Comparison between random forest and multiple linear regression to create digital maps of soil chemical properties in the Thung Kula Ronghai region, Thailand

Using machine learning (ML) algorithms to digital soil mapping (DSM) allows theelucidation of relationships between soil properties and environmental variables enabling the precise prediction of soil nutrient levels. The accuracy of the predicted values using the random forest (RF) algorithm, which is the most popular ML algorithm for digital soil mapping, and multiple linear regression (MLR) were compared to create digital maps of soil chemical properties in the ThungKula Ronghai (TKR) region, Thailand. The spectral indices including moisture stress index (MSI), normalized difference water index (NDWI), saturation index (SI), brightness index (BI), and coloration index (CI) obtained from remote sensing (RS) data were found to be more effective for predicting the various soil properties than the topographic indices derived from the DEM in the plain area. The MLR and RF models successfully predicted soil chemical properties with good predictiveaccuracy. The results indicated that the RF model has a slightly higher accuracy than the MLR model.However, the MLR model is superior in interpreting the relationship with the model equations

Atypical Pattern of Soil Carbon Stocks along the Slope Position in a Seasonally Dry Tropical Forest in Thailand

The pattern of soil carbon stock is atypical along the slope position in a seasonally dry tropical forest; the mean stock values increase from the lower, middle, to upper slopes, at 11.5, 13.2, and 15.5 kg m−2, respectively. In sloping landscapes, soil organic carbon tends to accumulate in lower slopes, but our previous soil respiration study suggested that soil carbon stock distribution along the slope position in seasonally dry tropical forests is atypical. The aims of this study were: (i) to examine whether the atypical pattern occurs widely in the watershed; and (ii) to examine the pattern of root development in the soil profile as a source of soil carbon. The density and stock of soil carbon in three soil layers (0⁻10, 10⁻30, and 30⁻100 cm) of 13 soil profiles were compared in different positions on the slope (upper, middle, and lower). Root biomass at each slope position was also determined. Soil carbon density in each layer increased significantly with an increase in the relative position of the slopes, particularly in the 10⁻30 cm soil layer. The density of medium root (3⁻10 mm in diameter) in the upper slopes was significantly higher than that in the middle and lower slopes, especially for 15⁻60 cm soil layers. The atypical pattern of soil carbon accumulation along the slope position occurred widely in the studied watershed and appeared to be caused by the development of root systems in deeply weathered soil under xeric soil conditions in the upper slopes. Roots of bamboo undergrowth may also contribute to soil carbon stabilization by reducing soil erosion in the surface soil