用于土壤湿度反演的极化SAR模型拟合：芬兰异质矿山环境中PALSAR-2数据研究
Polarimetric SAR Model Fitting for Soil Moisture Retrieval: Study of PALSAR-2 data over a Heterogeneous Mine Environment in Finland
AI总结：
研究基于ALOS-2 PALSAR-2四极化数据，通过半经验模型结合时间序列土壤湿度指数（SMI）和极化参数，在芬兰石灰岩采石场实现土壤湿度反演，最佳配置R²=0.67，RMSE=5.65 vol%。
摘要：
本文研究了基于模型的几种方法，用于从ALOS-2 PALSAR-2四极化图像中反演地表土壤湿度，研究区域位于芬兰东南部的一个石灰岩采石场。研究主要针对物理可解释的半经验建模方法，并以通用机器学习建模作为基准。除了常见的极化观测量，我们提出了一种基于SAR时间序列的TU Wien土壤湿度指数（SMI）反演的泛化方法，该方法在由极化相干矩阵$[T3]$导出的多个表示空间中进行了检验。本研究在一个封闭的尾矿储存设施和垃圾填埋场进行，使用了9幅重复轨道的PALSAR-2图像。结合时间上下文SMI和当前观测PolSAR参数的最佳半经验配置实现了$R^2=0.67$和RMSE $=5.65$体积百分比单位。最强的$SMI_{[T3]}$方法结合沉积物特定校准，实现了$R^2=0.66$和RMSE $=5.67$ vol.%，明显优于使用$SMI_{HH}$或$SMI_{VV}$。所提出的方法对表示形式敏感：基于dB的投影优于线性或迹归一化的$[T3]$表示。考虑沉积物信息相比全局模型拟合显著提高了反演性能。机器学习结果接近但未超越半经验模型方法。同样，它们强调了沉积物特定建模的必要性以及在土壤湿度反演中包含时间序列/时间后向散射动态的重要性。我们的研究证明了在相对稀缺的参考数据条件下，基于物理的土壤湿度反演方法在复杂多沉积物矿山环境中的实用性。
英文摘要：
This paper examines several model based approaches for retrieving surface soil moisture from ALOS-2 PALSAR-2 quad-pol imagery, over a lime stone quarry in southeastern Finland. The study primarily targets physically interpretable semi-empirical modeling approaches, with generic ML modeling used as a benchmark. Along with common polarimetric observables, we propose a generalization of the SAR time series based TU Wien soil moisture index (SMI) retrievals examined across several representational spaces derived from polarimetric coherency matrix $[T3]$. This study was conducted over a closed tailing storage facility and a landfill, with a set of 9 repeat pass PALSAR-2 images. The best semi-empirical configuration combining temporal context SMI and current observation PolSAR parameters achieved $R^2=0.67$ and RMSE $=5.65$ volumetric \% units. The strongest $SMI_{[T3]}$ approach with sediment-specific calibration, achieved $R^2=0.66$ and RMSE $=5.67$ vol. \%, which was considerably better than using $SMI_{HH}$ or $SMI_{VV}$. The proposed approach was sensitive to representations: dB-based projection outperformed linear or trace-normalized $[T3]$ representation. Factoring in sediment information dramatically improved retrieval performance compared to using global model fitting. Machine learning results closely approached but not outperformed semi-empirical model based methodologies. Similarly, they highlighted the need for sediment-specific modeling as well as the importance of including time-series/temporal backscatter dynamics during SSM retrieval. Our study demonstrated the utility of physics based SSM retrieval approaches in the complex multi-sediment mine environment under relatively scarce reference data conditions.
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