Journal of Integrative Agriculture ›› 2026, Vol. 25 ›› Issue (4): 1700-1715.DOI: 10.1016/j.jia.2025.06.003

• • 上一篇    下一篇

融合先验知识与多时相遥感影像提升盐碱-黑土交错区土壤有机质含量制图精度

  

  • 收稿日期:2025-02-13 修回日期:2025-06-02 接受日期:2025-05-19 出版日期:2026-04-20 发布日期:2026-03-16

Enhancing soil organic matter mapping in saline-alkali and black soil areas with prior knowledge and multi-temporal remote sensing

Depiao Kong1, 2, Chong Luo1#, Huanjun Liu1   

  1. 1 State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

    2 College of Geographical Sciences, Harbin Normal University, Harbin 150025, China

  • Received:2025-02-13 Revised:2025-06-02 Accepted:2025-05-19 Online:2026-04-20 Published:2026-03-16
  • About author:Depiao Kong, Mobile: +86-13883928843, E-mail: kongdepiao20000426@163.com; #Correspondence Chong Luo, E-mail: luochong@iga.ac.cn
  • Supported by:

    This study was supported by the Science and Technology Development Plan Project of Jilin Province, China (20240602052RC).  

PDF

摘要：

使用遥感技术进行土壤有机质 (SOM) 监测对于现代土地资源管理和环境保护至关重要。然而在土壤类型复杂、环境变量差异明显的盐碱-黑土交错区绘制 SOM 仍然具有挑战性。本研究整合先验知识，将吉林省分为盐碱区和黑土区，获取2019年至2023年吉林省裸土时期（4月至7月）的所有Sentinel-2影像。将 Sentinel-2 图像分为三个时间窗口（DOY 90-120、DOY 120-150 和 DOY 150-180），系统地评估了时间窗口、光谱指数（盐分指数、植被水分指数）、环境变量（地形和气候）以及局部回归在盐碱-黑土交错区的SOM含量制图潜力。结果表明：（1）盐碱区与黑土区SOM制图的最优时间窗口均为DOY 90-120；（2）加入盐度指数可提高盐碱区 SOM 制图精度，但会降低黑土区 SOM 制图精度，而植被水分指数可提高这两个地区的精度；（3）加入环境变量可以提高所有地区的SOM制图精度，其中黑土区地形变量相对重要，盐碱区气候变量相对重要；（4）基于盐碱区和黑土区分区的局部回归在SOM制图精度上优于整体回归，但其不确定性更高。研究表明将先验知识与多时相遥感影像的融合，可以显著提高盐碱-黑土交错区SOM制图精度，进而为不同土壤类型区域的精准管理和保护提供科学依据。

Abstract:

Soil organic matter (SOM) monitoring using remote sensing is critical for effective land resource management and environmental protection.  Mapping SOM in areas where saline and black soils are intertwined, with complex soil types and significant environmental variability, remains a challenging task.  This study integrated prior knowledge and classified Jilin Province, China, into saline-alkali and black soil areas.  All available Sentinel-2 images from 2019 to 2023 during the bare soil period (April to July) were collected, and the images were categorized into three time windows: day of year (DOY) 90–120, DOY 120–150, and DOY 150–180.  The potentials of these time windows, spectral indices (salinity index and vegetation moisture index), environmental variables (topography and climate), and local regression models for SOM mapping in the saline-black soil transition areas were then systematically evaluated.  The results revealed four key findings: (1) the optimal time window for SOM mapping in both the saline-alkali area and black soil area was DOY 90–120; (2) including the salinity index improved SOM mapping accuracy in the saline-alkali area but reduced it in the black soil area, whereas the vegetation moisture index enhanced accuracy in both areas; (3) incorporating environmental variables improved the SOM mapping accuracy in all areas, with topographic variables being more influential in the black soil area and climatic variables being more significant in the saline-alkali area; and (4) local regression models based on the saline-alkali area and black soil area outperformed the global regression model in terms of SOM mapping accuracy, although they exhibited higher uncertainty.  This study demonstrates that the integration of prior knowledge and multi-temporal remote sensing images significantly enhance SOM mapping accuracy in areas where saline and black soils intersect, thus providing a scientific foundation for the precise management and protection of areas with different soil types.

Key words: multi-temporal remote sensing , soil organic matter , prior knowledge , saline-alkali area , black soil area