基于遥感-模型融合的高寒山区土壤有机碳时空演化与空间表征研究

The alpine mountain area is particularly sensitive to climate change, and they are likely to lose C from the soil as the climate warms. We ought to be able to monitor the spatial distribution and changes in the C content of the soil in those regions..The Qinghai–Tibet Plateau is the highest and largest plateau on earth. Except for cultivation and in its lower parts it has hardly been disturbed by humans. Its vast expanse, 2.5 × 106 km2, in a cold and fairly humid climate means that its soil contains a great deal of C. It also plays a role in moderating climate changes both in Asia and the globally. We know rather little about the soil’s organic matter and its C content in the region, mainly because of its remoteness and the difficulties and expense of collecting soil material in that kind of terrain and transporting it to the laboratory..Our aim in this research is to investigate the spatial distribution and the changing rules of soil organic carbon (SOC) in the alpine mountain area using the combination of quantitative inversion method on land surface by remote sensing data and soil-landscape model as well as soil process model. Our research will focus on the Sejila Mountain Area in the Tibet Plateau. The three objectives of this research presented here are: (1) to explore the spatial distribution and variation of SOC and the environmental landscape control factors and the data confusing method using remotely sensed imagery and ground observations in the alpine mountainous; (2) to explore the effective way to combine the inversion method and soil process model and to build the SOC prediction model using the CASA and CENTURY models, which are based on the formation and evolution mechanism of SOC; and (3) to analysis the uncertainties of the prediction models and results of SOC based on the Meta analysis and stochastic simulation and UNEEC approach. Our research will provide a new idea of soil remote sensed survey in the alpine mountain area, and also provide the baseline information for estimating SOC in the Tibetan Plateau and the global alpine mountain.

正确揭示高寒山区土壤有机碳空间分布与演变特征关系到区域乃至全球土壤碳库估算的完整性。特别是高寒山区由于土壤采样不宜、垂直带空间变异强烈，对当前土壤遥感制图工作提出很大的挑战。本研究针对此问题，以青藏高原色季拉山地区为样区，提出采用遥感陆表定量反演方法与土壤-景观模型、土壤过程模型相结合来研究高寒山区的土壤有机碳空间分布与变化规律。研究包括：1）探索高寒山区复杂地形下土壤有机碳空间分异规律与环境景观控制因子以及星地协同反演方法；2）以土壤有机碳形成与演化机理为基础，引入CASA和 CENTRY模型，开展遥感陆表反演变量与过程模型的有效融合方法和有机碳空间预测建模方法的研究；3）基于Meta analyis、随机模拟以及UNEEC等方法的改进来分析土壤有机碳预测结果与模型的不确定性。研究将为高寒山区土壤遥感调查研究提供新思路，也为青藏高原乃至全球高寒山区土壤有机碳的估算提供基础数据支撑。

揭示高寒山区土壤有机碳空间分布与演变特征关系到区域乃至全球土壤碳库估算的完整性，同时可为准确评估土壤碳汇潜力，缓解气候变化研究提供重要的科学依据。.本课题针对高寒山区土壤采样不易、垂直带空间变异强烈等问题，提出的综合采用遥感陆表定量反演方法与土壤景观模型、土壤过程模型相结合来研究高寒山区的土壤有机碳空间分布与变化规律的理论体系，为高寒山区土壤遥感调查研究提供了新思路，也为青藏高原乃至全球高寒山区土壤有机碳的估算提供基础数据支撑。.围绕本课题研究任务，共发表了14篇论文，均发表在Remote Sensing of Environment、Geoderma、Science of the Total Environment等高水平SCI期刊上，其中有8篇论文为第1标注，同时获得国家发明专利5项。全面达成了项目设计的研究目标和相关指标。.主要研究成果包括：（1）收集与土壤发生发育相关的遥感和其他栅格数据集，以第二次全国土壤调查数据为基础，结合土壤景观模型、光谱测试方法和数字制图技术后，获取了西藏自治区土壤表层有机碳含量高精度空间分布图。同时基于Meta-analysis方法，与不同高寒山区土壤有机碳研究结果进行对比得出，本研究结果优于当前国际上常用的土壤数据集SoilGrid和HWSD，可更好的表征高寒山区土壤有机碳的垂直分布特性。为高寒山区土壤碳库估算提供了更准确的数据支撑。该成果已发表在土壤环境等领域期刊论文6篇，发明专利3项。.（2）基于土壤有机碳周转理论，对影响土壤碳固定分解的生物地球化学过程开展了一系列研究，主要包括对高寒山区高精度降水、土壤侵蚀的物理过程和微生物群落结构功能的生化过程的探索。结合数据挖掘手段、RUSLE模型和未来气候模式数据得到的青藏高原高时空分辨率的降水遥感数据集和土壤侵蚀空间分布结果，以及结合不同采样策略、高通量测序技术和数据分析手段得到的高寒山区海拔梯度与土地利用方式对土壤微生物群落的综合效应。对基于遥感-模型融合的高寒山区土壤有机碳时空演化和空间表征研究提供了更详尽的理论基础。最后基于蒙特卡洛随机模拟和全局敏感性分析方法对土壤过程模型CENTURY模型的模型参数、模型结构进行了优化和完善。相关研究已发表SCI论文8篇，发明专利2项。.本课题研究为高寒山区土壤有机碳要敢反演，模型模拟研究提供了新认识，具有重要的科学意义。