Geospatial Mapping of Soil Organic Carbon Using Regression Kriging and Remote Sensing

Abstract

Geo-spatial mapping of soil organic carbon using regression kriging was performed for Lalo khala sub-watershed (a part of Solani watershed) located in western Uttar Pradesh, India. Soil organic carbon was predicted using eight predictor variables derived from the advanced space borne thermal emission and reflection radiometer satellite images and digital elevation model. The soil organic carbon was determined in 248 soil samples collected randomly within a 300 m 2 grid overlaid on the study area. Out of the eight predictor variables used in simple regression, the normalized difference vegetation index has the maximum correlation with the soil organic carbon (0.64) followed by vegetation temperature condition index (0.60), brightness index (-0.60), greenness index (0.57) and wetness index (0.51). Standardized principle components of the predictor variables were used in the prediction model so as to address the multicollinearity problem. The regression kriging predicted SOC value ranged from 0.19 to 1.93% with a mean value of 0.64 and standard deviation of 0.29. The SOC values were higher in upper piedmont with moderate forest followed by Siwalik hills while low values were found in the upper alluvial plains. The RMSE of the predicted SOC map was only 0.196 indicating the closeness of predicted values to the observed values. Regression kriging predicted SOC map can be used for spatial agriculture planning and consider as an ideal input for spatially distributed models. The higher efforts for its preparation are justified when quality, spatial distribution and accuracy are considered.

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

“…This finding coincided with Pei et al [29], in the case of using the single flow direction algorithm method to calculate TWI, like in our research. Our results are in agreement with Kumar et al [81] who reported that the correlation between TWI and SOC in a tropical region (India) was 7%. She et al [82] also stated that TWI is positively correlated with SOC content.…”

Estimation of organic carbon in paramo ecosystem soils in Colombia

“…La comunidad científica ha desarrollado mapas y bases de datos regionales, continentales y mundiales, que sirven de base para la evaluación del riesgo del suelo; no obstante, algunos productos cartográficos derivados de levantamientos de suelos y generados mediante técnicas de mapeo convencionales, son de escala pequeña y carecen de detalles en los límites espaciales (Arrouays et al 2017). Dado esto, el mapeo digital de suelos (MDS), definido por Lagacherie y McBratney (2006), emerge como una herramienta de conocimiento útil para analizar patrones espaciales de las propiedades del suelo en función de datos auxiliares disponibles, relacionados con clima, vegetación, topografía, material parental y uso del suelo (Hinge et al 2018;Kumar et al 2018).…”

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

“…VV polarization is mainly directly affected by the ground and canopy, whereas VH includes stem-ground double scattering and volume scattering [69] that has more interference information, so VV_5 is more important than VH_5 in orchard. The results in Figure 6a also indicate that CI and BI2 have made great contributions in orchard, due to soil color and soil brightness index having a high correlation with SOC content [19,70,71]. For dry land, BI2 also has a great influence on the prediction accuracy of SOC (Figure 6b).…”