利用合成孔径雷达数据，提升矿区复杂地表土壤湿度反演精度。
Polarimetric SAR Model Fitting for Soil Moisture Retrieval: Study of PALSAR-2 data over a Heterogeneous Mine Environment in Finland
- 结合时序与极化参数，改进了基于物理模型的土壤湿度反演方法。
- 最优模型达到决定系数0.67，均方根误差5.65体积百分比单位。
- 引入沉积物信息和时序动态，显著提升复杂矿区反演效果。
本文研究了利用ALOS-2 PALSAR-2全极化影像在芬兰东南部石灰石矿坑区域反演地表土壤湿度的多种建模方法。重点评估了具有物理可解释性的半经验模型，以通用机器学习模型为基准。除常规极化观测外，提出了一种基于时间序列的TU Wien土壤湿度指数（SMI）在极化相干矩阵$[T3]$衍生表示空间中的推广方法。实验覆盖一个封闭尾矿库和垃圾填埋场，共使用9次重复过境的PALSAR-2影像。最佳半经验配置（结合时序上下文SMI与当前极化参数）获得$R^2=0.67$，RMSE $=5.65$ vol. %。针对沉积物特异性校准的最强$SMI_{[T3]}$方法达到$R^2=0.66$，RMSE $=5.67$ vol. %，显著优于使用$SMI_{HH}$或$SMI_{VV}$。该方法对表示形式敏感：分贝投影优于线性或迹归一化$[T3]$表示。引入沉积物信息后性能大幅提高，优于全局模型拟合。机器学习结果接近但未超越半经验模型；同时强调了沉积物特异性建模及时序后向散射动态的重要性。研究表明，在参考数据有限条件下，物理驱动的土壤湿度反演方法在多沉积物矿区仍具实用价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。