Alexakis Dimitrios D, Mexis Filippos-Dimitrios K, Vozinaki Anthi-Eirini K, Daliakopoulos Ioannis N, Tsanis Ioannis K
School of Environmental Engineering, Technical University of Crete, Chania 73100, Greece.
Sensors (Basel). 2017 Jun 21;17(6):1455. doi: 10.3390/s17061455.
A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R² values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.
提出了一种用于处理多时相哨兵 - 1和陆地卫星8号卫星图像以估算表层土壤湿度含量（SMC）以支持水文模拟研究的方法。在对遥感数据进行预处理后，评估后向散射系数、归一化植被指数（NDVI）、热红外温度和入射角参数推断5厘米深度处地面SMC测量值的潜力。测试了一种使用人工神经网络（ANN）的非线性方法。该方法应用于希腊克里特岛西部，2015年在那里部署了一个SMC测量网络。使用留一法交叉验证和敏感性分析评估所提出算法的性能。人工神经网络在SMC估算中被证明是最有效的，R²值在0.7到0.9之间。所提出的方法用于支持使用HEC - HMS模型进行的水文模拟，该模型应用于未测量SMC的凯拉米亚诺斯流域。结果和模型敏感性突出了结合哨兵 - 1合成孔径雷达（SAR）和陆地卫星8号图像对改善SMC估算和支持水文研究的贡献。
