This paper proposed an object-based crop type ground truth dataset from 2013 to 2023, called “CropSight-US”, using street view and Sentinel-2 images. This dataset covered 17 major crop types across 294 Agricultural Statistics Districts (ASDs) in CONUS. Specifically, crop type labels were extracted from Google street view images and field boundary information was derived from Sentinel-2 imagery. CONUS-UncertainFusionNet was developed to generate this dataset with uncertainty information. In the experimental results, the performance of several deep learning-based networks was compared. This paper is well-organized and the reviewer has the following detailed comments:

1. This paper included that “CropSight-US, the first national scale, object-based crop type ground truth dataset”. The authors were encouraged to review the existing products to highlight this contribution.
2. This article contains multiple instances of the full name and abbreviation for CONUS. It is recommended to provide the full name only upon first mention, using the abbreviation thereafter.
3. For line 138 and 655, these links were invalid. Please double check all the links in the manuscript.
4. What are the advantages of using Sentinel-2 over NAIP imagery?
5. What is the experimental environment of ViT-B16 and ResNet-50 training, such as GPU and CPU? The authors are suggested to offer more details about the setting of the proposed network.
6. For all the tables, it is recommended to display the best performance in bold to help readers better follow.
7. SAM is trained on high-resolution natural images, and Sentinel-2 has a lower resolution. How to overcome this resolution shift?
8. For Figure 6, there are some image patches after image embedding. Do they refer to image embeddings? The authors are suggested to check this as image patches are not equal to image embeddings.
9. For line 353, there is an error regarding reference “(Error! Reference source not found.).”.
10. How to choose reasonable thresholds for entropy and variance?

This manuscript introduces CropSight-US, a national-scale, object-based crop type ground truth dataset for the contiguous United States (2013–2023), derived from Google Street View imagery and Sentinel-2–based field boundary delineation. The dataset is novel in its integration of street-level imagery at national scale and in its object-level design with uncertainty metrics.However, several fundamental aspects of dataset representativeness, temporal consistency, and uncertainty interpretation remain insufficiently documented, limiting confidence in its general applicability.

Specific Comments

1.All samples are derived from road-accessible GSV imagery, yet the manuscript provides no quantitative assessment of resulting spatial bias. Without statistics on distance-to-road coverage or cropland representativeness at the ASD level, it is difficult to evaluate how well CropSight-US captures agricultural landscapes away from road networks.

2.The selection of Sentinel-2 (or NAIP) imagery “closest in time” to GSV acquisition is not quantified. The manuscript should report typical and maximum temporal offsets and discuss potential impacts on both crop labeling and boundary delineation.

3.The manuscript places strong emphasis on uncertainty as a key advantage of the dataset. However, it remains unclear how users are expected to interpret and use the provided uncertainty metrics in practice. In particular, it is not specified whether the reported entropy and variance values are directly comparable across crop types, or whether they should be interpreted as crop-specific relative measures. Without such clarification, the practical value of the uncertainty information for downstream applications is difficult to assess.

4.The authors group wheat, rice, and other small-grain cereals into a single “cereal” class based on their visual similarity in street-level imagery. While this decision is understandable from an engineering and labeling perspective, it introduces substantial risks at the application level that are not sufficiently discussed. In particular, rice paddies are typically characterized by surface flooding and distinct water management practices, whereas wheat is associated with fundamentally different hydrological and energy conditions. This raises the concern that water-related signals may be implicitly mixed into the “cereal” class, potentially degrading the reliability of rice-related samples. The manuscript should more explicitly discuss these implications and the limitations they impose on crop-specific analyses.

Technical Issues

Sect. 3.1.3 contains an unresolved reference (“Error! Reference source not found.”).