Abstract
Soil property monitoring is useful for sustainable agricultural production and environmental modeling. It is possible to automatically predict soil properties in a wide range based on remote sensing images. Heihe River Basin was chosen as the research area. Measurements on three soil properties, which were pH, organic carbon, and bulk density, were available there. Two kinds of attributes were extracted, which were the remote sensing index and terrain attributes. The prediction models were constructed by random forest algorithms. The features were determined by combining correlation statistics with prediction error, and different features were selected for each of the three properties. The validation experimental results are presented. The error results were as follows: pH (MAE = 0.28, RMSE = 0.39, R2 = 0.41), organic carbon (MAE = 4.75, RMSE = 8.26, R2 = 0.75), and bulk density (MAE = 0.11, RMSE = 0.13, R2 = 0.70). Through the analysis and comparison of the experimental results, it was proven that the algorithm in this paper had a good performance in the prediction of organic carbon and bulk density.
Highlights
Soil properties and functions are closely related to soil ventilation, fertilization, water filtering, and other environmental conditions, which are important references for soil utilization, management, and improvement
Gaussian process (GP), random forest (RF), M5 rules, bagging, and decision trees were compared with partial least squares regressions (PLSRs) [2]
We calculated 17 features from remote sensing images and DEM data and a prediction model of the soil properties was constructed by the RF algorithm
Summary
Soil properties and functions are closely related to soil ventilation, fertilization, water filtering, and other environmental conditions, which are important references for soil utilization, management, and improvement. Soriano et al, (2017) tested and compared the performance of portable mid-infrared (MIR) and visible-near-infrared (Vis-NIR) spectrometers by partial least squares regressions (PLSRs) for the prediction of soil properties [1]. They found that the best spectral ranges in the Vis-NIR and MIR regions for the prediction of soil properties were 1650–2450 nm in the NIR and 2500–5000 nm in the MIR. Gaussian process (GP), random forest (RF), M5 rules, bagging, and decision trees were compared with PLSR [2] Their results showed that GP was the best
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