王金亮导师研究小组的论文在SCI期刊Forests发表

    近日，以实验室2016级硕士研究生陈云为第一作者、王金亮教授为通讯作者撰写的“Hyperspectral Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China”论文在SCI期刊Forests上发表，论文以滇西北森林土壤为研究对象，利用高光谱技术，建立了土壤有机质含量高光谱估算模型。该论文得到了王金亮教授主持的国家自然科学基金、云南省中青年学术技术带头人、云南省高校科技创新团队支持计划等的资助。

标题：Hyperspectral  Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China

作者：Yun Chen, Jinliang Wang*, Guangjie Liu, Yanlin Yang, Zhiyuan Liu, and Huan Deng

通讯作者： Jinliang Wang, jlwang@ynnu.edu.cn

作者单位：1. College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China; 2. Key Laboratory of Resources and Environmental Remote Sensing, Universities in Yunnan, Kunming 650500, China, 3. Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province , Kunming 650500, China

出版物：Forests

摘要：Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM content efficiently and accurately. This study researched the best hyperspectral estimation model for SOM content in Shangri-La forest soil. The spectral reflectance of soils with sizes of 2 mm, 1 mm, 0.50 mm, and 0.25 mm were measured indoors. After smoothing and de-noising, the reciprocal reflectance (RR), logarithmic reflectance (LR), first-derivative reflectance (FR), reciprocal first-derivative reflectance (RFR), logarithmic first-derivative reflectance (LFR), and mathematical transformations of the original spectral reflectance (REF) were carried out to analyze the relevance of spectral reflectance and SOM content and extract the characteristic bands. Finally the simple linear regression (SLR), multiple stepwise linear regression (SMLR), and partial least squares regression (PLSR) models for SOM content estimation were established. The results showed that: (1) With the decrease of soil particle size, the spectral reflectance increased. The smaller the soil particle sizes, the more obvious was the increase in spectral reflectance. (2) The sensitive bands of SOM were mainly in the 580–690 nm range (correlation coefficient (R) >0.6, P-value (p) <0.01), and the spectral information of SOM could be significantly enhanced by first-order differential transformation. (3) Comparing the three models, PLSR had better estimation ability than SMLR and SLR. The precision of the 0.25 mm soil particle size and the LFR index in the PLSR estimation model of SOM content was the best (coefficient of determination of validation (R_v²) = 0.91, root mean square error of validation (RMSE_v) = 13.41, the ratio of percent deviation (RPD) = 3.33). The results provide a basis for monitoring SOM content rapidly in the forests of Northwest Yunnan, and provide a reference for forest SOM estimation in other areas.

关键字：soil organic matter; hyperspectral; soil particle size; Northwest Yunnan