Location: Hydrology and Remote Sensing LaboratoryTitle: Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China
|ZHENG, J. - Hohai University|
|ZHAO, T. - Chinese Academy Of Sciences|
|LU, H. - Tsinghua University|
|SHI, J.C. - Chinese Academy Of Sciences|
|JI, D. - Chinese Academy Of Sciences|
|JIANG, L. - Beijing Normal University|
|CUI, Q. - Collaborator|
|LU, H. - Tsinghua University|
|YANG, K. - Tsinghua University|
|WIGNERON, J. - Collaborator|
|LI, X. - Collaborator|
|ZHU, Y. - Hohai University|
|HU, L. - Nanjing University|
|WANG, X. - Hohai University|
|KERN, S. - University Of Hamburg|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/3/2022
Publication Date: 1/13/2022
Citation: Zheng, J., Zhao, T., Lu, H., Shi, J., Cosh, M.H., Ji, D., Jiang, L., Cui, Q., Lu, H., Yang, K., Wigneron, J., Li, X., Zhu, Y., Hu, L., Wang, X., Kern, S. 2022. Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China. Remote Sensing of Environment. 271:112891. https://doi.org/10.1016/j.rse.2022.112891.
Interpretive Summary: A network was developed to evaluate soil moisture products in the Shandian River Basin in northern China. A total of 34 sites were deployed and compared to 22 different soil moisture datasets from a variety of satellites, models, and merged products. Triple colocation analysis was also employed to test the efficiency and capability of that analysis. We determined that low frequency satellite products performed the best with respect to this network. This network and study provide a valuable reference point for future product development.
Technical Abstract: From evaluation of soil moisture (SM) products, one can better understand its limitations and error characteristics, and provide insights into the product application and algorithm improvement. A new soil moisture and temperature monitoring network (the SDR network) was established within the Shandian River Basin in 2018, which is located in a semiarid area of northern China and consists of 34 observation sites. In this study, in-situ measurements of the SDR network were used to evaluate 22 different SM data sets, including three categories: (1) the single-sensor retrieved SM, (2) multi-sensor merged SM, and (3) model-based SM. Moreover, triple collocation (TC) analysis was applied to all possible triplets to verify if the basic assumptions are met. We also investigated the impact of different factors influencing the accuracy of SM products within the SDR network, including the local acquisition time, physical surface temperature, and vegetation optical depth. The results reveal that the model-based products have the best performance concerning the in-situ SM of the SDR network. The low frequency passive microwave SMAP products have the lowest unbiased root-mean-squared error (ubRMSE) among all the single-sensor retrieved SM products, and it is found SMAP MDCA is slightly better than the SCA-V algorithm. The smaller vegetation optical depth (underestimated vegetation effects) may be the major factor causing the dry bias of the SMAP Soil Moisture Product. We also discovered that TC-based metrics may vary considerably if using different triplets. In other words, even though the most conservative TC triplets were employed, TC assumptions were inevitably violated. Redundant TC estimates from multiple independent triples could be averaged to increasing the accuracy of final TC estimates. It is found that the TC analysis may overestimate the correlation and underestimate the ubRMSE of soil moisture products compared with ground-based metrics. This study is the first to use in-situ measurements from the SDR network to conduct a comprehensive evaluation of commonly used, multi-source SM products. Results are expected to further promote the improvement of satellite- and model-based SM products.