the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An Accurate 10 m Annual Crop Map Product of Maize and Soybean Across the United States
Abstract. High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022, available at the Global Land Analysis and Discovery at the University of Maryland (https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95 % with standard errors of less than 1 %. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91 % and 84 % across the years, and UAs and PAs for soybean were greater than 88 % and 82 %, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1 % to 10 %, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 14 % and 16 %, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales. The dataset is also available at https://doi.org/10.6084/m9.figshare.28934993.v1 (Li et al., 2025).
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Status: open (until 23 Sep 2025)
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EC1: 'Comment on essd-2025-361', Peng Zhu, 18 Jul 2025
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Comments: This link does not work: https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states. I thought it would be important to address this issues
Please resolve it.
Citation: https://doi.org/10.5194/essd-2025-361-EC1 -
AC1: 'Reply on EC1', Haijun Li, 18 Jul 2025
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The website was experiencing an upgrade. We resolved this issue, and the link works now: https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states
Citation: https://doi.org/10.5194/essd-2025-361-AC1
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AC1: 'Reply on EC1', Haijun Li, 18 Jul 2025
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RC1: 'Comment on essd-2025-361', Anonymous Referee #1, 12 Aug 2025
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I have reviewed the paper, and my primary concern is whether the data make sense. National-scale crop type mapping is essentially an operational task, and conducting it at this scale poses significant challenges for a scientific team. I cannot recommend publication in ESSD before all my concerns are thoroughly addressed.
1. Over the past two decades, numerous studies have been published on maize and soybean mapping in the United States, and CDL data (including all major crop types) have been available for more than 15 years. In contrast, the dataset presented here includes only maize and soybean. Given that cropland in the U.S. is characterized by large field sizes, the higher spatial resolution offered by this dataset does not appear to meaningfully improve national-level crop area statistics. It is unclear who the intended users of this dataset are. Do the authors envision USDA adopting it to replace the current CDL for NASS statistics? Are there specific states or counties that would use it for their own statistical work? For maize/soybean yield forecasting, which national projects would benefit from this dataset? For climate change analysis, does this 10 m maize/soybean map outperform the existing CDL products? From my perspective, the dataset would be more compelling if it were a 10 m crop type map covering all crops in the U.S., or if it provided 10 m major crop type maps for underrepresented regions such as Africa.
2. Line 122~142, Sentinel 2 Level 2A data is Surface Reflectance data, I wonder whether the author know what is "TOA reflectance" and what is "SR"! And I wonder whether the author understand the Sentinel-2 ARD pre-processing workflow and know what is the input.
3. Section 2.3: The samples are used to identify maize and soybean in the PSUs, and the resulting maize/soybean maps within the PSUs are then used to identify these crops at the national level. I find this approach questionable, particularly given that the study focuses on only two crop types. It is unclear how well this method would perform if all crop types in the U.S. were included. Moreover, any misclassification in the PSU-level mapping is likely to be amplified when scaling up to the national level. A potentially more robust solution would be to use high-resolution drone imagery in place of Sentinel data for maize/soybean mapping within the PSUs.
4. Regarding the selection of training data, please demonstrate its representativeness by providing examples of typical vegetation index (VI) time series and corresponding meteorological data.
5. Validation data collection (Table S2): For each year, only 90 PSUs are used, and validation samples are collected solely from these PSUs. This sample size and distribution are not sufficiently representative. I recommend selecting at least 10 samples per PSU and including a minimum of 300 PSUs per year. The same PSUs could be used across multiple years, and the authors could consider contacting and collaborating with farmers to facilitate this process.
6. Figure 9, The comparison with CDL data does not make sense because the CDL result is the maize/soybean from all crop-type map, and the 10m map is just a maize/soybean map. Please conduct all crop type crop type mapping to make it comparable.
7. The mixed-pixel analysis (Figures 12 and 13) does not make sense either, If more crop types were considered, the number of mixed maize pixels would likely increase. Therefore, I recommend first producing a 10 m resolution map for all crop types. Additionally, for this scale factor analysis, please include CDL data in the comparison to provide a more complete evaluation.
Citation: https://doi.org/10.5194/essd-2025-361-RC1
Data sets
2019-2022 10-m maize and soybean maps over the United States Haijun Li, Xiao-peng Song, Bernard Adusei, Jeffrey Pickering, Andre de Lima, Andrew Poulson, Antoine Baggett, Peter Potapov, Ahmad Khan, Viviana Zalles, Andres Hernandez-Serna, Samuel M. Jantz, Amy H. Pickens, Carolina Ortiz-Dominguez, Xinyuan Li, Theodore Kerr, Zhen Song, Svetlana Turubanova, Eddy Bongwele, Heritier Koy Kondjo, Anna Komarova, Stephen V. Stehman, Matthew C. Hansen https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states
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