Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Forest soils have large contents of carbon(C)and total nitrogen(TN),which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition,erosion and leaching.Therefore,the ratio of C to TN contents(C∶N),a crucial indicator of soil quality and health,is also different depending on soil horizon.These attributes can cost-effectively and rapidly be estimated using visible-near infrared-shortwave infrared(VNIR-SWIR)spectroscopy.Nevertheless,the effect of different soil layers,particularly over large scales of highly heterogeneous forest soils,on the perfor-mance of the technique has rarely been attempted.This study evaluated the potential of VNIR-SWIR spectroscopy in quantification and variability analysis of C∶N in soils from different organic and min-eral layers of forested sites of the Czech Republic.At each site,we collected samples from the litter(L),fragmented(F)and humus(H)organic layers,and from the A_(1)(depth of 2-10 cm)and A_(2)(depth of 10-40 cm)mineral layers providing a total of 2505 samples.Support vector machine regression(SVMR)was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer(profile).We further produced the spatial distribution maps of C∶N as the target attribute at each soil layer.Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes.Moreover,F was the most accurately predicted layer,regardless of the soil attribute.C∶N models and maps in the organic layers performed well although in mineral layers,models were poor and maps were reliable only in areas with low and moderate C∶N.On the other hand,the study indicated that reflectance spectra could efficiently predict and map organic layers of the forested sites.Although,in mineral layers,high values of C∶N(≥50)were not detectable in the map created based on the reflectance spectra.In general,the study suggests that VNIR-SWIR spectroscopy has the feasibility of modelling and mapping C∶N in soil organic horizons based on national spectral data in the forests of the Czech Republic.

Removing Moisture Effect on Soil Reflectance Properties: A Case Study of Clay Content Prediction

Visible, near-infrared and shortwave-infrared(VNIR-SWIR) spectroscopy is an efficient approach for predicting soil properties because it reduces the time and cost of analyses. However, its advantages are hampered by the presence of soil moisture, which masks the major spectral absorptions of the soil and distorts the overall spectral shape. Hence, developing a procedure that skips the drying process for soil properties assessment directly from wet soil samples could save invaluable time. The goal of this study was twofold:proposing two approaches, partial least squares(PLS) and nearest neighbor spectral correction(NNSC), for dry spectral prediction and utilizing those spectra to demonstrate the ability to predict soil clay content. For these purposes, we measured 830 samples taken from eight common soil types in Israel that were sampled at 66 different locations. The dry spectrum accuracy was measured using the spectral angle mapper(SAM) and the average sum of deviations squared(ASDS), which resulted in low prediction errors of less than 8% and 14%, respectively. Later, our hypothesis was tested using the predicted dry soil spectra to predict the clay content, which resulted in R^2 of 0.69 and 0.58 in the PLS and NNSC methods, respectively. Finally, our results were compared to those obtained by external parameter orthogonalization(EPO) and direct standardization(DS). This study demonstrates the ability to evaluate the dry spectral fingerprint of a wet soil sample, which can be utilized in various pedological aspects such as soil monitoring, soil classification,and soil properties assessment.