Abstract
As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.
Highlights
The combination of the Cubist model and other machine learning models can greatly improve the accuracy of model data
Variables from five types of data sources were selected for data statistics, and the statistical results showed that the selected remote sensing data bands were all consistent with the band data distribution of radar data and spectral data
The results show that the prediction effect of different sensors and different data satellites on soil organic carbon (SOC) in sentinel data is better at the optimal resolution accuracy effect, separate prediction should be selected for the dominant resolution for prediction, and can see the change of the basic prediction performance of the data with the increase of the spectral resolution, the data due to the different resolution of each can reach a high accuracy prediction under the resolution related to the data itself
Summary
Soil organic carbon (SOC), as the main carbon pool on the land surface, plays an important role in the interactive process of carbon cycle in long time series [1]. Natural and anthropogenic factors within a small area can directly cause changes in SOC and indirectly affect changes in the carbon cycle within the area [2]. Small changes in SOC can cause changes in atmospheric CO2 and affect a series of global climate changes [3]. Global climate change is a serious problem that threatens the security of Earth’s system and human health today [4]. In order to predict climate change in advance and to find more effective ways to control the direction of climate change, research on the prediction of land surface SOC that causes climate change is an essential step.
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