Estimation of Soil Organic Matter Content in Nongan County Based on Multi-temporal Remote Sensing Images
Accurate acquisition of farmland soil carbon storage is of great significance for ensuring national food security and addressing climate change.Soil Organic Matter(SOM)is an important part of soil carbon pool.The accuracy of SOM estimation using remote sensing images is affected by single-phase and multi-temporal images,and it is not clear that how many images would be optimal to achieve the best performance for SOM estimation.This study took Nongan County,Changchun City,Jilin Province as the research area,and used Landsat 8 remote sensing images from 2013 to 2018 as the data source.Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI)and Soil Organic Carbon index(SOCI)were selected as indicators,and other spectral indices,topographic factors such as elevation,slope,slope direction and Topographic Wetness Index(TWI)were extracted.The farmland management measures such as grain production and fertilizer application were also employed for SOM estimation.SOM estimation model was constructed by using the random forest algorithm with a variety of indicators as input variables.The results show that compared with the indicators extracted from single phase satellite images,multi-temporal satellite images can make better use of multi-temporal image information,and the accuracy of SOM estimation is higher.With the increase of the employed image numbers,the improvement of model accuracy was marginal.In this study,the period of data used is 3 years,and the inversion accuracy will remain basically unchanged with the subsequent increase in the number of images used.The accuracy of model validation set R2 and RMSE for SOM inversion using 3-year image is 0.682 and 3.152 g/kg respectively.The results of this study not only have important significance for applying multi-temporal remote sensing image to SOM accurate estimation,but also have important significance for improving farmland soil quality and agricultural sustainable development.
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