Mapping Soil Organic Matter in Low-Relief Areas Based on Time Series Land Surface Diurnal Temperature Difference

Abstract

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus, digital soil mapping methods based on soil–landscape relationships often fail in these areas. It is necessary to find new environmental variables and methods to mapping soil attribute over the low-relief areas. Time series remotely sensed data, such as thermal imagery, provide possibilities for mapping SOM in such areas. In this study, Jiangsu Province was chosen as an example in eastern China and a total of 1519 soil samples (0 ~ 20 cm layer) were collected from the Second National Soil Survey of Jiangsu Province. 8-day composited land surface diurnal temperature difference (DTD) was extracted from the time series of MODIS 8-day composited land surface temperature. 8-day averaged DTD was mean of 8-day composited DTD in the same periods between 2002 and 2011. Analysis showed that SOM content was significantly negative correlated with 8-day averaged DTD of different periods, of which higher correlation was in vegetation sparse periods. Averaged DTD of many periods and averaged DTD of specific periods were selected as two group of independent variable dataset. Linear regression, regression kriging (RK), and linear mixed model (LMM) fitted by residual maximum likelihood were used to model and map SOM spatial distribution. Ordinary kriging was used as a baseline comparison. The root-mean-squared error, mean error, and mean absolute error calculated from independent validation were used to assess prediction accuracy. Results showed that LMM are the best predictions, of which LMM using DTD of specific periods and DTD cell statistics as variables performed best. RK were somewhat worse than LMM. Linear regression performed worst. This suggests that time series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas and LMM improved mapping SOM spatial distribution, which provided an effective approach for improving DSM in the low-relief areas.