Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy(IHSS) and the Soil Land Inference Model(So LIM) to map soil salinity over a regional area. A fuzzy c-means(FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level(membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to So LIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis(R2 = 0.70, RPD = 1.55, RMSE = 12.86) and Keriya Oasis(R2 = 0.75, RPD = 1.66, RMSE = 10.92), that in Fubei Oasis(R2 = 0.77, RPD = 2.01, RMSE = 6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method(R2 = 0.74, RPD = 1.67, RMSE = 11.18) performed better than the multiple linear regression model(R2 = 0.60, RPD = 1.47, RMSE = 14.45). 3) The statistical result show that approximately half(48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale. Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct- linking method derived from: historic observations Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (So LIM) To map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, including representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity v These samples are input based on fuzzy logic, to calculate the soil-forming environment similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy The results show that 1) the prediction is appropriate in Kuqa Oasis (R2 = 0.70, RPD = 1.55, RMSE = 12.86) and Keriya Oasis (R2 = 0.75, RPD = 1.66, RMSE = 10.92 ), that in Fubei Oasis (R2 = 0.77, RPD = 2.01, RMSE = 6.32) perform little better than in both two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases,3) The statistical result show that approximately half (R2 = 0.74, RPD = 1.67, RMSE = 11.18) show better than the multiple linear regression model 48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.