Correction: Zhao et al. Validating Data Interpolation Empirical Orthogonal Functions (DINEOF+) Interpolated Soil Moisture Data in the Contiguous United States. Agriculture 2025, 15, 1212

Title Correction

In the original publication [1], title missed the abbreviation “DINEOF+” for Data Interpolation Empirical Orthogonal Functions. The authors would like to add this abbreviation to fully explain the title.

Missing Citation

In the original publication [1], reference 34 “[34] Zhao, H.; Gao, F.; Anderson, M.; Cirone, R.; Chang, J.; Lin, L.; Zhang, C.; Li, H.; Zhao, H. Phenologically corrected crop condition mapping and assessment with vegetation index time series. In Proceedings of the 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Novi Sad, Serbia, 15–18 July 2024.” was not cited. The citation has now been inserted in the Introduction, Paragraph 4 to replace the original reference 34, and should read as follows:

Data Interpolating Empirical Orthogonal Functions (DINEOF) is a variant of a learning algorithm that uses matrix factorization to deal with missing data problems [27]. Compared to Kriging methods, it has the advantage of leveraging both spatial and temporal correlation between data points and requires no parameters or a priori information. It has been proven to be an effective tool for reconstructing missing values in geophysical datasets [28–32]. In a recent study, Zhao et al. [33] applied an enhanced version of DINEOF (DINEOF+) to recover missing data in the SM product. Their results demonstrate that DINEOF+ can effectively fill gaps in the daily SM data. However, they also found significant biases between interpolated and in situ measurements, without providing further analysis. Evaluating and validating soil moisture products is essential before utilizing them in supporting agricultural decision making and climate research [34]. In this study, we aim to provide a more comprehensive validation of DINEOF+-interpolated SM using both the original and in situ measurements. Our analysis is based on the 1 km THySM-based SMAP dataset over the CONUS, which includes the 48 adjoining U.S. states and the District of Columbia, excluding Alaska and Hawaii.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.