You need to model multi-variate regression (PLSR/PCR) between each laboratory obtained soil nutrient data of the sample points, keeping as dependent variable (Y) and spectral reflectance of the sampling points obtained from HYPERION data as independent variable (X). One usually predicts the nutrient value from the relationship between X and Y and cross-validates the predicted value through examining the precision of fit in linear regression between the actual and predicted values of nutrient.
You can learn about the MATLAB implementation of PLSR/PCR from the following link:
I'm finishing a manuscript about that but still under review. You can check these ones, I am sure it will help you. Furthermore, you can use NDWI or NDVI. In my study area, these indices had a good positive correlation with soil sampling data (SOC and TN). Usually, soil nutrients are related with humidity or vegetation. I hope this will help you Article Landsat-based approaches for mapping of land degradation pre...
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You need to model multi-variate regression (PLSR/PCR) between each laboratory obtained soil nutrient data of the sample points, keeping as dependent variable (Y) and spectral reflectance of the sampling points obtained from HYPERION data as independent variable (X). One usually predicts the nutrient value from the relationship between X and Y and cross-validates the predicted value through examining the precision of fit in linear regression between the actual and predicted values of nutrient.
You can learn about the MATLAB implementation of PLSR/PCR from the following link: