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An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products

by Huiran Gao 1,2, Wanchang Zhang 1,* and Hao Chen 1,2
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1697; https://doi.org/10.3390/rs10111697
Received: 4 September 2018 / Revised: 24 September 2018 / Accepted: 25 October 2018 / Published: 27 October 2018
Discriminating between surface soil freeze/thaw states with the use of passive microwave brightness temperature has been an effective approach so far. However, soil moisture has a direct impact on the brightness temperature of passive microwave remote sensing, which may result in uncertainties in the widely used dual-index algorithm (DIA). In this study, an improved algorithm is proposed to identify the surface soil freeze/thaw states based on the original DIA in association with the AMSR-E and AMSR2 soil moisture products to avoid the impact of soil moisture on the brightness temperature derived from passive microwave remotely-sensed soil moisture products. The local variance of soil moisture (LVSM) with a 25-day interval was introduced into this algorithm as an effective indicator for selecting a threshold to update and modify the original DIA to identify surface soil freeze/thaw states. The improved algorithm was validated against in-situ observations of the Soil Moisture/Temperature Monitoring Network (SMTMN). The results suggest that the temporal and spatial variation characteristics of LVSM can significantly discriminate between surface soil freeze/thaw states. The overall discrimination accuracy of the improved algorithm was approximately 89% over a remote area near the town of Naqu on the East-Central Tibetan Plateau, which demonstrated an obvious improvement compared with the accuracy of 82% derived with the original DIA. More importantly, the correct classification rate for the modified pixels was over 96%. View Full-Text
Keywords: soil freeze/thaw states; AMSR-E and AMSR2; soil moisture; dual-index algorithm soil freeze/thaw states; AMSR-E and AMSR2; soil moisture; dual-index algorithm
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MDPI and ACS Style

Gao, H.; Zhang, W.; Chen, H. An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products. Remote Sens. 2018, 10, 1697.

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