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.

中文翻译：

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使用数字土壤测绘方法预测卡纳塔克邦的土壤深度

区域和国家层面土壤深度的空间信息对于做出作物适宜性决定至关重要。在本研究中，卡纳塔克邦的高分辨率（250 m）土壤深度图是使用数字土壤测绘方法制作的。NBSS&LUP 研究了 30 年的总共 5174 个土壤遗留数据集被收集和组织用于制图。测试分位数回归森林 (QRF) 和回归克里金 (RK) 算法以预测卡纳塔克邦的土壤深度。来自数字高程模型、归一化差异植被指数、landsat-8 数据和气候变量的地形属性用作协变量。对于模型校准，80% 的土壤深度数据用于验证，20% 的数据用于验证。为评估模型性能，计算了验证数据集的经典不确定性估计值，例如确定系数 (R2) 和均方根误差 (RMSE) 和偏差。与 QRF（R2 = 17%，RMSE = 37 cm）相比，RK 模型解释了预测土壤深度（R2 = 30%，RMSE = 34 cm）的最大变异性。发现岩性和海拔是预测卡纳塔克邦土壤深度的最重要变量。卡纳塔克邦预测的土壤深度范围为 22 至 173 厘米，目前的高分辨率（250 米）土壤深度图可用于不同的水文、作物建模和气候变化研究。RMSE = 34 cm）与 QRF（R2 = 17%，RMSE = 37 cm）相比。发现岩性和海拔是预测卡纳塔克邦土壤深度的最重要变量。卡纳塔克邦预测的土壤深度范围为 22 至 173 厘米，目前的高分辨率（250 米）土壤深度图可用于不同的水文、作物建模和气候变化研究。RMSE = 34 cm）与 QRF（R2 = 17%，RMSE = 37 cm）相比。发现岩性和海拔是预测卡纳塔克邦土壤深度的最重要变量。卡纳塔克邦预测的土壤深度范围为 22 至 173 厘米，目前的高分辨率（250 米）土壤深度图可用于不同的水文、作物建模和气候变化研究。