Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing

Abstract

Soil carbon stocks (SCSs) in an upland area of Tokachi District, Hokkaido, Japan, were estimated by satellite remote sensing and a soil survey. The soil parent materials in the studied area were alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash. Surface soil carbon concentrations (SSCCs) were negatively correlated with satellite image data (green, red, and near-infrared reflectance) for each parent material. The highest correlations between reflectance and SSCCs were obtained from red wavelength reflectance for alluvial deposits (r ¼-0.82, p < 0.01) and volcanic ash plus alluvial deposits (r ¼-0.91, p < 0.01), and from near-infrared reflectance for volcanic ash (r ¼-0.90, p < 0.01). We generated an SSCC map of the study area using the regression equations and satellite reflectance data. The soil survey results showed that SSCCs were positively correlated with SCSs in the 0-30 cm depth interval for each parent material (best-fit regressions: alluvial deposits, r ¼ 0.97; volcanic ash plus alluvial deposits, r ¼ 0.97; volcanic ash, r ¼ 0.97), and they were also positively correlated with SCSs in the 30-95 cm depth interval for volcanic ash (r ¼ 0.94). We were therefore able to generate a map of estimated SCSs from the SSCC map and the regression equations developed between SSCCs and SCSs. The estimated and measured SCSs in both depth intervals showed an almost 1:1 relationship, with root-mean-square errors of 19.5 (0-30 cm) and 28.6 Mg carbon (C) ha-1 (30-95 cm). According to the SCS maps, SCSs at 0-30 cm depth in areas of alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash were mostly 50-150, 100-200, and 50-250 Mg C ha-1 , respectively, whereas at 30-95 cm depth in the volcanic ash area they were mostly less than 250 Mg C ha-1 .

Rapid Increase and Long-Term Slow Decrease in Soil C stock Due to Agricultural Development in Hokkaido Tokachi District

“…The effective soil layer thickness was not uniform because of the underlying gravel layer. Effective tillage depth has been recognized as approximately 0.30 m for agricultural fields in Tokachi District [23]. The morphological features of soil profiles were described at each pit and at each time point from 2016 to 2018, just before wheat harvest every year (mid August to September).…”

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

“…For instance, Landsat, WorldView, SPOT, KOMPSAT, and IKONOS images have been effectively used for this purpose, for different soil layers [16]. In homogeneous or bare soils, simple linear regression models and remote sensing band values are usually used to estimate SOC and STN stocks across space [12,17]. To date, various DSM techniques have been used for the mapping, such as multiple stepwise regression (MLSR) model [13], regression Kriging [18], ordinary Kriging [19], random forest model [20], boosted regression trees (BRT) [21], geographic weighted regression (GWR) [22], and principal component regression [23].…”