Environmental drivers of spatial patterns of topsoil nitrogen and phosphorus under monsoon conditions in a complex terrain of South Korea

Abstract

Nitrogen (N) and phosphorus (P) in topsoils are critical for plant nutrition. Relatively little is known about the spatial patterns of N and P in the organic layer of mountainous landscapes. Therefore, the spatial distributions of N and P in both the organic layer and the A horizon were analyzed using a light detection and ranging (LiDAR) digital elevation model and vegetation metrics. The objective of the study was to analyze the effect of vegetation and topography on the spatial patterns of N and P in a small watershed covered by forest in South Korea. Soil samples were collected using the conditioned latin hypercube method. LiDAR vegetation metrics, the normalized difference vegetation index (NDVI), and terrain parameters were derived as predictors. Spatial explicit predictions of N/P ratios were obtained using a random forest with uncertainty analysis. We tested different strategies of model validation (repeated 2-fold to 20-fold and leave-one-out cross validation). Repeated 10-fold cross validation was selected for model validation due to the comparatively high accuracy and low variance of prediction. Surface curvature was the best predictor of P contents in the organic layer and in the A horizon, while LiDAR vegetation metrics and NDVI were important predictors of N in the organic layer. N/P ratios increased with surface curvature and were higher on the convex upper slope than on the concave lower slope. This was due to P enrichment of the soil on the lower slope and a more even spatial distribution of N. Our digital soil maps showed that the topsoils on the upper slopes contained relatively little P. These findings are critical for understanding N and P dynamics in mountainous ecosystems.

Error propagation in spectrometric functions of soil organic carbon

“…Pimstein et al, 2011). Altogether, Vis-NIR soil spectrometry has been used on many occasions to build SOC prediction models (Jiang et al, 2016;Kuang and Mouazen, 2013;Nocita et al, 2013).…”

“…However, the number of measurements used to gain one averaged spectrum differs between studies. Jiang et al (2016), for example, averaged 10 measurements to receive one spectrum, while Volkan Bilgili et al (2010) and Wang et al (2014) used four measurements. This difference is also assumed to have an influence on the uncertainties contained in the input data.…”

Peer Review #1 of "Some methods to improve the utility of conditioned Latin hypercube sampling (v0.1)"

“…cLHS is a random stratified procedure that choses sampling locations based on prior information pertaining to a suite of environmental variables in an givenarea. cLHS has been used extensively in digital soil mapping projects throughout the world with recent examples in the last 5 years including Sun et al (2017) in China, Jeong et al (2017) in Korea, Scarpone et al (2016) in Canada, and Thomas et al (2015) in Australia. cLHS has also been used for other purposes and contexts too.…”

Spatiotemporal Variability of Soil Nitrogen in Relation to Environmental Factors in a Low Hilly Region of Southeastern China

“…A wealth of literature is available that presents measurements of soil TN using digital soil mapping (DSM) techniques based on the integration of remotely sensed imagery with a geographic information system (GIS). These studies on DSM have employed two types of data mining techniques: aspatial techniques (e.g., boosted regression tree (BRT) [ 11 ], artificial neural network (ANN) [ 12 ], and random forest (RF) [ 13 ] models) and spatial techniques (e.g., regression kriging (RK) [ 14 ] and geographically weighted regression (GWR) [ 15 ] analysis). In terms of data, both hyperspectral data and field sampling data have been used in DSM models [ 16 ].…”