基于可见光和红外波段的陆面蒸散与土壤水分的一体化遥感模型及其反演

Great progresses have been made in the regional estimate of soil moisture and terrestrial evapotranspiration from remote sensing; however there are still many unresolved issues. The currently existing approaches have both advantages and disadvantages. In remote sensing, soil moisture and terrestrial evapotranspiration are usually estimated independently, although they are closely related and coupled to each other in hydrometeorology. Development and improvement of new approaches in retrieval of soil moisture and terrestrial evapotranspiration is needed to further our understanding of the interactions between the soil moisture and terrestrial evapotranspiration in a regional or global climate system. We propose to develop a new approach which can retrieve soil moisture and terrestrial evapotranspiration simultaneously from visible and thermal infrared remote sensing observations by linking them with the relative soil moisture content. In this approach, the uncertainty in the models of soil moisture and terrestrial evapotranspiration will be minimized by the synchronized estimation. The linkage is established by thermal inertia and the trapezoid spaceof vegetation and land surface temperature. Together with the relationship between pseudo thermal inertia for vegetated area and the true thermal inertia for bare soil, multi-layered Penman-Monteith approach for evapotranspiration, physically based spatial expansion of surface air temperature and humidity, the synchronized retrieval of soil moisture and terrestrial evapotranspiration from remote sensing can be achieved. In this proposed study, the scientific questions to be solved include: a) estimating the soil moisture for vegetated area which traditionally couldn’t be done by thermal inertia; b) developing a method to locate the extreme dry edge in the trapezoid space of vegetation and land surface temperature which is traditionally subjectively determined; c) Reducing the uncertainty in spatial expansion of surface air temperature and humidity usually based on geostatistics; d) Improving the accuracy of the terrestrial evapotranspiration retrieval from remote sensing.

目前土壤水分和地表蒸散的遥感反演方法都有其各自的优缺点，仍需要继续发展和完善。已有的土壤水分和地表蒸散的遥感反演方法大都相互独立进行。自然界中土壤水分和地表蒸散之间具有千丝万缕的联系，二者相互作用，互为因果。本项目的目标拟发展一种能够同时反演土壤水分和地表蒸散的一体化遥感模型，通过土壤相对湿度将二者联系起来，同时解决模型中的几个关键不确定性问题。以地表热惯量和植被指数-地表温度梯形空间为纽带，发展植被覆盖下伪热惯量与裸土真热惯量的转换方法，发展分层P-M蒸发蒸腾方法，发展基于局地热量平衡和水平平流的空气温度空气湿度遥感方法，在上述基础上建立土壤水分和地表蒸散的一体化遥感反演模型。解决植被覆盖度高的区域不能使用遥感热惯量估算土壤水分的难题；解决植被指数-地表温度梯形空间理论干边难以准确定位的问题；解决依赖于地统计方法的空气温湿度空间扩展不确定性大的问题；提高遥感反演土壤水分和地表蒸散的精度。

目前土壤水分和地表蒸散的遥感反演方法都有其各自的优缺点，仍需要继续发展和完善。已有的土壤水分和地表蒸散的遥感反演方法大都相互独立进行。本项目的目标发展一种能够同时反演土壤水分和地表蒸散的一体化遥感模型，通过土壤相对湿度将二者联系起来，同时解决模型中的几个关键不确定性问题。以地表热惯量和植被指数-地表温度梯形空间为纽带，发展植被覆盖下伪热惯量与裸土真热惯量的转换方法，发展分层P-M蒸发蒸腾方法，发展基于局地热量平衡和水平平流的空气温度空气湿度遥感方法，在上述基础上建立土壤水分和地表蒸散的一体化遥感反演模型。解决植被覆盖度高的区域不能使用遥感热惯量估算土壤水分的难题；解决植被指数-地表温度梯形空间理论干边难以准确定位的问题；解决依赖于地统计方法的空气温湿度空间扩展不确定性大的问题；提高遥感反演土壤水分和地表蒸散的精度。