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Watershed Planning within a Quantitative Scenario Analysis Framework
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Spatial prediction of soil depth using environmental covariates by quantile regression forest model.

M Lalitha1, S Dharumarajan2, Amar Suputhra2

  • 1ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bangalore, 560024, Karnataka, India. mslalit@yahoo.co.in.

Environmental Monitoring and Assessment
|September 18, 2021
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Summary

This study mapped soil depth distribution across Andhra Pradesh using quantile regression forest (QRF). The QRF model accurately predicted soil depth, outperforming ordinary kriging for land resource management.

Keywords:
Andhra PradeshPrediction performanceRandom forest modelSoil depthSpatial distributionUncertainty analysis

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Area of Science:

  • Environmental Science
  • Soil Science
  • Geospatial Analysis

Background:

  • Accurate soil depth prediction is crucial for land resource management, crop, nutrient, and ecosystem modeling.
  • Understanding spatial soil depth distribution is vital for effective agricultural and environmental planning.

Purpose of the Study:

  • To assess the spatial distribution of soil depth over 160,205 km² of Andhra Pradesh, India.
  • To compare the predictive accuracy of quantile regression forest (QRF) with ordinary kriging for soil depth mapping.

Main Methods:

  • Utilized 2854 soil datasets for calibration and validation (80:20 ratio).
  • Employed 20 covariables including Landsat imagery, terrain datasets, and bioclimatic factors.
  • Applied quantile regression forest (QRF) for spatial prediction of soil depth.

Main Results:

  • Precipitation, multi-resolution index of valley bottom flatness (MrVBF), mean diurnal range, isothermality, and elevation were key predictors.
  • QRF model achieved a R² of 42%, ME of -1.81 cm, and RMSE of 34 cm.
  • QRF outperformed ordinary kriging (R² of 32%, ME of -0.14 cm, RMSE of 37 cm).

Conclusions:

  • The QRF model demonstrated superior accuracy in predicting soil depth compared to ordinary kriging.
  • Soil depth is spatially dynamic and significantly influenced by terrain and environmental covariates.
  • Future improvements can be achieved by incorporating high-density bioclimatic and high-resolution terrain variables.