基于遥感与随机森林算法的陕西省土壤有机质空间预测

遥感数据作为反映土壤组成结构及植被生长状况的数据源,借助辅助环境因子的土壤属性预测在数字土壤制图中日益受到重视。论文运用随机森林（Random Forest,RF）算法,基于AWIFS（分辨率56 m）和MODIS（分辨率250 m）遥感数据及501个实测样点数据对陕西省土壤有机质空间分布状况进行预测,并对预测精度进行估算。结果表明陕西省土壤有机质含量以南部的秦岭山地区和大巴山区为最高,土壤有机质含量大于25 g·kg^-1,黄土高原南部处于中等水平,大部分在16~25 g·kg^-1之间,关中平原和汉中低山丘陵区含量偏低,大部分在13~25 g·kg^-1,而黄土高原北部和风沙滩区含量大部分低于10 g·kg^-1。基于AWIFS影像的预测效果要优于MODIS影像,成像日期对有机质预测的影响不大。基于RF模型的土壤有机质预测精度在设定的不同抽样百分比条件下,独立验证数据集的平均误差大部分不超过3 g·kg^-1,预测值与实测值的相关系数在0.7以上。高程是影响土壤有机质预测的最重要因子,当影像的分辨率降低时,样点分布的地理经纬度和坡度对土壤有机质预测的影响上升,植被因子的影响程度下降。

Soil Organic Matter Prediction Based on Remote Sensing Data and Random Forest Model in Shaanxi Province

There exists deviation of predication of soil organic matter (SOM) with observed data in special local topography units. The accuracy of SOM predication can be improved by combining observed data and remote sensing (RS) data, especially for SOM predication in large scale. In this study, AWIFS (Advanced Wide Field Sensor) and MODIS (Moderate Resolution Imaging Spectroradiometer) data, whose spatial resolution are 56 and 250 meters respectively, were combined with observed sample data to predict the spatial distribution of SOM in Shaanxi Province with RF (Random Forest) model. The spatial distribution of SOM in six types of topographical units were summarized, and the prediction accuracies of SOM based on RF model and OK (Ordinary Kriging) model were compared. The results indicated that the spatial differentiation of SOM is obvious in north-south direction in Shaanxi Province. It is the highest in Qinling and Daba mountain areas with SOM content higher than 25 g·kg^-1, and it is medium high in the south of Loess Plateau area with the SOM content 22-30 g·kg^-1. The content of SOM is lower in Guanzhong Plain and Hanzhong basin areas with SOM content among 13-25 g·kg^-1, while it is the lowest in north Loess Plateau and the blown-sand areas with SOM content less than 10 g·kg^-1. The prediction results based on AWIFS data (with higher spatial resolution) were better than those based on MODIS (with lower spatial resolution) data. The acquired data of images has little influence on SOM prediction. It is shown that the predicted value of SOM is a bit lower in autumn than in spring. With different percentages of sampling, the SOM prediction based on RF model is always better than that based on Ordinary Kriging model. The prediction accuracy in this study is reliable, because the mean error in independent validation set is no more than 3 g·kg^-1,and the correlation coefficient of the predicted values and the observed values are higher than 0.7. Elevation is the most importance factor influencing SOM prediction in Shaanxi Province. When the spatial resolution of RS data decreases, the importance of geographic location of sampling points increase and the importance of vegetation decrease.