基于地表动态反馈的平缓地区土壤属性数字制图方法探索研究

The soil-landscape relationship theory relates difficult-to-measure soil information, which includes soil type and soil properties, with some easy-to-obtain soil-forming environmental factors. This makes it possible to infer soil spatial variations from the easy-to-obtain environmental factors. In low relief areas such as plains, however, easily obtained soil forming factors generally do not co-vary with soil conditions over space to the level that they can be used effectively in predictive soil mapping (also called "digital soil mapping"). Mapping variation of soil properties over such areas remains a challenge. The research will infer the information of soils themselves backwards from the behaviors of the soils through the observations, characterizations and analyses on the behaviors. During the time with low vegetation cover, specific observation periods immediately after hydrothermal input (major rainfall and solar radiation) to land surface will selected. At the periods, we will use remote sensing observations with high temporal resolution to capture the dynamic feedbacks (responses) of the land surface to the hydrothermal input. From the land surface dynamic feedbacks, the information of the hydrothermal behaviors of soils will be extracted. Then, based on the information, we will construct covariates which can indicate soil spatial differences over areas with low relief. And finally through using the constructed covariates, an applicable approach to predictive soil mapping will be developed for such areas. This research can be a beneficial development on the theory and methods of existing predictive soil mapping which conventionally make soil inference from formative environmental factors such as terrain attributes and vegetation conditions. Also, it will be helpful for overcoming the "bottleneck" to obtaining soil spatial information in low relief areas, and for improving the accuracy of soil mapping.

土壤-景观关系理论将难以获取的土壤信息与易于观测的成土环境因素联系起来，使可以根据易于观测的环境因素推测土壤信息。然而，在平原等平缓地区，地形和植被等易于观测获取到的因素信息通常与土壤的空间协同程度比较低，难以有效地用于土壤推测制图。本研究拟从土壤的行为出发，通过对土壤行为的观测、表征和分析，反推土壤本身。在植被覆盖度较低的时期，选取水热（降雨/太阳辐射）输入地表后紧接的特定时段，利用较高时间分辨率遥感捕捉地表对水热输入的（逐日/日内）动态反馈，从中提取土壤水热行为信息，构建能够体现平缓地区土壤空间差异的协同变量，建立适用于平缓地区的推测性土壤制图方法。该研究可以拓展现有土壤推测制图的理论与方法（从成土因素出发推测土壤），有助于克服平缓地区土壤信息获取的"瓶颈"，提高土壤制图的准确性。

土壤景观关系将难以获取的土壤信息与易于观测的成土环境因素联系起来，使可根据易于观测的环境因素推测土壤信息。然而，在平原等平缓地区，地形和植被等易于观测获取到的因素信息通常与土壤的空间协同程度比较低，难以有效地用于土壤推测制图。针对这一问题，本项目提出了从土壤的行为出发，通过对行为的观测、表征和分析，推测土壤本身的思路，建立了基于地表动态反馈的平缓地区环境协同变量开发与数字土壤制图的方法，探索了利用地表对水热输入（降雨/灌溉和太阳辐射）的动态反馈开发能够体现平缓地区土壤空间差异的环境协同变量的方法，探讨了基于协同变量对平缓地区土壤属性（土壤质地和有机碳含量）进行二维（多变量空间统计）和三维（人工智能方法与深度函数结合）推测制图的方法，验证了所提出方法的适用性和有效性，在一定程度上克服了平缓地区土壤信息获取的困难，提高了平缓地区数字土壤制图的准确性。该研究是对现有从成土因素出发推测土壤属性的土壤制图思路的有力拓展，也为利用遥感进行资源调查和环境研究提供了可借鉴的思路。