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Journal of Arid Land
Journal of Arid Land  2021, Vol. 13 Issue (12): 1287-1298    DOI: 10.1007/s40333-021-0110-5     CSTR: 32276.14.s40333-021-0110-5
Geography, geology and natural resources in Central Asia (Guest Editorial Board Member:Prof. Dr. XIAO Wenjiao)     
Estimation of rock Fe content based on hyperspectral indices
WANG Jinlin1,2,3,4, WANG Wei1,2,3,4,*(), CHENG Yinyi1,2,3,4, ZHANG Zhixin1,2,3,4, WANG Shanshan1,2,3,4, ZHOU Kefa1,2,3,4, LI Pingheng5
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
3Xinjiang Research Centre for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China
4University of Chinese Academy of Sciences, Beijing 100049, China
5Zhejiang A & F University, Hangzhou 311300, China
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Abstract  

Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines. However, the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies. The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area. Specifically, we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements (Dataset I) and field measurements (Dataset II). In situ field measurements were acquired to verify the laboratory measurements. Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples. In addition, we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples. The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces. It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content. Based on sample data analysis, the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content, with R2 values of 0.45 and 0.40 corresponding to datasets I and II, respectively. Hence, the modified normalized difference index (the wavelengths of 2220, 2290, and 2370 nm) identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas. The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.

Key words： bare rocks      Fe content      reflectance      spectral indices      modified normalized difference index      Saur Mountain     
Received: 30 October 2020      Published: 31 December 2021
Corresponding Authors: *WANG Wei (wangw1114@ms.xjb.ac.cn)
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WANG Jinlin
WANG Wei
CHENG Yinyi
ZHANG Zhixin
WANG Shanshan
ZHOU Kefa
LI Pingheng
Cite this article:

WANG Jinlin, WANG Wei, CHENG Yinyi, ZHANG Zhixin, WANG Shanshan, ZHOU Kefa, LI Pingheng. Estimation of rock Fe content based on hyperspectral indices. Journal of Arid Land, 2021, 13(12): 1287-1298.

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http://jal.xjegi.com/10.1007/s40333-021-0110-5     OR     http://jal.xjegi.com/Y2021/V13/I12/1287

Index Definition Formula Reference
R Reflectance ρλ1
D Reflectance difference index ρλ1-ρλ2 Le Maire et al. (2004)
SR Simple ratio ρλ2/ρλ1 Le Maire et al. (2008)
ND Normalized difference index (ρλ1-ρλ2)/(ρλ1+ρλ2) Wang et al. (2011)
mSR Modified simple ratio (ρλ1-ρλ2)/(ρλ1+ρλ2-2ρλ3) Sims and Gamon (2002)
mND Modified normalized difference index (ρλ1-ρλ3)/(ρλ2-ρλ3) Sims and Gamon (2002)
Table 1 Descriptions of the generic types of indices
Fig. 1 Fe content of 81 rock samples. (a), spatial variation of Fe content of 81 rock samples for the three plots; (b), number of samples in each level. Plot 1, Plot 2, and Plot 3 represent the spatial aggregation distribution of 81 rock samples. Level 1, 0.00%-1.00%; Level 2, 1.00%-2.00%; Level 3, 2.00%-3.00%; Level 4, 3.00%-4.00%, Level 5, 4.00%-5.00%; Level 6, >5.00%.
Fig. 2 Reflectance properties of rock samples. (a), mean reflectance of the four groups, including Reflectance- Laboratory-Old, Reflectance-Laboratory-New, Reflectance-Field-Old, and Reflectance-Field-New; (b), mean reflectance of the six Fe content levels; (c), continuum removal reflectance of the six Fe content levels within the wavelengths of 2100-2500 nm.
Rock type Index Wavelength (nm) Fe=ea+b×index Dataset I Dataset II
λ1 λ2 λ3 a b R2 RMSE Relative
RMSE (%)		 R2 RMSE Relative
RMSE (%)
Weather rock surface R 2210 - - 0.43 0.01 0.03 1.64 72.57 0.05 1.63 72.13
D 2220 2240 - 0.48 0.24 0.30* 1.42 62.84 0.25* 1.54 68.15
SR 2220 2250 - 3.64 4.06 0.35* 1.30 57.53 0.32* 1.43 63.28
ND 2220 2250 - 0.35 6.86 0.36* 1.31 57.97 0.33* 1.43 63.28
mSR 2210 2250 2360 0.47 2.20 0.38* 1.27 56.20 0.34* 1.39 61.51
mND 2220 2290 2370 0.21 0.79 0.40* 1.26 55.76 0.38* 1.37 60.62
Fresh rock
surface		 R 2210 - - 0.51 0.01 0.05 1.64 72.57 0.06 1.63 72.13
D 2220 2240 - 0.68 0.27 0.29* 1.47 65.05 0.25* 1.55 68.59
SR 2220 2250 - 4.55 5.07 0.40* 1.30 57.53 0.37* 1.36 60.18
ND 2220 2250 - 0.64 7.24 0.41* 1.30 57.53 0.38* 1.32 58.41
mSR 2210 2250 2360 0.54 2.16 0.42* 1.26 55.76 0.38* 1.37 60.62
mND 2220 2290 2370 0.34 0.62 0.45* 1.24 54.87 0.39* 1.34 59.30
Table 2 Results of the general types of indices calibrated with the Dataset I and validated to the Dataset II for the estimation of rock Fe content
Fig. 3 Comparison between the measured and estimated Fe contents based on the modified normalized difference index. (a), fresh rock surfaces with laboratory reflectance; (b), fresh rock surfaces with field reflectance; (c), weathered rock surfaces with laboratory reflectance; (d), weathered rock surfaces with field reflectance.
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