%0 Journal Article
%T Analysis on Possibilities of Multi-spectral Data for Quantitative Retrieving Soil Nutrition Element Contents<br>多光谱数据定量反演土壤营养元素含量可行性分析
%A WANG Lu
%A LIN Qi-zhong
%A JIA Dong
%A SHI Huo-sheng
%A HUANG Xiu-hua
%A <br>王璐
%A 蔺启忠
%A 贾东
%A 石火生
%A 黄秀华
%J 环境科学
%D 2007
%I 
%X Models for predicting soil nutrition elements content were established by regression methods. The data source was simulated multi-spectral data from reflectance spectra measured under laboratory condition. First, the reflectance spectra were resampled to the corresponding bands of multi-spectral sensors (TM and ASTER) according to their reflectance response functions. Then, the experiential models were established between measured spectra, simulated reflectance spectra (TM and ASTER) and soil nutrition element contents by stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) methods. Precision of these models was test by validation soil samples. Compared with models established by measured spectra, precision of simulated spectra models is slightly affected by spectral resolution. Simulated spectra models give good results for nitrogen (R=0.89), phosphor (R=0.79), and potassium (R=0.68). The selected band range of SMLR models for soil N, P, and K are 2000 to 2300nm, 1650 to 1800nm and 600 to 800nm respectively. The coefficients of PLSR models show that near infrared (NIR) is more sensitive to nitrogen and phosphor than visible (VIS) band, while VIS is better for potassium. Good prediction performance indicates theoretically the future possibilities of multivariate calibration for soil nutrition element concentrations by multi-spectral remotely sensed images and bands character of sensors should be considered well because different element has different response.
%K nutrition element
%K soil
%K multi-spectra<br>营养元素
%K 土壤
%K 多光谱
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=3FF3ABA7486768130C3FF830376F43B398E0C97F0FF2DD53&cid=A7CA601309F5FED03C078BCE383971DC&jid=64CD0AA99DD39F69401C615B85F123EF&aid=15BD448442B396A3E59C644FA97D1DCA&yid=A732AF04DDA03BB3&vid=D3E34374A0D77D7F&iid=5D311CA918CA9A03&sid=316EE604774E1650&eid=A84288F223082930&journal_id=0250-3301&journal_name=环境科学&referenced_num=0&reference_num=25