The spatial extent and duration of soil freeze/thaw (F/T) control water and heat exchange, the energy cycle, and climate change. Global warming causes permafrost thawing, which increases carbon emissions and in turn exacerbates climate change. Passive microwave remote sensing has been proven to be effective in monitoring land surface F/T. However, it was found that the applicability of existing passive microwave remote sensing-retrieved F/T products in large-scale areas [such as the Qinghai–Tibetan Plateau (QTP)] was influenced by some landscape factors, such as the arid climate type and terrain elevation gradient. FengYun-3 (FY-3) series satellites have accumulated nearly ten years of passive microwave data, but there is little work based on FY-3 passive microwave data to see its potential in land surface F/T status monitoring. In this work, we proposed a dynamic method to determine the surface F/T status by combining the edge detection method and discriminant function algorithm from FengYun-3B (FY-3B) X- and Ka-band microwave radiation imager (MWRI) data. Comparing the results against three F/T products based on in situ 5-cm soil temperature, we demonstrate that this algorithm performs best over different validation areas with an overall accuracy of 86.5%. More specifically, the new algorithm improved the accuracy of current F/T products in arid and semiarid regions from 73% to 90%. Additionally, the spatial distribution of frozen days over the QTP of 2018 based on the new algorithm has good consistency with the permafrost map. However, the accuracy is influenced by snowmelt and appears to be overestimated for thaw soil during the day. This algorithm performs well in QTP areas with complex topography and climate types and holds the promise of providing users with highly accurate F/T products on larger and even global scales.

中文翻译：

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FY-3/MWRI数据青藏高原地表冻融探测


土壤冻融（F/T）的空间范围和持续时间控制着水和热交换、能量循环和气候变化。全球变暖导致永久冻土融化，从而增加碳排放，进而加剧气候变化。被动微波遥感已被证明可以有效监测地表F/T。然而，研究发现现有被动微波遥感反演F/T产品在大范围区域[如青藏高原(QTP)]的适用性受到一些景观因素的影响，如干旱气候类型和地形高程梯度。风云三号（FY-3）系列卫星积累了近十年的被动微波数据，但基于风云三号被动微波数据的工作很少看到其在地表F/T状态监测方面的潜力。在这项工作中，我们提出了一种结合风云三号B（FY-3B）X和Ka波段微波辐射成像仪（MWRI）数据的边缘检测方法和判别函数算法来确定表面F/T状态的动态方法。将结果与基于原位 5 厘米土壤温度的三种 F/T 产品进行比较，我们证明该算法在不同的验证区域中表现最佳，总体精度为 86.5%。更具体地说，新算法将干旱和半干旱地区现有 F/T 产品的准确率从 73% 提高到 90%。此外，基于新算法的2018年青藏高原冻土日数空间分布与多年冻土图具有良好的一致性。然而，精度受到融雪的影响，并且对于白天解冻的土壤来说，精度似乎被高估了。 该算法在地形和气候类型复杂的青藏高原地区表现良好，有望为用户提供更大范围乃至全球范围内高精度的F/T产品。