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A semi-empirical microwave transmissivity model for forest canopies during the snow season

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：yangjw@bnu.edu.cn
  • Introduction：杨建卫，研究方向为积雪微波遥感。E-mail： yangjw@bnu.edu.cn
YANG Jianwei1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：jiang@bnu.edu.cn
  • Introduction：蒋玲梅，研究方向为微波遥感。E-mail： jiang@bnu.edu.cn
JIANG Lingmei1*,  
  • Affiliation：

    National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China

    Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China

WU Shengli23,  
  • Affiliation：

    Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

LUAN Yinghong4,  
  • Affiliation：

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

PAN Jinmei5,  
  • Affiliation：

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

SHI Jiancheng5

résumé

Spaceborne passive microwave remote sensing is a crucial technique for monitoring the global spatiotemporal distribution of snow depth. The forest canopy not only attenuates microwave radiation from the soil but also emits radiation into the sensor. Therefore, forest canopies increase the uncertainty of snow depth retrievals via passive microwave sensing. This research aimed to develop a microwave transmissivity model at the scale of satellite observations (0.25°×0.25°) to realize forest correction via satellite observations. The proposed novel method (hereafter referred to as the adjacent pixel approach) for estimating canopy transmissivity combines the radiative transfer functions of adjacent forests and open pixels. A semi-empirical transmissivity model based on forest biomass was built to correct satellite-observed brightness temperatures. The modeling brightness temperature data were compared with the AMSR2 observations in Northeast China to demonstrate the ability of the proposed transmissivity model to retrieve snow depth.As forest canopy effects were ignored by the microwave emission model, the brightness temperature was somewhat underestimated with respect to the satellite observations. By contrast, the proposed method corrected the information by using AMSR2 observations; hence, the model simulations were much closer to the AMSR2 observations. Then, the proposed semi-empirical microwave transmissivity model was further verified via the leave-one-out cross-validation method. The correlation coefficient between the estimates and reference values reached 0.7, and the RMSEs were 0.0589 and 0.0787 at 18.7 GHz and 36.5 GHz, respectively. The relationship between the brightness temperature spectral difference (Tb18.7V - Tb36.5V) and ground-based snow depth improved after forest correction, from 0.26 before correction to 0.46 after correction. An empirical retrieval algorithm was subsequently selected for testing to demonstrate the improvement in snow depth retrieval via forest radiation correction. The RMSE was 7.8 cm with forest radiation correction, whereas it was 8.9 cm without correction. Moreover, the correlation coefficient increased from 0.32 to 0.49.The proposed semi-empirical transmissivity method can significantly improve the performance of microwave radiative transfer models in forested areas. Moreover, this method can directly correct satellite-based brightness temperatures, thereby reducing the uncertainty of estimated snow depth values. This study provides a reference and guideline for improving snow depth under forest canopies.

mots-clés

passive microwave remote sensing;forest canopy;adjacent pixel approach;microwave transmissivity;snow depth

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