Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Feb 18;22(4):1611. doi: 10.3390/s22041611.
Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.
土壤湿度含量（SMC）在地球科学研究中起着至关重要的作用。可以使用基于遥感数据的人工神经网络（ANN）来获取 SMC。用于 ANN 训练和测试的样本数量和质量是影响 SMC 检索结果的两个关键因素。本研究重点关注样本数量和质量的优化。一方面，开发了稀疏样本开发（SSE）方法来解决光学图像中云层遮挡和原位 SMC 测量仪器故障导致的样本稀缺问题。有了这个方法，通常在传统方法中排除的数据可以充分利用。另一方面，除了之前研究中通常讨论的基本输入参数外，还优化了几个新参数来改善特征描述。本研究采用 Sentinel-1 SAR 和 Landsat-8 图像来获取奥地利东部研究区域的 SMC。通过 SSE 方法，ANN 训练和测试的可用样本数量从 264 个增加到 635 个，检索精度可以显著提高。此外，优化后的参数也提高了反演效果，海拔是最具影响力的输入参数。
