An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data
An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data
复制标题
结合 Sentinel-1 SAR 和 MODIS 数据降尺度 SMAP 土壤湿度的方法
DOI：
10.3390/rs11232736
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发表时间：
2019-12-01
期刊：
影响因子：
5
通讯作者：
Meng, Lingkui
中科院分区：
文献类型：
--
作者：
Bai, Jueying;Cui, Qian;Meng, Lingkui
提出了一种通过将光学/红外数据与基于随机森林（RF）模型的合成孔径雷达（SAR）数据相结合来生成缩小尺度土壤湿度主动被动（SMAP）土壤湿度（SM）数据的方法。该方法利用主动微波对地表 SM 的敏感性以及植被指数 (VI)、地表温度 (LST) 和 SM 之间的三角形/梯形特征空间。首先，对五种 RF 架构 (RF1–RF5) 在 9 公里处进行训练和测试。其次，对 RF1-RF5 进行了比较，并根据原位 SM 测量进行了评估。第三，使用原位 SM 测量在 3 公里和 1 公里处对两种 SMAP-Sentinel 主动-被动 SM 产品进行了比较。第四，RF5模型仿真与基于可选算法的SMAP L2_SM_SP产品在3公里和1公里分辨率下进行了比较。结果表明，基于光学/红外数据和垂直-垂直（VV）偏振后向散射协同使用的缩小尺度SM在植被覆盖相对较低的半干旱地区是可行的。具有反向散射和来自光学/红外数据的更多参数的 RF5 模型在五个 RF 模型中表现最好，并且在 3 公里和 1 公里处都令人满意。与L2_SM_SP相比，RF5在1公里上更优越。按重要性降序排列的输入变量是整个研究区域的后向散射、LST、VI 和地形因素。低植被覆盖条件可能放大了反向散射和地表温度的重要性。足够数量的VI可以增强RF模型对不同植被条件的适应性。
A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1–RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1–RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active–passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical–vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.