University of Tehran Press Iranian Journal of Soil and Water Research 2008-479X 55 3 2024 05 21 Digital mapping of soil properties (Calcium Carbonate and soil clay percentage) using landsat 8 and Prisma satellite images by the random forest algorithm Digital mapping of soil properties (Calcium Carbonate and soil clay percentage) using landsat 8 and Prisma satellite images by the random forest algorithm 381 399 97393 10.22059/ijswr.2024.363941.669558 FA Sajjad Teimouri Bardyani Department of Soil Science and Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran Fereydoon Sarmadian soil science department< faculty of agricultural engineering and technology, university of Tehran Journal Article 2023 08 22 Mapping soil properties using hyperspectral and multispectral satellite images, along with statistical approaches, and machine learning models such as Random Forests (RF), has shown great progress in accurately preparing agricultural maps. This study aimed to compare the performance of PRISMA and Landsat 8 images in modeling calcium carbonate and clay percentage using a Random Forest model. Firstly, Surface soil data was collected from Abik region of Qazvin province from October 2020 to October 2022. Furthermore, PRISMA and Landsat 8 spectral datasets were extracted from images downloaded from the websites of these two satellites, and soil reflectance data were obtained. The Random Forest regression model was then calibrated to estimate soil properties. The results of this study showed that the best accuracy in estimating soil characteristics using PRISMA data was obtained by using Auxiliary Variables such as principal components analysis, spectral indices, and indices extracted from the digital elevation model. The use of these three data sets provided the uppermost value for following statistical indices for estimating calcium carbonate and soil clay percentage: coefficient of determination (R2), and Ratio of Performance to Inter Quartile range (RPIQ), Ratio Performance Deviation (RPD) and the lowest Root Mean Squared Error (RMSE) and Normalized Root Mean Squared Error (NRMSE). The best model for estimating clay percentage, using the Random Forest model and statistical indices, had an R2 of 0.90, RMSE of 4.11, NRMSE of 0.18, RPIQ of 0.95, and RPD of 2.29. The best model for estimating calcium carbonate, using the Random Forest model and statistical indices, had an R2 of 0.62, RMSE of 0.72, NRMSE of 0.20, RPIQ of 0.77, and RPD of 1.27. The results supported the expectation of the good ability of the PRISMA imager to estimate surface soil properties. Mapping soil properties using hyperspectral and multispectral satellite images, along with statistical approaches, and machine learning models such as Random Forests (RF), has shown great progress in accurately preparing agricultural maps. This study aimed to compare the performance of PRISMA and Landsat 8 images in modeling calcium carbonate and clay percentage using a Random Forest model. Firstly, Surface soil data was collected from Abik region of Qazvin province from October 2020 to October 2022. Furthermore, PRISMA and Landsat 8 spectral datasets were extracted from images downloaded from the websites of these two satellites, and soil reflectance data were obtained. The Random Forest regression model was then calibrated to estimate soil properties. The results of this study showed that the best accuracy in estimating soil characteristics using PRISMA data was obtained by using Auxiliary Variables such as principal components analysis, spectral indices, and indices extracted from the digital elevation model. The use of these three data sets provided the uppermost value for following statistical indices for estimating calcium carbonate and soil clay percentage: coefficient of determination (R2), and Ratio of Performance to Inter Quartile range (RPIQ), Ratio Performance Deviation (RPD) and the lowest Root Mean Squared Error (RMSE) and Normalized Root Mean Squared Error (NRMSE). The best model for estimating clay percentage, using the Random Forest model and statistical indices, had an R2 of 0.90, RMSE of 4.11, NRMSE of 0.18, RPIQ of 0.95, and RPD of 2.29. The best model for estimating calcium carbonate, using the Random Forest model and statistical indices, had an R2 of 0.62, RMSE of 0.72, NRMSE of 0.20, RPIQ of 0.77, and RPD of 1.27. The results supported the expectation of the good ability of the PRISMA imager to estimate surface soil properties. https://ijswr.ut.ac.ir/article_97393_247977d3c0a6d0368348c5e37d1e9a77.pdf