第36卷，第5期 2016年5月
光谱学与光谱分析 Spectroscopy and Spectral Analysis
Vol. 36,No. 5+pp1615-1622
May,2016
HyperspectralModelsandForcasting ofPhysico-Chemical
PropertiesforSalinizedSoilsinNorthwestChina
XIAO Zhen-zhen',LI Yil.2*,FENG Hao2.3
1. College of Water Resources and Architecture Engineering, Northwest A&.F University, Yangling 712100, China 2. Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&.F University, Yangling 712100, China
3. National Engineering Research Center for Water Saving Irrigation at Yangling, Northwest A&.F University, Yangling712100, China
Abstract
Hyperspectral remote sensing data have special advantages, i. e. , they have high spectral resolution
and strong band continuity, and a great number of spectral information could be widely used in soil properties monitoring research. Using hyperspectral remote sensing technique to analyze saline soil properties makes great significance for the crop growth in the irrigation district and agricultural sustainable development. 221 soil samples were collected from Manasi River Basin to measure soil electrical conductivity (EC), soil organic matter (SOM) and 3 kinds of cation concentrations including Na, Ca*+ and Mg?+, which were used to obtain sodium adsorption ration value (SAR). The soil hyperspectral curves were also measured. EC, SOM and SAR models were established based on the six spectral-related indices, including raw reflectance (R) , standard nor mal variable (SNV), normalized difference vegetation index (NDVI), logarithm of the reciprocal (LR), the first derivative reflectance (FDR) and continuum-removal reflectance (CR) by the stepwise linear regression method. The results showed that, compared to the other five models, the model of log (EC) ~R had the high est accuracy with r value of 0. 782 and RMSE value of 0. 256. The model of SOM vs. NDVI had the highest accuracy with r value of 0. 670 and RMSE value of 5. 352. The model of SAR vs. FDR had the highest accura cy with r value of 0. 647 and RMSE value of 1. 932. As to the model accuracy of the studied soil physico-chem-ical properties, the log(Ec) model was the most effective one, followed by the SOM model, the SAR model was the most inaccurate. The sensitive wavelengths for EC, SOM and SAR distributed in 395 ~1 801 nm, 352 ~1 144 nm and 394 ~1 011 nm, respectively. Since soil physico-chemical properties were highly spatially vari-able, there were large differences for the model establishment and validation of the soil properties. This re search could be a reference of hyperspectral remote sensing monitoring of salinized soils.
KeywordsSoil electrical conductivity; Soil organic matter; Sodium adsorption ration; Hyperspectral model
中图分类号：S127
Introduction
文献标识码：A
D0I: 10. 3964/i. issn. 10000593 (2016)05161508
production and restricts agricultural sustainable develop ment-J.In Xinjiang Uygur Autonomous Region, northwest of China, the area of arable saline soil land account for 31. 1%
Soil salinization is a typical phenomenon of land degrada-tion. It reduces soil permeability, constrains crop growth and Received: 2015-03-06; aceepted: 2015-07-10
of the total arable land:2). Among the related indices to deno ting soil salinization, soil electrical conductivity (EC) is an important soil quality assessment parameter[3-J, Besides, so
Foundation item: National High Technology Research and Development Program of China (SS2013AA100904) , Natural Science Foundation of
China (51579213), the China 111 Project (B12007), and China Scholarship Council for Studying Abroad (201506305014)
Biography: XIAO Zhen-zhen, (1992—), Master Degree in College Water Resources and Architecture Engineering, Northwest A&.F
University
e-mail ; xiaozz0212@163. com
* Corresponding author
e-mail : liyikitty@126. com