Prediction of soil organic carbon at the country scale: stratification strategies for near-infrared data
Research has shown that the application of near-infrared (NIR) spectroscopy can be used to predict soil attributes, in particular for regional to continental scales. However, there are challenges when NIR is used at the regional
scale because of the considerable spatial variation. This study has predicted SOC at the country scale (German
agricultural soil inventory) with different stratification strategies for NIR data: (i) calibration with memory-based learning (MBL) algorithms that use spectral similarity and (ii) simple stratification based on soil properties
(depth, pH and soil texture) and land use. To optimize calibration models, this study aimed to predict soil organic carbon (SOC) determined by these three strategies for 1410 soil profiles selected from the German agricultural
soil inventory. The profiles covered a wide range of soil types and characteristics. The calibration procedures were based on complete soil profile data of two-thirds of the dataset and one-third of the dataset was used for independent validation (prediction); the profiles were selected randomly. Available soil properties for stratifying the datasets were: soil depth (topsoil 0–30 cm and subsoil 31–100 cm), pH and texture class (silty, clayey, sandy
and loamy). The profiles were also stratified by land use (cropland and grassland) and with the MBL method. The calibrations were carried out by partial least-squares regression (PLSR), and each stratification model was
compared with the global model. The root mean square error of cross-validation (RMSECV) for the global model was 4.2 g SOCkg
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