Abstract: More reasonable soil management zones can be made using weighted environmental factors as cluster variables. Weights of the environmental factors are generally acquired from the relation between crop yields and environmental variables. However, it would be impossible to obtain the weights in the case that no relation exists between crop yields and environmental variables or there are no crop yields available. Fortunately, the normalized difference vegetation index (NDVI) can be expected to reflect the soil condition, which was significantly influenced by the environmental variables. In this study, an accurate and efficient approach was developed to obtain the weights of environmental variables using NDVI. The relation model between NDVI and environmental variables was then established in this case. Three steps consisted of this approach. Firstly, the key environmental variables were selected to significantly impose the influence on NDVI. The relation model was then established between NDVI and the selected key environmental variables. Finally, the weights of environmental variables were acquired from the established relation model. A case study was applied to test the effectiveness of the approach. Specifically, the weights of environmental variables were acquired for a rubber plantation in Danzhou County, Hainan Province, China. Among them, the candidates were taken as the 10 terrain attributes, 19 bioclimatic variables, and 5 parent materials types. Furthermore, 15 key environmental variables were determined to construct the relationship model with the NDVI using the random forests (RF). The importance index was extracted from the fitted relation model, and then calculated the weight of each key environmental variable. These acquired weights were also used to weigh the environmental variables, and then to serve as the input variables for the K means clustering. Finally, six groups of soil management zones were generated for the rubber plantation. A total of 1 296 top soil (0-20 cm) samples were employed to verify the soil management zones. Results indicated that the management zones better distinguished the abundance and deficiency levels of different soil nutrients (soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK). Zone 1 presented the high AP content, the normal range of pH value and SOM content, as well as the TN and AK in deficiency. Zone 2 shared the high AP content, pH value suitable for the rubber tree growth, but the SOM, TN, and AK in deficiency. Zone 3 demonstrated a very high AP content anda pH value within the normal range. Nevertheless, the contents of SOM, TN, and AK were all lower than the normal range in Zone 3. More importantly, the contents of SOM, TN, and AK in Zone 4 were similar to those in Zone 3. However, the values of pH and AP were significantly lower than that in Zone 3. In Zone 5, the values of pH and AK were the highest in the normal range among the six zones, while the values of SOM and TN were the lowest in deficiency. In Zone 6, the soil TN content wasthe highest among the six zones within the normal range. The pH value and AP were also within the normal range, but the APcontent was close to the lower limit of the normal range, indicating the insufficient content of SOM and AK. At the same time, the mean differences were all significant at the 0.05 level in 15 selected environmental variables among different management zones. Consequently, the soil management zones further verified the effectiveness of the approach, where the weights of environmental variables were acquired by the NDVI. Anyway, the NDVI acquiring the weights of environmental variables canbe applied to much wider ranges, because the NDVI can be much easier to access than the crop yields in practice.