Hyper-spectral characteristics and classiifcation of farmland soil in northeast of China

The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter (SOM) based on pre-classiifcation. This experiment was conducted under a controlable environment, and differen...

Full description

Saved in:
Bibliographic Details
Published in农业科学学报(英文版) no. 12; pp. 2521 - 2528
Main Authors LU Yan-li, BAI You-lu, YANG Li-ping, WANG Lei, WANG Yi-lun, NI Lu, ZHOU Li-ping
Format Journal Article
LanguageEnglish
Published Key Laboratory of Crop Nutrition and Fertilization, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China 2015
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:The physical and chemical heterogeneities of soils make the soil spectral different and complicated, and it is valuable to increase the accuracy of prediction models for soil organic matter (SOM) based on pre-classiifcation. This experiment was conducted under a controlable environment, and different soil samples from northeast of China were measured using ASD2500 hyperspectral instrument. The results showed that there are different relfectances in different soil types. There are statisticaly signiifcant correlation between SOM and relfectence at 0.05 and 0.01 levels in 550–850 nm, and al soil types get signiifcant at 0.01 level in 650–750 nm. The results indicated that soil types of the northeast can be divided into three categories: The ifrst category shows relatively lfat and low relfectance in the entire band; the second shows that the spectral relfectance curve raises fastest in 460–610 nm band, the sharp increase in the slope, but uneven slope changes; the third category slowly uplifts in the visible band, and its slope in the visible band is obviously higher than the ifrst category. Except for the classiifcation by curve shapes of relfectance, principal component analysis is one more effective method to classify soil types. The ifrst principal component includes 62.13–97.19% of spectral information and it mainly relates to the information in 560–600, 630–690 and 690–760 nm. The second mainly represents spectral information in 1640–1740, 2050–2120 and 2200–2300 nm. The samples with high OM are often in the left, and the others with low OM are in the right of the scatter plot (the ifrst principal component is the horizontal axis and the second is the longitudinal axis). Soil types in northeast of China can be classiifed effectively by those two principles; it is also a valuable reference to other soil in other areas.
ISSN:2095-3119
2352-3425
DOI:10.1016/S2095-3119(15)61232-1