In Marathwada, cash crops like cotton and sugarcane are preferred, with a heavy dosage of fertilizers and pesticides. Literature reveals that repetition of the cash crops can result in bad soil health, landscape fragmentation and environmental degradation. Has resulted in the loss of soil health. Therefore, it is important to develop a method to recommend the next crop to the farmer based on the properties of the soil. In this paper, we develop and present a methodology to estimate the properties of the soil using in-situ sampling, remote sensing data and spatial interpolation algorithms. The locations of the sampling sites are identified by using the lower values of Normalized Difference Vegetation Index (NDVI) on the Sentinel-2 datasets. At each of the sites, three samples are collected from 15 cm underneath the surface. Using standard laboratory testing procedures, the quantities of Nitrogen, Phosphorus, Potassium, Organic Carbon, pH, electric conductivity are determined. The temperature is measured in-situ. The results of several spatial interpolation algorithms viz. inverse distance weighting, thin plate splines and Kriging and compared via cross validation analysis. The interpolation results can be used to determine crop type suitability for the district of Aurangabad, Maharashtra State based on the guidelines of Food and Agriculture Organisation (FAO).