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This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0-30, 30-60, and 60-90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0-30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 x 150 m(2)) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.

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