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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Freeze/Thaw Detection and Validation Using Aquarius’ L-Band Backscattering Data
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Freeze/Thaw Detection and Validation Using Aquarius’ L-Band Backscattering Data

机译：使用Aquarius的L波段反向散射数据进行冻结/解冻检测和验证

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摘要

The seasonal cycle of landscape freeze/thaw (FT) state across mid- to high latitudes influences critical processes such as the land surface energy balance, carbon cycle dynamics related to vegetation growth, and hydrological partitioning between surface runoff and infiltration. In this paper, we produce the first daily FT classification for the 2011–2014 period based on L-band radar measurements from Aquarius. The radar FT algorithm used in this paper is based on a seasonal threshold approach, which is also the baseline algorithm applied to higher-resolution (3 km) radar measurements from NASA’s Soil Moisture Active/Passive (SMAP) mission (Launched January 31, 2015). The lower frequency (L-band) radar backscatter measurements from Aquarius provide enhanced sensitivity to FT conditions in vegetation canopy, snow and surface soil layers, although the relative radar penetration depth and sensitivity of the FT signal to these landscape elements will vary according to surface moisture and vegetation biomass conditions, and underlying land cover and terrain heterogeneity , . Evaluation of the seasonal threshold FT algorithm using Aquarius was performed using surface air and soil temperatures from selected stations in the Snow Telemetry (SnoTel) network. Analysis identified good agreement during the fall freeze-up period with flag agreement exceeding the 80% SMAP accuracy target when summarized on a monthly basis. Disagreement was greater during the spring thaw transition due in part to uncertainty in characterizing thaw from measurements. Unlike the fall season, stronger agreement in the spring was identified when the reference state was characterized with air temperature compared to soil temperature.
机译：中高纬度地区的景观冻结/融化（FT）状态的季节周期影响着关键过程，例如土地表面能量平衡，与植被生长有关的碳循环动态以及地表径流与入渗之间的水文分配。在本文中，我们根据水瓶座的L波段雷达测量结果得出了2011-2014年期间的每日FT分类。本文使用的雷达FT算法基于季节性阈值方法，这也是从美国国家航空航天局（NASA）的土壤水分主动/被动（SMAP）任务（2015年1月31日启动）应用于高分辨率（3 km）雷达测量的基线算法）。水瓶座的低频（L波段）雷达后向散射测量增强了对植被冠层，雪和表层土壤层中FT条件的灵敏度，尽管相对雷达穿透深度和FT信号对这些景观元素的灵敏度会根据地表而变化水分和植被生物量条件，以及潜在的土地覆盖和地形异质性，。使用雪瓶遥测（SnoTel）网络中选定站点的地面空气和土壤温度对使用水瓶座的季节性阈值FT算法进行了评估。分析确定了在秋季冻结期间的良好协议，按月汇总时，标志协议超过了SMAP准确性目标的80％。春季解冻过渡期间的分歧更大，部分原因是测量中表征解冻的不确定性。与秋季不同，当参考状态用空气温度而不是土壤温度来表征时，可以确定春季有更强的一致性。

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