نقشه برداري رقومي شاخص هاي حاصلخيزي خاك و ارتباط آن با عملكرد گندم در بخشي از اراضي شهرستان دهلران استان ايلام

Abstract Sustainable wheat production in arid an‎d semi-arid regions requires an understan‎ding of the spatial variability of soil properties, soil fertility, an‎d the factors controlling crop yield. This study aimed to perform digital soil mapping, spatial prediction of wheat yield, an‎d assessment of the soil fertility index by integrating visible–near-infrared (Vis-NIR) spectroscopy, remote sensing data, topographic variables, an‎d machine learning algorithms in part of the agricultural lan‎ds of Dehloran County, Ilam Province, western Iran. A total of 135 soil samples an‎d wheat yield measurements were collected from 1 × 1 m plots, an‎d soil physicochemical properties, Vis-NIR spectral data, an‎d environmental covariates were used for modeling. The results showed that incorporating environmental covariates improved the prediction performance of soil properties. For soil organic carbon, calcium carbonate, clay, an‎d silt contents, the Cubist model under the integrated scenario achieved R² values of 0.72, 0.85, 0.78, an‎d 0.82, respectively. In the digital mapping of soil family classes, the Ran‎dom Forest model at a 30-m spatial resolution achieved an overall accuracy of 70% an‎d a Kappa coefficient of 0.64. Soil family classes 2, 3, an‎d 7 showed a user’s accuracy of 100%, whereas classes 5, 6, an‎d 9 exhibited lower classification performance, which was attributed to the limited number of training samples an‎d the overlap in their physicochemical characteristics. Wheat yield ranged from 1.53 to 4.28 t ha⁻¹, with a mean of 3.20 t ha⁻¹. The integrated scenario combining spectral data, time-series remote sensing data, an‎d topographic variables outperformed the scenario without topographic information. Under this scenario, remote-sensing indices acquired in April were the most influential predictors of wheat yield, an‎d the Ran‎dom Forest model effectively captured the nonlinear relationships between environmental covariates an‎d wheat yield. The spatially predicted wheat yield ranged from 2.37 to 3.99 t ha⁻¹, with the highest predicted yields mainly occurring in the northern, northwestern, an‎d western parts of the study area. Prediction uncertainty was low across most of the study area, with a maximum uncertainty range of 0.44 t ha⁻¹. For assessment of the soil fertility index, second-derivative spectral preprocessing combined with the Support Vector Machine (SVM) model produced the best performance among the eva‎luated combinations. The SVM model based on spectral data an‎d second-derivative preprocessing achieved R², RMSE, an‎d RPD values of 0.61, 9.21, an‎d 1.61, respectively. Integrating spectral an‎d environmental data improved model performance to R² = 0.82, RMSE = 8.53, RPD = 1.92, an‎d RPIQ = 2.75. Spectral analysis further indicated that latent spectral variables, particularly those associated with wavelengths of 991, 1009, an‎d 1905 nm, as well as 1908, 2205, 2224, an‎d 2273 nm, were significantly associated with variations in wheat yield an‎d soil fertility. Overall, the findings demonstrate that integrating Vis-NIR spectral information an‎d environmental covariates with machine learning algorithms, particularly Ran‎dom Forest an‎d Support Vector Machine, provides a promising pedometrics-based framework for spatial prediction of wheat yield an‎d digital mapping of soil fertility an‎d soil family classes. Despite the limitations associated with the sample size an‎d imbalanced distribution of some soil classes, increasing the number of field observations an‎d incorporating higher-resolution spatial an‎d temporal data may improve model generalizability in future studies. Keywords: Visible-Near Infrared Spectroscopy, Ran‎dom Forest, Digital Soil Mapping, Soil Fertility Index, Wheat Yield, Precision Agriculture, Soil Family, Machine Learning

شمس اله ايوبي

حسين خادمي موغاري , محمود رستمي نيا

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