Estimation accuracies of near infrared spectroscopy for general soil properties and enzyme activities for two forest sites along three transects

Ludwig, Bernard; Vormstein, Svendja; Niebuhr, Jana; Heinze, Stefanie; Marschner, Bernd; Vohland, Michael

doi:10.1016/j.geoderma.2016.10.022

Cited by 38 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with artificial neural network (ANN)-ECD (nonlinear) model performance for the mobile prediction of SOC reported by Kuang et al (2015), slight improved results can be observed with MARS-ECD obtained in the current work (RPIQ for TC = 3.57 -4.33), as RPD values reported by Kuang et al (2015) were improved from 1.93 for PLSR analysis to 2.28, for ANN. Furthermore, the current results outperform those obtained by other researchers (Pietrzykowski and Chodak, 2014) for TN based on non-mobile measurement and PLSR analysis only (RPIQ = 2.34), and combined PLSR with genetic algorithm (RPIQ = 4.12; Ludwig et al, 2017). This is also true for TC based on PLSR (RPIQ = 31.3; Sarkhot et al, 2011) and SVM and PLSR (RPIQ = 2.03 and 2.59, respectively; Wijewardane et al, 2016).…”

Section: Influence Of Geographical Scale On Models' Performancesupporting

confidence: 52%

Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques

Nawar

Mouazen

2017

CATENA

120

View full text Add to dashboard Cite

The development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties based on mobile measurements is essential for site specific soil management at fine sampling scale. The objective of the present study was to compare the mobile and laboratory prediction performance of vis-NIR spectroscopy for total nitrogen (TN), total carbon (TC) and soil moisture content (MC) of field soil samples based on single field (SFD), two-field dataset (TFD), UK national dataset (UND) and European continental dataset (ECD) calibration models developed with linear and nonlinear data mining techniques including spiking. Fresh soil samples collected from fields in the UK, Czech Republic, Germany, Denmark and the Netherlands were scanned with a fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305-2200 nm. After dividing spectra into calibration (75%) and validation (25%) sets, spectra in the calibration set were subjected to three multivariate calibration models, including the partial least squares regression (PLSR), multivariate adaptive regression splines (MARS) and support vector machines (SVM), with leave-one-out cross-validation to establish calibration models of TN, TC and MC. Resultsshowed that the best model performance in cross-validation was obtained with MARS methods

show abstract

Section: Influence Of Geographical Scale On Models' Performancesupporting

confidence: 52%

Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques

Nawar

Mouazen

2017

CATENA

120

View full text Add to dashboard Cite

show abstract

“…However, the use of GA‐PLS for three transects at two forest sites for an estimation of main soil properties (SOC, N, pH, soil texture) resulted in a marked improvement of estimation accuracies only in a cross‐validation approach. Validation with an independent soil profile at each site did generally not show marked improvements of estimation accuracies compared to PLSR ( Ludwig et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, accuracies of N estimations may depend on the range of N contents. For instance, Ludwig et al () reported a low precision of the N estimation for a validation profile (10–185 cm) of a forest soil and suggested that this was due to the very low range of N content, where laboratory results are also less reliable.…”

Section: Introductionmentioning

confidence: 99%

Use of different chemometric approaches for an estimation of soil properties at field scale with near infrared spectroscopy

Ludwig

Murugan

Parama

et al. 2018

J. Plant Nutr. Soil Sci.

Self Cite

View full text Add to dashboard Cite

Visible and near infrared spectroscopy (vis‐NIRS) may be useful for an estimation of soil properties in arable fields, but the quality of results are often variable depending on the applied chemometric approach. Partial least squares regression (PLSR) may be replaced by approaches which employ supervised learning methods or variable selection procedures in order to increase the proportion of informative wavelengths used in the estimation procedure, to reduce the noise of the spectra and to find the best fitting solution. Objectives were (1) to compare the usefulness of PLSR with either PLSR combined with a genetic algorithm (GA‐PLSR) or support vector machine regression (SVMR) for an estimation of soil organic carbon (SOC), total nitrogen (N), pH, cation exchange capacity (CEC) and soil texture for surface soils (0–5 cm, n = 144) of an arable field in Bangalore (India) and (2) to test and optimize different calibration strategies for GA‐PLSR for an improved estimation of soil properties. PLSR was useful for an estimation of SOC, N, sand and clay. In the cross‐validation (n = 96), accuracies of estimated soil properties generally decreased in the order GA‐PLSR > SVMR > PLSR. However, the order of estimation accuracies for the random validation sample (n = 48) changed to SVMR > GA‐PLSR > PLSR for SOC, N, pH, and CEC, whereas for clay the order changed to SVMR > PLSR > GA‐PLSR. A sequential procedure, which used the most frequently selected wavelengths of the GA‐PLSR runs, proved to be useful for an improved estimation of SOC and N. Overall, SVMR especially improved estimations of SOC and clay, whereas GA‐PLSR was particularly useful for SOC and N and it was the only approach which successfully estimated CEC in cross‐validation and validation.

show abstract

“…Chang et al [51] interpret their results in favour of direct measurement. Ludwig et al [18] name possible error propagation within the single measurement as possible source for the poorer performance, whereas Mutuo et al [74] did not suggest any reasons for the different outcomes.…”

Section: Predicting Ah Propertiesmentioning

confidence: 99%

“…They report the development of successful models for prediction, even though the studied soils differed from natural conditions in chemical and microbial properties. Ludwig et al [18] investigated the applicability of spectroscopy to predict SOC, N content and pH value along with enzyme activities on mineral horizons in two forest sites in Germany. They used partial least squares regression (PLSR) approaches and confirmed the usefulness for SOC and N contents, but had variable results for pH-values dependent on the data range.…”

Section: Introductionmentioning

confidence: 99%

Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?

Thomas

Petzold²,

Landmark

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, and could facilitate the provision of the necessary information. This study evaluates the potential of visual and near-infrared spectroscopy (vis-NIRS) for estimating soil parameters relevant for humus mapping in Saxon forests. Therefore, soil samples from the organic layer are included. So far there is little knowledge about the applicability of vis-NIRS in the humus layer of forests. We investigate the spectral behaviour of samples from organic (Oh) and mineral (0–5 cm, Ah) horizons, pointing out differences in the occurring absorption features. Further, we identify and assess the accuracy of selected soil properties based on vis-NIRS for forest sites, compare the outcome of different regression methods, investigate the implications for forest soils due to the presence and different composition of the humus layer and organic horizons and interpret the results regarding their usefulness for soil mapping and monitoring purposes. For this, we used retained humus soil samples of forests from Saxony. Regression models were built with Partial Least Squares Regression, Support Vector Machine and Cubist. Investigated properties were carbon (C) and nitrogen (N) content, C/N ratio, pH value, cation exchange capacity (CEC) and base saturation (BS) due to their importance for assessing humus conditions in forests. In organic Oh horizons, prediction results for C and N content achieved R² values between 0.44 and 0.58, with corresponding RPIQ ranging from 1.58 to 2.06 depending on the used algorithm. Estimations of C/N ratio were more precise with R² = 0.65 and RMSE = 2.16. Best results were reported for pH value, with R² = 0.90 and RMSE = 0.20. Regarding BS, the best model accuracy was R² = 0.71, with RMSE = 13.97. In mineral topsoil, C and N content models achieved higher values of R² = 0.59 to 0.72, with RPIQ values between 2.22 and 2.54. However, prediction accuracy was lower for C/N ratio (R² = 0.50, RMSE = 3.52) and pH values (R² = 0.62, RMSE = 0.29). Models for CEC achieved R² = 0.65, with RPIQ = 2.81. In general, prediction precision varied dependent on the used algorithm, without showing clear tendencies. Classification into pH classes was exemplified since this offers a new perspective for humus mapping on forest soils. Balanced accuracy for the defined classes ranged from 0.50 to 0.87. We show that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), in particular not only for mineral horizons but also for organic Oh horizons.

show abstract

Estimation accuracies of near infrared spectroscopy for general soil properties and enzyme activities for two forest sites along three transects

Cited by 38 publications

References 25 publications

Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques

Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques

Use of different chemometric approaches for an estimation of soil properties at field scale with near infrared spectroscopy

Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?

Contact Info

Product

Resources

About