the determination coefficient (R2), the root-mean-square error (RMSE) and the ratio of performance to deviation (RPD)
I. Introduction
II. Soil Spectral Database
III. Discussion & Conclusion
OUTLINE
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.
Thanks!