Soil organic carbon mapping and prediction based on depth intervals using kriging technique: A case of study in alluvial soil from Sudan

Abstract

Soil organic carbon plays a vital role in the arid and semiarid regions. This study aimed to predict and map soil organic carbon content at soil depth intervals of 0-0.3 m, 0.3-0.6 m, 0.6-0.9 m, and 0.9-1.2 m in alluvium soils along Blue Nile and River Nile, Sudan. Ordinary kriging (OK) technique was used as a geostatistical tool and applied to model the spatial variability of soil organic carbon in the study area. A total of 38 soil profiles were excavated in the study area and 152 samples from the four depths intervals were collected for determining organic carbon content. Results revealed that, the spatial autocorrelation for the different soil layers was moderate to weak with a nugget to sill ratios ranging from 0.21 to 0.86 suggesting their controlled by both intrinsic and extrinsic factors. The root mean square error standardized (RMSE) of the predictions ranging from 0.79 to 0.83 indicating that the model which generated by ordinary kriging was correctly estimating the variability of soil organic carbon in the study area.

Dependência espacial e krigagem para a estimativa textural de solos em São João Evangelista

“…A amostragem de solo, por sua vez, deve ser planejada com cautela e considerar aspectos estatísticos relacionados à dependência espacial (SILVA et al, 2017;TAKOUTSING et al, 2017;FERRAZ et al, 2018;SULIEMAN;ALGARNI, 2019;WANG et al, 2020). O modo mais simples para a obtenção de muitas amostras compostas de solo se baseia em métodos aleatórios para a seleção de pontos amostrais.…”

“…Entretanto, a amostragem aleatória baseada na estatística "clássica" não é apropriada diante da ocorrência de dependência espacial (GUARÇONI; ALVAREZ, SOBREIRA, 2017). Nestas circunstâncias, recomendam-se técnicas geoestatísticas de interpolação para a estimativa de atributos do solo (SHAHRIARI et al, 2019;SULIEMAN;ALGARNI, 2019;LI;WAN;SHANG, 2020).…”

Soil moisture data using citizen science technology cross-validated by satellite data

“…While Simple kriging assumes a known mean over the entire domain, OK assumes a constant unknown mean only over the search neighborhood. Sulieman & Algarni (2019) used the OK technique to predict and map soil organic carbon at several depth intervals in alluvium soils along the Blue Nile, Sudan. They collected 152 samples at several depths and utilized a spherical semivariogram to describe the data's autocorrelation.…”