Abstract: Abstract: Traditional chemical analysis for the determination of leaf phosphorus content is time consuming, expensive and labor-intensive. Hyperspectral remote sensing has the potential to rapidly and accurately predict the element concentrations or biochemical composition in foliage. Thus, the aim of this study was to test the utility of hyperspectral sensitive bands in combination with back propagation artificial neural network to estimate leaf phosphorus contents for rubber tree seedlings. A sand culture experiment was carried out to grow rubber tree seedlings. These rubber tree seedlings were cultivated with Hoagland's nutrient solutions. Hoagland's nutrient solutions were set at five levels of phosphorus concentration. They were 31 mg/kg (P4), 23.25 mg/kg (P3), 15.5 mg/kg (P2), 7.75 mg/kg (P1), and 0 mg/kg (P0), respectively. Each level was replicated five times, and each replicate included five seedlings. Leaves of rubber tree seedlings were sampled at 85, 100, 115, and 133 days after start of the culture. At each sampling time, two matured leaves were collected for each tree. Ten leaves were obtained from each replicate and were combined as a sample. Finally, a total of 100 samples were collected. At each sampling date, leaf samples were sent to laboratory soon after the collection, and their leaf hyperspectral reflectance was measured by ASD FieldSpec 3 spectrometer. Phosphorus contents of the corresponding leaves were also analyzed using the conventional chemical analysis method. A second order low-pass digital Butterworth filter with normalized cutoff frequency 0.5 was used to the original spectra to filter out the noise signals. Next, differential technology was applied to the denoising of leaf hyperspectral reflectance in order to extract the first and the second derivative spectra, respectively. After that, correlation analysis was conducted to calculate correlation coefficients between rubber tree seedling leaf phosphorus contents and leaf hyperspectral reflectance as well as its first and second derivative spectra. Finally, hyperspectral sensitive bands were selected based on the results of the correlation analysis. These selected hyperspectral sensitive bands were used as input variables, and multiple linear regression (MLR), partial least-squares regression (PLSR), as well as back propagation artificial neural network (BPANN) model were used to estimate the leaf phosphorus contents for rubber tree seedlings, respectively. Results indicated that wavelength of 555 and 722 nm of the original hyperspectral reflectance, wavelength of 674, 710, 855, 1091, 1197, 1275, 1718, 2181, and 2228 nm of the first derivative spectra, and wavelength of 816, 890, 1339, 1357, and 2201 nm of the second derivative spectra were the hyperspectral sensitive bands. BPANN model with the selected 16 hyperspectral sensitive bands had the best predicted results. Correlation coefficients (r) between predicted leaf phosphorus contents and measured leaf phosphorus contents were 0.964 and 0.967 for training dataset and test dataset, respectively. Values of root mean squared errors (RMSE) were 0.01039 and 0.00856 for training dataset and test dataset, respectively. Values of relative error (RE) were 4.51% and 4.08% for train dataset and test dataset, respectively, and values of ratio of performance to deviation (RPD) were 3.71 and 3.23 for training dataset and test dataset, respectively. The results demonstrated that hyperspectral remote sensing could be used to rapidly, and accurately predict the leaf phosphorus contents for rubber seedlings.