ABSTRACT

We investigated the sensitivity to vegetation cover type of active (PALSAR) and passive (SMAP) freeze/thaw (F/T) classification. We also used F/T classification from high-resolution PALSAR data (30 m) to follow the evolution of frozen and thawed soil states obtained from an adaptive algorithm with low-resolution SMAP data (36 km). We used PALSAR and SMAP scenes acquired from June 2015 to January 2017 over the Tursujuq National Park (Umiujaq, Quebec, Canada). A new F/T algorithm with a specific reference threshold under each vegetation type (shrub, grass, lichen, wetland, and bare land) is proposed to classify PALSAR pixels. The validation of the PALSAR F/T classification with soil temperature at ~5 cm depth revealed a greater overall accuracy (> 80%), with horizontal transmitted and vertical received (HV) thresholds. The PALSAR F/T classification shows that a SMAP pixel is classified as frozen when more than 50% of its area is frozen at the surface. We confirmed the sensitivity to vegetation cover type of passive and active F/T classification with L-band sensor.

摘要

我们研究了主动 (PALSAR) 和被动 (SMAP) 冻融 (F/T) 分类对植被覆盖类型的敏感性。我们还使用来自高分辨率 PALSAR 数据 (30 m) 的 F/T 分类来跟踪从具有低分辨率 SMAP 数据 (36 km) 的自适应算法获得的冻融土壤状态的演变。我们使用了 2015 年 6 月至 2017 年 1 月在 Tursujuq 国家公园（加拿大魁北克 Umiujaq）上获取的 PALSAR 和 SMAP 场景。提出了一种在每种植被类型（灌木、草、地衣、湿地和裸地）下具有特定参考阈值的新 F/T 算法来对 PALSAR 像素进行分类。PALSAR F/T 分类在 ~5 cm 深度土壤温度的验证显示更高的整体精度 (> 80%)，具有水平传输和垂直接收 (HV) 阈值。PALSAR F/T 分类表明，当一个 SMAP 像素超过 50% 的区域被冻结在表面时，它被归类为冻结。我们用 L 波段传感器确认了被动和主动 F/T 分类对植被覆盖类型的敏感性。