中文摘要：

运用室内光谱模拟多光谱数据，采用回归分析方法建立土壤营养元素含量预测模型，并进行验证．首先，根据光谱响应函数将实验室光谱数据重采样至多光谱传感器（TIM和ASTER）波段；然后，分别利用多元逐步回归（SMLR）和偏最小二乘回归（PLSR）方法，建立建模土壤样本实测光谱以及模拟光谱（TIM和ASTER）与土壤营养元素含量间的经验模型；最后，利用检验土壤样本进行模型精度验证．与实测光谱模型相比，模拟光谱模型对土壤营养元素含量的预测精度受光谱分辨率的影响并不大．模拟光谱模型对N、P、K元素含量预测精度最高分别为0．89、0．79和0．67．土壤N、P、K元素含量SMLR模型的入选波段分别位于2000-2300nm、1650～1800nm和600～800nm波长附近范围内；土壤N、P、K元素含量PLSR模型的系数表明，近红外（NIR）波段对总氮和总磷元素含量比可见光（VIS）波段敏感，而VIS对K元素含量预测的贡献更大．利用多光谱数据进行土壤营养元素含量的估测具有理论上的可行性；由于不同元素对不同光谱波段的响应不同，在选择多光谱遥感数据时要充分考虑传感器的波段特征．

英文摘要：

Models for predicting soil nutrition elements content were established by regression methods. The data source was simulated multispectral 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 tested 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 2 000 to 2 300 nm, 1 650 to 1 800 nm and 600 to 800 am respectively. The coefficients of PLSR models show that near infrared （NIR） band 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.