TY - JOUR
T1 - High-Spectral Inversion Based on Characteristic Band in the Three-River Headwater Region of Soil Total-Nitrogen
AU - Lin, Hui
AU - Pu, Ruiliang
AU - Wang, Lijuan
AU - Li, Changchun
AU - Shao, Chongying
PY - 2016/1/1
Y1 - 2016/1/1
N2 - In this paper, in 2012 and 2013 two of the Three-River Headwaters Region of soil total-nitrogen, data combined ASD FieldSpec 4 made by America spectroradiometer measured spectral reflectance of soil sample chamber data model MSLR and ANN methods modeling. The spectral data is mainly composed of original spectral reflectance(REF) through nine point weighted moving average obtaining four forms of data: first derivative reflectance(FDR), second derivative reflectance(SDR), Log(1/R), band depth(BD), gaining the model input variable of characteristic band. The sample was divided into total samples and 5 types of soil by analyzing spectral reflectance of typical soil of the Tibet Plateau in the three-river headwaters region, which is served as a reference for recognizing the type of soil. Comparing the model of MSLR and ANN, we can conclude that the precision of modeling with all band (350~2500nm) and verification is wider than characteristic bands (500~900nm, 1400~1500nm, 1900~2000nm and 2200~2300nm), which has better stability and efficiency and the precision of nonlinear model of ANN is obviously better than MSLR. Modeling with total sample has better stability and precision of inversion for that modeling with overall sample is able to estimate roughly total nitrogen composition of soil, which shows a stable model and situation of verification.
AB - In this paper, in 2012 and 2013 two of the Three-River Headwaters Region of soil total-nitrogen, data combined ASD FieldSpec 4 made by America spectroradiometer measured spectral reflectance of soil sample chamber data model MSLR and ANN methods modeling. The spectral data is mainly composed of original spectral reflectance(REF) through nine point weighted moving average obtaining four forms of data: first derivative reflectance(FDR), second derivative reflectance(SDR), Log(1/R), band depth(BD), gaining the model input variable of characteristic band. The sample was divided into total samples and 5 types of soil by analyzing spectral reflectance of typical soil of the Tibet Plateau in the three-river headwaters region, which is served as a reference for recognizing the type of soil. Comparing the model of MSLR and ANN, we can conclude that the precision of modeling with all band (350~2500nm) and verification is wider than characteristic bands (500~900nm, 1400~1500nm, 1900~2000nm and 2200~2300nm), which has better stability and efficiency and the precision of nonlinear model of ANN is obviously better than MSLR. Modeling with total sample has better stability and precision of inversion for that modeling with overall sample is able to estimate roughly total nitrogen composition of soil, which shows a stable model and situation of verification.
KW - High-spectral inversion
KW - types of soil composition
KW - transformation
KW - MSLR model
KW - ANN model
UR - https://digitalcommons.usf.edu/geo_facpub/1364
UR - http://ijssst.info/Vol-17/No-25/cover-17-25.htm
U2 - 10.5013/IJSSST.a.17.25.15
DO - 10.5013/IJSSST.a.17.25.15
M3 - Article
VL - 17
JO - International Journal of Simulation: Systems, Science Technology
JF - International Journal of Simulation: Systems, Science Technology
ER -