Prediction of Soil Depth in Karnataka Using Digital Soil Mapping Approach
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RESEARCH ARTICLE
Prediction of Soil Depth in Karnataka Using Digital Soil Mapping Approach S. Dharumarajan1 • R. Vasundhara1 • Amar Suputhra1 • M. Lalitha1 • Rajendra Hegde1 Received: 27 June 2020 / Accepted: 17 September 2020 Ó Indian Society of Remote Sensing 2020
Abstract Spatial information of soil depth in regional and national level is essential for arriving crop suitability decisions. In the present study, high-resolution (250 m) soil depth map of Karnataka is prepared using digital soil mapping approach. A total of 5174 Soil legacy datasets studied by NBSS&LUP over a period of 30 years is collected and organized for mapping. Quantile regression forest (QRF) and regression kriging (RK) algorithm is tested to predict the soil depth in Karnataka. Topographic attributes derived from digital elevation model, normalized difference vegetation index, landsat-8 data and climatic variables are used as covariates. For model calibration, 80% of soil depth data is used and 20% of data is used for validation. The classical uncertainty estimates such as coefficient of determination (R2) and root mean square error (RMSE) and bias were calculated for the validation datasets in order to assess the model performance. RK model explained maximum variability for prediction of soil depth (R2 = 30%, RMSE = 34 cm) compared to QRF (R2 = 17%, RMSE = 37 cm). Lithology and elevation are found to be most important variables for prediction of soil depth in Karnataka. The predicted soil depth in Karnataka is ranged from 22 to 173 cm, and the present high-resolution (250 m) soil depth maps are useful in different hydrological, crop modelling and climate change studies. Keywords Soil depth Digital soil mapping Prediction Quantile regression forest Regression kriging
Introduction Soil depth is an important soil property which determines the crop selection and controls many key soil properties like plant available water content, nutrient capacity and biological activity. Mapping of soil depth at high resolution is of great importance for natural resources management. Since the spatial variation of soil depth is controlled by complex interactions of parent material, relief, climate, and biochemical processes (Catani et al. 2010), prediction of soil depth with high accuracy is challenging task. Modern land management tools demand spatially accurate and quantitative information on soil properties for land use planning. Traditional soil survey maps, typically having scales of 1: 500,000 to 1: 50,000, often poorly represent spatial patterns of soil depth and other properties and are & S. Dharumarajan [email protected] 1
therefore of very limited use for many analysis such as models of crop yield, soil erosion studies and hydrological studies etc. Various methods have been explored by the researchers across the world to estimate the soil depth over landscapes (Minasny and McBratney 1999; D’Odorico 2000; Tesfa et al. 2009). Digital soil mapping (DSM) is an internationally recognized approach to map the soil properties at higher resoluti
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