Accurately mapping the spatial distribution of soil organic matter (SOM) content is critical for informed land management decisions and comprehensive climate change analyses. Remote sensing-based SOM mapping models during periods of bare soil exposure have demonstrated efficacy in various regional studies. However, integrating bare soil imagery with growing season imagery for SOM content mapping remains a complex process. We conducted a study in Youyi Farm, a representative area of black soil in Northeast China. We collected 574 soil samples (0–20 cm) with SOM content through field sampling and laboratory analysis. Additionally, cloud-free Sentinel-2 images were obtained from the Google Earth Engine (GEE) platform for both the bare soil period (April-June, October) and crop growth period (July-September) from 2019 to 2021. To assess the influence of crop growth information on SOM mapping, we incorporated remote sensing imagery during the crop growth period, considering different crop type zones (maize (Zea mays L.), soybean (Glycine max L.), and rice (Oryza sativa L.)). We conducted overall and zonal regressions using the random forest (RF) model to validate the prediction results through cross-validation. Our findings indicate that: (1) adding crop growth period images to the bare soil period images in different years can improve the accuracy of SOM mapping. For example, in the overall regression model of 2020, the highest accuracy was achieved by using the combination of May-July images, with an R value of 0.70 and an RMSE value of 0.71 %; (2) zonal regression by differentiating crop types can effectively improve the SOM mapping accuracy. In 2019, using zonal regression, the R of SOM mapping accuracy was improved by 0.02 and the RMSE was reduced by 0.03 % compared with the overall regression; (3) precipitation is an important factor affecting the accuracy of SOM prediction, and the lower the precipitation, the higher the accuracy of SOM prediction. In summary, the results of this study show that in the SOM remote sensing mapping of the black soil area, the growing period remote sensing information of different crop types should be comprehensively considered and combined with the image data of the years of lower precipitation, the accuracy of the SOM mapping can be effectively improved, which provides a new technological path and an application basis for the enhancement of the accuracy in remote sensing mapping with soil attributes.

中文翻译：

---

裸土与作物生长遥感数据融合提高黑土区土壤有机质制图精度


准确绘制土壤有机质 (SOM) 含量的空间分布对于明智的土地管理决策和全面的气候变化分析至关重要。裸露土壤暴露期间基于遥感的 SOM 绘图模型已在各种区域研究中证明了有效性。然而，将裸露土壤图像与生长季节图像相结合以绘制 SOM 内容图仍然是一个复杂的过程。我们在东北黑土的代表区友谊农场进行了研究。我们通过现场采样和实验室分析收集了 574 个土壤样品（0-20 cm），其中含有 SOM 含量。此外，从 Google Earth Engine (GEE) 平台获得了 2019 年至 2021 年裸土期（4 月至 6 月、10 月）和作物生长期（7 月至 9 月）的无云 Sentinel-2 图像。为了考虑作物生长信息对 SOM 制图的影响，我们结合了作物生长期间的遥感影像，考虑了不同作物类型区域（玉米 (Zea mays L.)、大豆 (Glycine max L.) 和水稻 (Oryza sativa L.)） ）。我们使用随机森林（RF）模型进行整体和区域回归，通过交叉验证来验证预测结果。我们的研究结果表明：（1）将不同年份的作物生长期图像添加到裸土时期图像中可以提高SOM制图的准确性。例如，2020年整体回归模型中，使用5月-7月图像组合实现了最高准确率，R值为0.70，RMSE值为0.71%； (2)通过区分作物类型进行分区回归可以有效提高SOM制图精度。 2019年，使用分区回归，SOM制图精度的R提高了0.02，RMSE降低了0。与整体回归相比03%； (3)降水量是影响SOM预测精度的重要因素，降水量越低，SOM预测精度越高。综上所述，本研究结果表明，在黑土区SOM遥感制图中，应综合考虑不同作物类型的生长期遥感信息，并结合降水量较少年份的影像数据，可以有效提高SOM制图精度，为提高土壤属性遥感制图精度提供了新的技术路径和应用基础。