Evaluating the spatial variations of soil salinity and calcium carbonate in marginal lands of Sirjan playa (Iran), using PRISMA hyperspectral imagery

Farmers, agricultural decision-makers, and land-use planners require updated, reliable, and accurate assessments of soil characteristics for sustainable management of natural resources. Saline and calcareous soils are a significant threat to crop production and can significantly reduce agricultural productivity, especially in arid and semi-arid regions. The accumulation of salt in the root zone restricts soil processes including nitrification, denitrification, and residue decomposition due to diminishing microorganism activity and soil biodiversity. On the other hand, in calcareous soils, the availability of some nutrients such as P, Fe, and Cu is limited due to the high pH. Given that traditional approaches to monitoring and mapping are costly and time-consuming, a rapid and efficient estimation of soil properties has been given attention by researchers using remote sensing data with field measurements. In this study, we have explored the performance of the PRISMA hyperspectral imagery satellite (prisma.asi.it) for estimating spatial variations of electrical conductivity (EC) and calcium carbonate equivalent (CCE). The study took place in the marginal lands of Sirjan Playa, southeast of Iran (Lat. 55°32′E, Lon. 29°23′N), which are mainly under pistachio cultivation. A total of twelve PRISMA L2D (BOA reflectance) images, acquired from June 2020 to December 2023; co-registered with the closest Sentinel-2 image (of about 0.5 pixel of RMS) and smoothed by the Savitzky-Golay filter (frame size of 7 and 3rd degree polynomial), were used. Field campaigns were performed to collect 250 soil samples from the top 15 cm of surface soil. Furthermore, the EC (min = 1.25%, max = 44.75%, std = 5.65%). and CCE (min = 1.25%, max = 44.75%, std = 5.65%) was measured using HCl. Both gaussian process regression (GPR) and the partial least squares regression (PLSR) algorithms were tested to predict EC and CCE from PRISMA 2D image spectra The results revealed that GPR achieved good prediction for CCE with a R² of 0.75, a root mean square error (RMSE) of 4.09%, and a ratio of performance to interquartile distance (RPIQ) of 2.75. The PLSR model, instead, showed the highest performance (R² = 0.63, RMSE = 44.8, RPIQ = 1.5) for predicting EC. These models were validated by the K-fold cross-validation approach (k = 10). The reason for the weaker salinity (EC) prediction by the PLSR could be attributed to the non-linear spectral behavior with respect to the salinity level. Furthermore, it seems that the presence of significant amounts of gypsum in the area could mask the accuracy of the EC prediction. Moreover, salt (i.e., the dominant salt is halite in the study area) diagnostic absorption bands occur in the atmospheric water vapor absorption region of soil spectra. Therefore, hyperspectral remote sensing appears to be a valuable resource for monitoring the spatiotemporal variation of EC (a fair prediction model with an RPIQ of 1.5) and CCE (a very good model with an RPIQ of 2.75). Further analysis should be done to better understand the effect of the external parameter (e.g., gypsum abundance) on the EC prediction.