Gap Filling for ISMN Time Series Using CYGNSS Data

Qingyun Yan;Mingbo Hu;Shuanggen Jin;Weimin Huang
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Abstract

This study introduces a method for filling the data gaps in the International Soil Moisture Network (ISMN) by soil moisture (SM) estimated using data from the Cyclone Global Navigation Satellite System (CYGNSS). The estimation process leverages the random forest (RF) algorithm, incorporating CYGNSS-derived products along with soil and surface parameters as input features. This research was conducted based on the daily SM data from the ISMN for the entire years of 2019 and 2020, which served as training and test datasets. Comparison experiments were performed to highlight the limitations of existing methods and SM products for gap filling in ISMN SM data. Subsequently, the optimal retrieval model was deployed to estimate SM for the duration of the study, thereby filling the gaps within the ISMN dataset. The SM results after gap filling showed strong consistency with measured SM, achieving an R-squared ( $R^{2}$ ) of 0.7930 and a root-mean-square error (RMSE) of 0.0492 cm3/cm3. These results indicate that CYGNSS-based SM inversion is a promising approach to enhance the completeness of the ISMN dataset.
利用CYGNSS数据对ISMN时间序列进行间隙填充
本文介绍了一种利用气旋全球导航卫星系统(CYGNSS)数据估算土壤湿度(SM)来填补国际土壤湿度网络(ISMN)数据空白的方法。估计过程利用随机森林(RF)算法,结合cygnss衍生产品以及土壤和地表参数作为输入特征。本研究以ISMN 2019年和2020年全年的日常SM数据为基础,作为训练和测试数据集。通过对比实验,突出了现有方法和SM产品在ISMN SM数据缺口填充方面的局限性。随后,部署最优检索模型来估计研究期间的SM,从而填补ISMN数据集内的空白。填隙后的SM结果与实测SM具有较强的一致性,R平方($R^{2}$)为0.7930,均方根误差(RMSE)为0.0492 cm3/cm3。这些结果表明,基于cygnss的SM反演是提高ISMN数据完整性的一种有前景的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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