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      A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

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          Abstract

          Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013–2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R 2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis.

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          Random Forests

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            A Threshold Selection Method from Gray-Level Histograms

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              Efficient Machine Learning for Big Data: A Review

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                Author and article information

                Contributors
                suwei@cau.edu.cn
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                16 February 2024
                16 February 2024
                2024
                : 11
                : 216
                Affiliations
                [1 ]College of Land Science and Technology, China Agricultural University, ( https://ror.org/04v3ywz14) Beijing, 100083 China
                [2 ]Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, ( https://ror.org/05ckt8b96) Beijing, 100083 China
                Author information
                http://orcid.org/0000-0003-0341-1983
                http://orcid.org/0000-0002-6942-0746
                Article
                2998
                10.1038/s41597-024-02998-7
                10873423
                38365784
                badd7519-1256-45eb-83ae-ba4cddbeb011
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 September 2023
                : 25 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 42171331
                Award Recipient :
                Funded by: 2115 Talent Development Program of China Agricultural University
                Categories
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                © Springer Nature Limited 2024

                carbon cycle,sustainability
                carbon cycle, sustainability

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