Abstract
Soil organic matter (SOM) plays a critical role in agroecosystems and the terrestrial carbon cycle. Thus, accurately mapping SOM promotes sustainable agriculture and estimations of soil carbon pools. However, few studies have analyzed the changing trends in multi-period SOM prediction accuracies for single cropland soil types and mapped their spatial SOM patterns. Using time series 7 MOD09A1 images during the bare soil period, we combined the pixel dates of training samples and precipitation data to explore the variation in SOM accuracy for two typical cropland soil types. The advantage of using single soil type data versus the total dataset was evaluated, and SOM maps were drawn for the northern Songnen Plain. When almost no precipitation occurred on or near the optimal pixel date, the accuracies increased, and vice versa. SOM models of the two soil types achieved a lower root mean squared error (RMSE = 0.55%, 0.79%) and mean absolute error (MAE = 0.39%, 0.58%) and a higher coefficient of determination (R2 = 0.65, 0.75) than the model using the total dataset and resulted in a mean relative improvement (RI) of 30.21%. The SOM decreased from northeast to southwest. The results provide reference data for the accurate management of cultivated soil and determining carbon sequestration.
Highlights
Soil organic matter (SOM) is a vital component of the soil and contributes to the improvement of soil fertility status [1,2,3,4] and increasing grain yields [5,6,7]
Our study focused on two typical cropland soil types on the northern Songnen Plain and attempted to incorporate the pixel dates of training samples and precipitation to evaluate their impacts on SOM prediction accuracy based on time series MOD09A1 images and SOM observation data from multiple soil types
We demonstrated the advantages of using single soil type data to improve the SOM prediction accuracy and mapped the SOM content in the study area
Summary
Soil organic matter (SOM) is a vital component of the soil and contributes to the improvement of soil fertility status [1,2,3,4] and increasing grain yields [5,6,7]. Due to the high spatial heterogeneity of soil properties, these methods require numerous representative sample points to ensure prediction accuracy [19,21,22,23,24]. To overcome these pitfalls, SOM prediction using remote-sensing data is a cost-effective way to reduce sampling and analysis budgets in order to predict soil properties and categories over large areas [25,26]. Stepwise multiple regression provides the advantage of eliminating multicollinearity between input variables [35,36], and as such, it offers an improvement on traditional linear regression
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