地表冻融状态的被动微波遥感判别研究进展

Research advances in passive microwave remote sensing of surface freeze-thaw state

地表土壤冻融循环过程对地表水分和能量平衡有重要影响,进而影响植被生长、土壤含水量、碳循环和陆地生态系统。被动微波具有时间分辨率高、数据量丰富、对土壤水分敏感等特点,在监测地表冻融过程中发挥了重要的作用。随着国内外被动微波传感器相继升空,为冻土的年际变化、季节变化、日变化及长时序的近地表土壤冻融循环的研究提供了保障,近年来利用被动微波数据进行地表冻融循环的研究逐渐增多。基于以往研究,本文总结了被动微波遥感数据的类型和所含波段的特点;阐述了被动微波数据用于冻融监测的原理,重点介绍了被动微波数据在冻融监测研究中的五类算法,包括双指标算法、决策树算法、冻融判别式算法、季节阈值算法和基于L波段相对冻结因子阈值判别算法,并对五类算法进行了对比分析;梳理了基于不同算法和被动微波数据的冻融产品;研究中受传感器物理特性和地球形状及轨道等影响导致被动微波数据缺失的问题,可利用前后两天被动微波数据平均值,或者建立统计函数来补齐缺失数据。针对现有冻融判别算法对积雪覆盖地区的判别精度较低的问题,可采用数据同化的方法,或者从积雪辐射和冻土介电模型出发,对算法进行优化来提高判别精度。此外,SMAP冻融产品时序较短,在未来研究中可联合SMOS卫星扩展冻融产品的时间序列。

Soil freeze-thaw cycles have important effects on surface water and energy balance, and then affect vegetation growth, soil water content, carbon cycle and terrestrial ecosystem. Passive microwave plays an important role in monitoring global and regional surface freeze-thaw processes due to its high temporal resolution, abundant data and sensitivity to soil moisture. With the launch of passive microwave sensors at home and abroad, it provides conditions for the study of permafrost interannual variation, seasonal variation, diurnal variation and long time series of near-surface soil freeze-thaw cycle. In recent years, the study of surface freeze-thaw cycle using passive microwave data has gradually increased. Based on previous studies, this paper summarizes the types of passive microwave remote sensing data and the characteristics of the bands contained in them. Expounded the principle of passive microwave monitoring data used for freezing and thawing, focus on passive microwave data in five categories in the study of freezing and thawing monitoring algorithms, including double index algorithm, the decision tree algorithm, freeze-thaw discriminant algorithm, seasonal threshold algorithm and based on the freezing L-band relative factors discriminant algorithm threshold, and analysis of 5 kinds of algorithms are compared;The freeze-thaw products based on different algorithms and passive microwave data were combed. Finally, the problems and future research directions of passive microwave remote sensing in surface freeze-thaw applications are summarized. In the acquisition of passive microwave data, it is found that the passive microwave data is missing due to the physical characteristics of the sensor, the shape and orbit of the earth, and the low resolution of passive microwave data leads to the low precision of freeze-thaw discrimination.For the problem of missing passive microwave data, it is proposed to use the average value of passive microwave data before and after two days to fill the missing brightness temperature data, or establish statistical function to complement the missing data. For the problem of low passive microwave resolution, the current development trend is to scale down based on passive microwave data and combine with multiple data products, such as ground temperature and active microwave data, or perform probability discrimination on surface freezing-thawing state in pixels, so as to better describe surface freeze-thaw state. In terms of the algorithm for discriminating surface freezing-thawing, based on the problem that dual-index algorithm, decision tree algorithm, freezing-thawing discriminant algorithm and seasonal threshold algorithm cannot accurately distinguish snow and frozen soil, this paper proposes to adopt the method of data assimilation or start from the snow radiation and frozen soil dielectric model. Optimization of the algorithm for the snow covered surface can further improve the accuracy of freezethaw classification. Based on existing freeze-thaw products, Although SMAP freeze-thaw products continue to be updated, SAMP satellite was launched late, and SAMP freeze-thaw products have a short time series. In the future, the time span of this algorithm for freezing-thawing products can be extended by combining L-band data provided by SMOS satellite. The problems mentioned above and the direction of further research are of great significance for improving the accuracy of freezing and thawing discrimination and improving the understanding of the variation law of freezing and thawing cycles, and also have certain research space.