The use of hyperspectral remote sensing data combined with statistical modeling is currently the main method for hyperspectral estimation of soil organic matter(SOM)content.In order to explore the optimal spectral transformation and spectral resolution for SOM content estimation，the study took soil samples from the black soil area of Heilongjiang province as the research object，and the spectral reflectance of the soil sample were measured by the SR-6500 portable spectrometer in the laboratory.Spectral reflectance was resampled to 12 kinds of spectral resolution such as 1，5，10，20，30，40，50，60，70，80，90 and 100 nm，smoothed by Savitzky-Golay，and subjected to 10 kinds of spectral transformation such as reflectance reciprocal(1/R)，logarithm(logR)，reciprocal logarithm[log(1/R)]，logarithm reciprocal(1/logR)，first-order differential(R′)，reciprocal first-order derivative[(1/R)′]，logarithmic first-order derivative[(logR)′]，reciprocal logarithm first-order derivative[log(1/R)]′ and logarithmic first-order derivative[(1/logR)′].The SOM content prediction model was established by using multiple linear stepwise regression(MLSR)and partial least square regression(PLSR)methods.The results showed:(1)spectral transformation of 1/R and(1/R)′ performed higher and more stable SOM content estimation accuracy with R2val≥0.87 and R2val≥0.90，respectively；(2)spectral resolution of 5 and 10 nm had higher estimating accuracy by MLSR and PLSR under 1/R and (1/R)′ spectral transformation，which were selected as suitable spectral resolutions；(3)the spectral resolution used for SOM content estimation was not as high as possible.Appropriate reduction of spectral resolution and selection of appropriate spectral transformation can not only reduce the workload of data processing，but also improve the SOM content estimation accuracy.