Abstract: A local model has been widely used to determine the dynamic relationship between the spectra and leaf phosphorus concentration (LPC) of rubber trees. Some local samples can be assumed as the stationary A local model has been widely used to determine the dynamic relationship between the spectra and Leaf Phosphorus Concentration (LPC) of rubber trees. Some local samples can be assumed as the stationary relationship of LPC-spectra. A similar LPC-spectra can be closely related to the local samples, where the key points can be normally evaluated for the local model. However, the current searching approaches of local samples cannot consider the environmental differences of rubber tree leaf using only spectral similarity. Some leaf samples under the different conditions from the samples to be estimated can be selected to construct the hyperspectral estimation model, resulting in low accuracy of the model prediction. In this study, a new Local Sample Searching using Environmental Similarity and Spectral Similarity (LSS-ESSS) was proposed to evaluate the LPC of rubber trees. Two steps were divided during searching. Specifically, the leaf samples were first classified as different categories, where the environmental factors were taken as group variables. Then, the local sample searching was conducted in the same dataset with the same category as the sample to be estimated. A case study was applied to verify the model in the Hainan Island of China, where there were large areas of rubber tree forests. A field sampling test was conducted three times in the development periods of rubber tree leaf (the period of putting forth buds and leaves from April to June; the period of leaf maturity from July to September; and the period of leaf senescence from October to December). The samples of rubber tree leaf were collected from nine predefined sites in each period. The hyperspectral estimation models in each period were then employed to predict the LPC of rubber trees. The prediction accuracies of the models were compared in the three periods using the local sample searching. The collected leaf samples in each period were randomly divided into the training dataset and test dataset five times, in order to evaluate the stability and reliability of the model. An analysis of variance was then used to determine the significant differences in the prediction accuracy of the models. Results showed that the prediction accuracies of the LSS-ESSS models were much higher than before, indicating the significant differences at P < 0.05 level in the period of leaf maturity and senescence. Consequently, the environmental samples of rubber tree leaves can greatly contribute to improving the prediction performance of the model during local sample searching.