Soil Organic Matter Predicton Based On Geostatistics And Scorpan Model

Soil organic matter is a major source of plant nutrients and a major reservoir in the global C-cycle. Accurate estimate of spatial variability of soil organic matter is essential to evaluate soil quality and assess the carbon sequestration potential from field to regional scales, which is important for agricultural management and environment research on terrestrial sequestration of atmospheric carbon. Soil organic matter, influenced by natural soil variability and topography, is often spatially variable. Recently utilizing spatially correlated secondary information to improve the accuracy of prediction of soil properties has received more attention in pedometrics. Although some studies have been conducted to identify the spatial pattern of soil properties distribution in low-relief areas, only little is known about spatial distribution of the soil properties in more complicated areas in Northeast China. In order to provide adequate soil information for the modeling of landscape process, such as soil erosion, soil quality evaluation and plant growth, this study investigates to what extent cheap and readily available ancillary information derived from digital elevation models and remote sensing data can be use to support soil mapping respectively, and to indicate soil characteristics on the regional scale. Therefore, the terrain attributes and remote sensing indices are derived from different resolution DEM and remote sensing image respectively by 3S technology, the correlations between soil organic matter and derived secondary variables are compared and mapping results using different kriging strategies are presented.In this paper, we firstly study the impact of DEM on SOM mapping by implementing different kriging methods in which DEM and derived attributes are employed as secondary variables. The DEM with the resolution of 30m and 90m are selected, and several corresponding terrain attributes are computed by GIS. And the correlations between the derived secondary variables and SOM are compared. It is found that TWI based on MFD algorithm showed a stronger correlation with SOM than TWI base on D8 algorithm. Then, to compare their performance in different SOM mapping strategies, we designated TWI based on different algorithm as exhaustive secondary variables and employed OK, RK and CK for spatial prediction.This study was conducted to evaluated and compare spatial estimation by kriging and cokriging with remotely sensed data to predict SOM. To predict SOM in the study area using remotely sensed data as auxiliary variables, we selected a Landsat Thematic Mapper (TM) image acquired on 18 Aug. 2006. Several vegetation indices and tasseled cap transformation were calculated after the image preprocessing. And the relationship between SOM and remotely sensed data was estimated. Strongest correlations were found between the Landsat spectral reflectance of B5 and SOM content. Then we predicted the SOM using the kriging and cokriging models based on the B5 as secondary variables. And the performance of kriging and cokriging was assessed.The main conclusions of this paper are as follows: (1) Stronger correlations were found between topographic wetness index and SOM, and the correlation varied with the DEM resolution. The secondary variables derived from higher resolution DEM improved the prediction accuracy obviously. (2) Cokriging performed better than regression kriging when secondary variables do not have strong enough correlations with the primary variable. (3) The vegetation condition revealde by TM images can reflect SOM distribution indirectly. (4) The result showed cokriging with remotedly sensed data was better than kriging in SOM prediction, and the cokriging approach indicated that remotely sensed data have the potential as auxiliary variables for improving the accuracy and reliability of SOM prediction.The main innovation of this study is as follows: (1) the study which incorporated TWIs base on SFD and MFD algorithms into different kriging methods to predict SOM has a theoretic value, few studies have characterized explicitly the impact of different TWI algorithms on SOM mapping. (2) How to influence the performance of SOM prediction with different resolution DEM as secondary variable is not conclusive in larger scale, and the research of comparing the SOM prediction performance with different resolution DEM is few. Thus it provided a case study on the theory of soil attributes spatial variability, and it is significant to the development of theory about soil characteristics spatial variability. (3) The NDVI can indirectly the spatial variability of soil organic matter. It is a method exploration in a large scale. The result provides guidance for soil spatial variability study.