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The spatial information of soil organic matter (SOM) is crucial for precision agriculture and environmental modeling. It is, however, difficult to obtain the regional details of SOM by dense sampling due to the high cost. Although a variety of interpolation methods are available for mapping SOM at regional scales, accurate prediction usually needs densely distributed samples and requires the interpolated variable to meet some constraints such as spatial stationarity. This paper introduces the Geographically Weighted Regression (GWR) technique as an alternative approach for SOM mapping. We interpolated the spatial distribution of SOM based on a limited number of samples with the incorporation of multiple independent variables. We also compared GWR with the ordinary least squares regression approach in mapping SOM. Results indicated that GWR could capture more local details and improve the prediction accuracy. However, more attention should be paid to the selection of independent variables.

A new Mn(II) coordination polymers based on the 1,10- phenanthroline derivative and flexible dicarboxylate, namely [Mn2(L)2(trans-1,4-chdc)] (1), where L = 11-fluoro-dipyrido[3,2-a: 2′,3′-c]phenazine and trans-1,4-chdc = 1,4-cyclohexanedicar- boxylate, has been successfully synthesized under hydrothermal condition. Compound 1 shows a one-dimensional chain structure. The neighboring chains are extended into a 3D supramolecular architecture by π-π interactions. Its thermal property was also investigated.

Soil organic matter (SOM) is an important component of soils, and knowing the spatial distribution and variation of SOM is the premise for sustainably utilizing soils. The objective of this study was to compare geographically weighted regression (GWR) with regression kriging (RK) for estimating the spatial distribution of SOM using field-sample data in SOM and auxiliary data in correlated environmental variables (e.g., elevation, slope, ferrous minerals index, and Normalized Difference Vegetation Index). Results showed that GWR was a relatively better method and could provide promising results for SOM prediction in comparison with RK. The map interpolated by GWR showed similar spatial patterns influenced by environmental variables and the nonapparent effect of data outliers, but with higher accuracies, compared to that interpolated by RK.