204-2 Clay Content Prediction Using on-the-Go Proximal Soil Sensor Fusion.
See more from this Division: SSSA Division: Soil Physics and Hydrology
See more from this Session: Remote Sensing of Land Surface and Vadose Zone Hydrologic Processes Oral
Abstract:
Previous researchers have tried to use methods such as VNIR spectroscopy for on-the-go modeling of several soil constituents. However, in spite of being a highly influential parameter on soil usability, very few studies so far have provided robust and accurate predictions for fields with high clay content variability.
An on-the-go multi-sensor platform was used to measure topsoil (25cm) VNIR spectra and temperature as well as electrical conductivity of top 30cm and top 90cm in 5 fields in different regions in Denmark. 125 calibration samples were collected from the points found by clustering the principal components (PC) of the spectra. Several pretreatments such as mean-centering, auto-scaling, spectral transformations and removal of faulty measurements were performed on the data. Partial least squares regression (PLSR) and support vector machines regression (SVMR) were performed using VNIR spectra, EC and soil temperature as predictors and clay content as the response variable. PLSR and SVMR models were validated using full and 20-segment cross-validation respectively.
The results were highly accurate with R2 of 0.94 and 0.94, root mean square error (RMSE) of 0.99 and 0.95, and ratio of performance to interquartile range (RPIQ) of 5.56 and 5.79 for PLSR and SVMR respectively. This shows the high potential of on-the-go soil sensor fusion to predict soil clay content and automate the mapping process.
See more from this Division: SSSA Division: Soil Physics and Hydrology
See more from this Session: Remote Sensing of Land Surface and Vadose Zone Hydrologic Processes Oral