Remote sensing inversion model of soil organic matter in farmland by introducing temporal information

Abstract: Soil organic matter (SOM) is an important index to evaluate soil quality. The monitoring of SOM content and its spatial distribution is of great significance to soil utilization, soil conservation, estimation of soil organic carbon pool, and so on. It is difficult to estimate the reserves of soil organic carbon pool by using traditional methods. Early studies showed that there was a significant negative correlation between SOM and soil spectral reflectance, and there was a quantitative relationship between SOM and soil organic carbon, so we can estimate the SOM content by remote sensing and it will provide help to estimate the soil organic carbon pool. In this paper, 4 main soil types (black soil, chernozem soil, meadow soil and aeolian sand) were collected as sampling points in the typical area of Songnen Plain to construct prediction model for estimating the SOM content in the study area. The number of the soil sampling points was 147. Half of the sampling points (74 samples) were used to serve as calibration set and other sampling points (73 samples) were used to serve as validation set. The remote sensing inversion model was established to reveal the spatial distribution of SOM in the study area. To improve the accuracy and stability of inversion model and find the optimal model, spectral indices based on single MODIS image or multi MODIS images during the bare soil period, which could contain temporal information of the variation of soil moisture content and SOM content, were introduced into the multiple linear regression model. The results showed that the 8-day data of surface reflectance (MOD09A1 and MYD09A1) could be used to estimate the SOM content of different types of soils in Songnen Pain, Northeast China. The primary input variable of the model changed regularly, because the bare soil condition changed with the variation of date. Furthermore, because the condition of bare soil was the optimal, the prediction model based on the MODIS image on the 137th day in the year was the best among the models based on single MODIS image. The ratio of spectral index R61 (the ratio of Band 6 to Band 1) is significantly related to SOM, but it has little correlation with soil moisture, so it can eliminate the influence of soil moisture to a certain degree. R61 is suitable as a primary input variable for the inversion model to estimate SOM by using remote sensing method. The accuracy and stability of models based on multi MODIS images were generally better than the models based on single MODIS image. The model based on multi MODIS images on the 137th and 105th day in the year is the best among all models, and its R2 is 0.68, and its RMSE (root mean square error) is 0.84 for calibration set and 0.84 for validation set. The SOM content in Songnen Plain showed a decreasing trend from northeast to southwest. This study provides a rapid and nondestructive method to estimate SOM content in a large scale. Moreover, the results of remote sensing inversion give supports for soil degradation assessment, land use, and estimation of soil carbon pool.