黑土典型区土壤有机质遥感反演

    Soil organic matter predicting with remote sensing image in typical blacksoil area of Northeast China

    • 摘要: 土壤有机质(SOM)含量时空变异规律研究对于土壤肥力评价、土壤碳库估算、土壤资源利用与保护具有重要意义,而地貌、成土母质、土壤类型等差异、高光谱卫星影像较少等因素制约了区域尺度的SOM含量遥感反演方法研究的开展。该文以黑龙江省黑土带典型区为例,采集区域土壤样本,获取Landsat TM遥感影像,基于有机质含量与土壤反射率的定量关系,建立区域SOM遥感预测模型。结果表明:黑土区SOM含量高,一般大于2%,决定了有机质对土壤反射光谱特性的主要作用,而且该区SOM空间变异性显著,且耕作方式、气候等因素决定了裸土时间长,因而该区适于SOM含量遥感反演;有机质与TM各波段反射率均显著相关,最大相关系数在第3波段(0.63~0.69 μm),为-0.710,其次为4波段(0.76~0.90 μm),与实验室基于高光谱反射率数据分析的结果一致;基于TM影像2、3、4波段的SOM指数模型最优,预测精度高、稳定性好,可以用于揭示黑土典型区SOM含量的空间分布特征。该研究将为改进土壤理化参数遥感反演、土地质量评价、土壤碳库估算等工作方法提供理论与技术支持。

       

      Abstract: The study on spatial heterogeneity of soil organic matter (SOM) is significantly important to soil fertility evaluation, soil carbon pool estimation, soil resources utilization and protection. However, many factors, such as the spatial variation of relief, soil material and classes, few hyperspectral satellite images, restrict the regional SOM reversing with remote sensing (RS). Soil samples in typical blacksoil area of Heilongjiang province were collected, Landsat TM image was transformed and processed, and then the regional SOM predicting model was built with statistical methods. Results show that SOM content in blacksoil area is higher and larger than 2%, determining the dominant effect of soil organci matter; The spatial heterogeneity of SOM is significant, and soils are exposed for long time because of special farming and climate in blacksoil area, all these determine that the area is fit for SOM retrieving with RS; There are significant correlation between SOM and reflectance of six bands in VNIR spectral region, and the maximum R2 is -0.710 at Band 3 (0.63-0.69 μm), and the second at Band 4 (0.76-0.90 μm), which corresponds to the laboratory analysis result of hyperspectral reflectance; SOM exponential model based on Band 2-4 reflectance is the best on precision and stability, which can uncover the spatial content of SOM in blacksoil area; The results can provide theoretical and technical support for improving RS retrieving of soil physic-chemical parameters, evaluating soil quality and carbon pool.

       

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