Mapping the soil organic matter content in Northeast China considering the difference between dry lands and paddy fields

Abstract

Mapping the soil organic matter (SOM) content of cultivated lands at the regional scale is of great significance for evaluating the cultivated land quality and monitoring the soil carbon cycle, especially in the fertile black-soil area of China. The large paddy fields area is one of the characteristics of the black-soil area in Northeast China. The vast differences between paddy fields and dry lands may pose a major challenge in mapping the SOM contents of local cultivated lands. In this study, the SOM of cultivated lands in Northeast China is taken as the research object, and all available Landsat-8 images from 2014 to 2022 and the main environmental covariates (climate and terrain) are obtained. By combining the random forest regression algorithm, SOM prediction models of paddy fields and dry lands are established to evaluate the optimal window period and appropriate environmental covariates for paddy fields and dry lands. Finally, the accuracy difference between the global regression and local regression results for distinguishing paddy fields and dry lands is compared. The results showed that (1) the SOM content in Northeast China increased gradually from south to north, and the average SOM content in paddy fields was approximately 0.4 % higher than that in dry lands; (2) the SOM mapping time windows in paddy fields and dry lands in Northeast China differed, with paddy fields mapped in April and dry lands mapped in May; (3) the addition of environmental covariates improved the SOM prediction accuracy, with a greater importance for mapping SOM in paddy fields than in dry lands; and (4) the local regression results based on the division of paddy fields and dry lands achieved the highest prediction accuracy, with the highest determination coefficient (R²) being 0.653 and lowest root mean square error (RMSE) being 1.144 %. This study proves that different types of arable land have a great impact on the SOM prediction accuracy. Researchers should adopt different strategies to map the SOM contents of paddy fields and dry lands.