Soil salinity estimation of sparse vegetation based on multispectral image processing and machine learning

The main effects of soil degradation include loss of nutrients, desertification, salinization, deterioration of soil structure, wind and water erosion, and pollution. Soil salinity is an environmental hazard present worldwide, especially in arid and semi-arid areas, which occurs mainly due to irrigation and other intensified agricultural activities. Therefore, the measurement of soil degradation in areas of low vegetation is of great importance in Peru. Two commonly used methods for estimating soil salinity are based on a measurement of electrical conductivity. Although on one hand, one of these methods is quite accurate, it requires many field samples and laboratory tests, which makes it quite expensive and impractical to measure large areas of the Peruvian coast. On the other hand, the second method is based on relative conductivity measurements in situ, being less accurate, but equally very expensive when measuring very large areas. For this reason, the use of multispectral imaging has been proposed for this purpose, using linear regression techniques. Following this trend in this work, the different descriptors used for the estimation were studied, comparing the correlations between the salinity indices and the soil samples, and two estimators based on SVM and PLSR were used to verify if the estimation improved. The PSWIR band, followed by the red one, was found to have the highest correlation and the indices based on the combination of these bands provide the best estimate with the classifiers evaluated.