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On the Use of COSMO-SkyMed X-Band SAR for Estimating Snow Water Equivalent in Alpine Areas: A Retrieval Approach Based on Machine Learning and Snow Models
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2022-07-15 , DOI: 10.1109/tgrs.2022.3191409
Emanuele Santi 1 , Ludovica De Gregorio 2 , Simone Pettinato 1 , Giovanni Cuozzo 2 , Alexander Jacob 2 , Claudia Notarnicola 2 , Daniel Gunther 3 , Ulrich Strasser 4 , Francesca Cigna 5 , Deodato Tapete 5 , Simonetta Paloscia 1
Affiliation  

This study aims at estimating the dry snow water equivalent (SWE) by using X-band synthetic aperture radar (SAR) data from the COSMO-SkyMed (CSK) satellite constellation. The time series of CSK acquisitions have been collected during the dry snow period in the Alto Adige test site, in the Italian Alps, during the winter seasons from 2013 to 2015 and from 2019 to 2021. The SAR data have been analyzed and compared with the in situ measurements to understand the X-band SAR sensitivity to SWE, which has been further assessed by dense media radiative transfer (DMRT) model simulations. The sensitivity analysis provided the basis for addressing the SWE retrieval from the CSK data, by exploiting two different machine learning (ML) techniques, namely, artificial neural networks (ANNs) and support vector regression (SVR). To ensure statistical independence of training and validation processes, the algorithms are trained and tested using SWE predictions of the fully distributed snow model AMUNDSEN as reference data and are subsequently validated on the experimental dataset. Due to its influence on the CSK estimates, the effect of forest canopy was accounted for in the analysis. Depending on the algorithm, the validation resulted in a correlation coefficient $0.78\le R \le0.91$ and a root-mean-square error (RMSE) 55.5 mm $\le $ RMSE $\le87.4$ mm between estimated and in situ SWE. Further analysis and validation are needed; however, the obtained results seem suggesting the CSK constellation as an effective tool for the retrieval of the dry SWE in alpine areas.

中文翻译:

使用 COSMO-SkyMed X 波段 SAR 估计高山地区雪水当量:基于机器学习和雪模型的检索方法

本研究旨在通过使用来自 COSMO-SkyMed (CSK) 卫星星座的 X 波段合成孔径雷达 (SAR) 数据来估计干雪水当量 (SWE)。收集了上阿迪杰试验场干雪期、意大利阿尔卑斯山、2013 年至 2015 年和 2019 年至 2021 年冬季期间 CSK 采集的时间序列。对 SAR 数据进行了分析并与原位测量以了解 X 波段 SAR 对 SWE 的敏感性,这已通过密集介质辐射传输 (DMRT) 模型模拟进行了进一步评估。敏感性分析通过利用两种不同的机器学习 (ML) 技术,即人工神经网络 (ANN) 和支持向量回归 (SVR),为解决 CSK 数据中的 SWE 检索提供了基础。为了确保训练和验证过程的统计独立性,算法使用完全分布式雪模型 AMUNDSEN 的 SWE 预测作为参考数据进行训练和测试,随后在实验数据集上进行验证。由于其对 CSK 估计的影响,分析中考虑了森林冠层的影响。根据算法,验证产生相关系数 $0.78\le R \le0.91$和均方根误差 (RMSE) 55.5 mm $\乐$均方根误差 $\le87.4$mm 在估计和原位SWE 之间。需要进一步分析和验证;然而,获得的结果似乎表明 CSK 星座是在高山地区反演干燥 SWE 的有效工具。
更新日期:2022-07-15
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