资源简介

摘要： 基于Sentinel-1合成孔径雷达 (SAR) 数据及相同时段的中分辨率成像光谱仪（MODIS）和Landsat 8两种归一化植被指数（NDVI），构建变化检测模型以估算黑河中游的高分辨率土壤水分，并探讨模型中具体参数设置对估算精度的影响。结果表明：①在对后向散射系数时间序列的差值 ( Δ σ ) 和植被指数 ( V I ) 进行线性建模过程中，MODIS NDVI和Landsat 8 NDVI这两种植被产品所构建的模型在 Δ σ - V I 空间中所选取的采样点比例分别为2%和4%时，各自取得最优精度; ②以土壤水分反演为目标，使用Landsat 8 NDVI构建的变化检测模型略优于使用MODIS NDVI构建的变化检测模型，两种模型的均方根误差RMSE分别为0.040 m3/m3和0.044 m3/m3，相关系数R分别为0.86和0.83; ③对于变化检测方法的关键参数，若使用低分辨率的SMAP/Sentinel-1 L2_SM_SP土壤水分数据分别代替站点观测的土壤水分初始值和缩放因子 (即两个连续时相土壤水分变化的最大值 Δ M s m a x ) 这两个参数，则土壤水分RMSE将分别增加0.01 m3/m3和0.04 m3/m3。即土壤水分缩放因子这一参数的误差对反演结果的影响大于土壤水分初始值误差对反演结果的影响，故采用高精度的缩放因子进行变化检测估算。研究结论对于利用新兴的Sentinel-1 SAR数据，通过变化检测算法准确获取高分辨率土壤水分信息具有实际参考价值。 关键词： 黑河流域;  土壤水分;  变化检测;  Sentinel-1     Abstract: In this study, a change detection model, constructed using the Sentinel-1 Synthetic Aperture Radar (SAR) data and the simultaneous Normalized Difference Vegetation Index (NDVI) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 sensors, is applied to estimate soil moisture in middle reaches of the Heihe River Basin, and the effects of two key parameters on retrieval accuracy are comprehensively investigated. The results show that: (1) when constructing the empirical relationship between backscattering coefficient difference ( Δ σ ) and Vegetation Index (VI) required by change detection model, the optimal sampling ratios in the ( Δ σ - V I ) space are approximately 2% and 4% for MODIS NDVI and Landsat 8 NDVI, respectively; (2) the Landsat 8 NDVI-based change detection model slightly outperforms the MODIS NDVI-based model in soil moisture retrieval accuracy, with Root Mean Square Error(RMSE) of 0.040 m3/m3 and 0.044 m3/m3respectively; (3) for the key parameters of the change detection method, replacing the ground-based initial soil moisture and scaling factor (maximum soil moisture difference between two adjacent dates Δ M s m a x ) by the low-resolution SMAP/Sentinel-1 L2_SM_SP data will increase the RMSE by 0.01 m3/m3 and 0.04 m3/m3 respectively. Comparing to the parameter of initial soil moisture, the error in soil moisture scaling factor will lead to more significant degradation in the performance of the change detection method, thus it is recommended to use the high precision scaling factor for soil moisture estimation. This study confirms the promising potential of Sentinel-1 data for retrieving high-resolution soil moisture via change detection method and provides practical insight into its application.

相关资源

1. 水质遥感监测的关键要素叶绿素a的反演算法研究进展
2. 基于无人机遥感技术的森林参数获取研究进展
3. 基于无线传感器网络的森林生态系统观测试验平台构建
4. 基于无人机激光雷达的森林冠层高度分析
5. 多源时序国产卫星影像的森林火灾动态监测
6. 基于无人机遥感技术的台风灾害倒伏绿化树木检测

下载说明

·如果您发现该资源不能下载，请通知管理员.gissky@gmail.com

·为确保下载的资源能正常使用，请使用[WinRAR v3.8]或以上版本解压本站资源,缺省解压密码www.gissky.net ，如果是压缩文件为分卷多文件，请依次下载每一个文件，并按照顺序命名为1.rar,2.rar,3.rar...，然后鼠标右击1.rar解压.

·为了保证您快速的下载速度，我们推荐您使用[网际快车]等专业工具下载.

·站内提供的资源纯属学习交流之用,如侵犯您的版权请与我们联系.