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 (R) 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.

中文翻译：

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考虑旱地和水田差异的东北地区土壤有机质含量制图


绘制区域尺度耕地土壤有机质（SOM）含量对于评价耕地质量和监测土壤碳循环具有重要意义，特别是在我国肥沃的黑土地区。水田面积大是东北黑土区的特点之一。水田和旱地之间的巨大差异可能对绘制当地耕地的有机物质含量构成重大挑战。本研究以东北地区耕地SOM为研究对象，获取了2014年至2022年所有可用的Landsat-8影像以及主要环境协变量（气候和地形）。结合随机森林回归算法，建立水田和旱地的SOM预测模型，评估水田和旱地的最佳窗口期和合适的环境协变量。最后，比较了全局回归和局部回归结果区分水田和旱地的精度差异。结果表明：（1）东北地区SOM含量由南向北逐渐增加，水田平均SOM含量比旱地高约0.4%； （2）东北地区水田和旱地SOM制图时间窗口不同，水田制图时间为4月，旱地制图时间为5月； (3)环境协变量的加入提高了SOM预测的准确性，对于绘制水田的SOM比旱地的SOM更重要； (4)基于水田和旱地划分的局部回归结果预测精度最高，判定系数(R)最高为0。653，最低均方根误差 (RMSE) 为 1.144 %。本研究证明不同类型的耕地对SOM预测精度有很大影响。研究人员应采用不同的策略来绘制稻田和旱地的 SOM 含量图。