CAJ | 학술논문

高光谱遥感技术作为当前遥感发展的前沿科技，通过电磁波与地物的相互作用，可以定量反演地物的物理化学性质。土壤有机质是重要的土壤养分信息参数，利用高光谱遥感技术快速获取其含量信息可以为精准农业的发展提供必要的数据支撑。然而，由于受到外部参数差异的干扰，导致建模精度降低的同时，还会造成已有模型传递性的“失效”。为了消除湿度差异的干扰，进一步拓展已有模型的适用空间，以江汉平原滨湖地区为例，通过对95个土壤样本进行加湿处理，在实验室自然风干的条件下，量测得到13套不同湿度等级土壤样本的可见—近红外反射光谱数据，建立了各湿度等级下土壤有机质的光谱反演模型，研究水分差异对建模精度的影响；在此基础上，运用Direct Standardization（DS）算法对湿土光谱进行校正，进而探讨该算法在提高模型传递性能方面的潜力。结果表明：基于风干土光谱建立的模型预测精度最高，未经校正的湿土光谱无法通过该模型进行土壤有机质含量预测，预测偏差在－8.34～3.32 g? kg －1，RPD在0.64～2.04；经过DS算法校正后的湿土光谱可以通过该模型进行土壤有机质含量预测，预测偏差降低至0， RPD值提高至7.01。研究表明DS算法能有效降低湿度差异对光谱反演土壤有机质的影响，使土壤有机质光谱反演模型适用于不同水分含量的土壤样本。
고광보요감기술작위당전요감발전적전연과기，통과전자파여지물적상호작용，가이정량반연지물적물이화학성질。토양유궤질시중요적토양양분신식삼수，이용고광보요감기술쾌속획취기함량신식가이위정준농업적발전제공필요적수거지탱。연이，유우수도외부삼수차이적간우，도치건모정도강저적동시，환회조성이유모형전체성적“실효”。위료소제습도차이적간우，진일보탁전이유모형적괄용공간，이강한평원빈호지구위례，통과대95개토양양본진행가습처리，재실험실자연풍간적조건하，량측득도13투불동습도등급토양양본적가견—근홍외반사광보수거，건립료각습도등급하토양유궤질적광보반연모형，연구수분차이대건모정도적영향；재차기출상，운용Direct Standardization（DS）산법대습토광보진행교정，진이탐토해산법재제고모형전체성능방면적잠력。결과표명：기우풍간토광보건립적모형예측정도최고，미경교정적습토광보무법통과해모형진행토양유궤질함량예측，예측편차재－8.34～3.32 g? kg －1，RPD재0.64～2.04；경과DS산법교정후적습토광보가이통과해모형진행토양유궤질함량예측，예측편차강저지0， RPD치제고지7.01。연구표명DS산법능유효강저습도차이대광보반연토양유궤질적영향，사토양유궤질광보반연모형괄용우불동수분함량적토양양본。
Hyperspectral remote sensing ,known as the state‐of‐the‐art technology in the field of remote sensing ,can be used to retrieve physical and chemical properties of surface objects based on the interactions between electromagnetic waves and the ob‐jects .Soil organic matter (SOM ) is one of the most important parameters used in the assessment of soil fertility .Quick estima‐tion of SOM with hyperspectral remote sensing technique can provide essential soil data to support the development of precision agriculture .The presence of external parameters ,however ,may affect the modeling precision ,and further handicap the trans‐ferability of existing model .With the aim to study the effects of soil moisture on the Vis/NIR estimation of soil organic matter , and the capacity of direct standardization (DS)algorithm in the calibration transfer ,95 soil samples collected in the Jianghan plain were rewetted and air‐dried .Reflectance of these samplesat 13 moisture levels was measured .Results show that the model cali‐brated using air‐dried samples has the highest prediction accuracy .This model ,however ,was not suitable for SOM prediction of the rewetted samples .Prediction bias and RPD improved from -8.34~3.32 g?kg -1 and 0.64~2.04 to 0 and 7.01 ,when DS algorithm was applied to the spectra of the rewetted samples .DS algorithm has been proven to be effective in removing the effects of soil moisture on the Vis/NIR estimation of SOM ,ensuring a transferrable model for SOM prediction with soil samples at different moisture levels .