CAJ | 학술논문

地面高光谱遥感光谱分辨率高，能详细地反映地物波谱特征；多光谱遥感时域宽，覆盖范围广，对较大时空区域的地物特征反演具有更大的优势。探求以不同反射率指标的土壤有机质含量预测模型，及其敏感波段，可以结合两种光谱数据的优点，为研究土壤有机质含量的时空变化规律提供新途径。本研究选取黑河上游223个土壤样品测定其有机质含量和高光谱曲线，应用原始光谱曲线反射率（λ）、倒数（REC ）、倒数之对数（LR）、归一化（CR）和一阶微分（FRD）五种指标，采用逐步线性回归分析方法建立预测模型。通过统计检验，结果表明，以反射率指标为自变量的模型预测效果最佳，其相关系数（r）和均方根误差（RMSE）分别为：0.863和4.79。最优模型中得出的敏感波段有TM1内的474 nm、TM3内的636 nm和 TM5内的1632 nm。研究结果可为使用T M遥感数据反演黑河上游土壤有机质含量提供参考。
지면고광보요감광보분변솔고，능상세지반영지물파보특정；다광보요감시역관，복개범위엄，대교대시공구역적지물특정반연구유경대적우세。탐구이불동반사솔지표적토양유궤질함량예측모형，급기민감파단，가이결합량충광보수거적우점，위연구토양유궤질함량적시공변화규률제공신도경。본연구선취흑하상유223개토양양품측정기유궤질함량화고광보곡선，응용원시광보곡선반사솔（λ）、도수（REC ）、도수지대수（LR）、귀일화（CR）화일계미분（FRD）오충지표，채용축보선성회귀분석방법건립예측모형。통과통계검험，결과표명，이반사솔지표위자변량적모형예측효과최가，기상관계수（r）화균방근오차（RMSE）분별위：0.863화4.79。최우모형중득출적민감파단유TM1내적474 nm、TM3내적636 nm화 TM5내적1632 nm。연구결과가위사용T M요감수거반연흑하상유토양유궤질함량제공삼고。
Benefiting from the high spectral resolution ,ground hyperspectral remote sensing technology can express the ground surface feature in detail ,meanwhile ,multispectral remote sensing has more advantages in studying the features in a large space-time region ,because of its long time-series images and wide coverage .Investigating the prediction models between the soil or-ganic matter (SOM ) content and the hyperspectral data and the sensitive bands based on different indices mathematically obtained from reflectance could combine the advantages of both kinds of spectral data ,and provide a new method to search the spatio-tem-poral characteristics of SOM .Two hundred twenty three soil samples were chosen from the upper reaches of Heihe Basin to measure the SOM content and hyperspectral curve .Taking 181 of them ,the stepwise linear regression methods were used to es-tablish models between the SOM and five indices ,including reflectance (λ) ,reciprocal (REC) ,logarithm of the reciprocal (LR) ,continuum-removal (CR) and the first derivative reflectance (FDR) .After then ,the left 42 samples were used for model validation :firstly ,the best model of the same index was chosen by the values of Pearson correlation coefficient (r) and Root mean squared error (RMSE) between the measured value and predicted value ;secondly ,the best models of different indices were compared .As a result ,the model built by reflectance has a better estimation of SOM with the r :0 .863 and RMSE:4.79 .And the sensitive bands of the reflectance model contain 474 nm during TM1 ,636 nm during TM3 and 1 632 nm during TM5 .This result could be a reference for the retrieval of SOM content of the upper reaches by using the TM remote sensing data .