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Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields

Guardat en:
Publicat a:Nitrogen vol. 6, no. 2 (2025), p. 23-37
Autor principal: Al-Shujairy Qassim A. Talib
Altres autors: Al-Hedny, Suhad M, Naser, Mohammed A, Shawkat Sadeq Muneer, Ali Ahmed Hatem, Panday Dinesh
Publicat:
MDPI AG
Matèries:
Iraq
Outliers (statistics)
Wheat
Accuracy
Agricultural production
Datasets
Models
Agricultural ecosystems
Landsat
Arid zones
Principal components analysis
Nitrogen
Satellite imagery
Data processing
Monoculture
Soil improvement
Spectral reflectance
Contamination
Machine learning
Remote sensing
Spatial data
Soils
Environmental conditions
Spectrum analysis
Fertilizers
Soil conditions
Arid regions
Algorithms
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Resum:Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R2 = 0.773), followed by the ratio (B5/B7) model (R2 = 0.489), while the inverse log transformation (1/log (B5/B7), R2 = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems.
ISSN:2504-3129
DOI:10.3390/nitrogen6020023
Font:Agriculture Science Database