Cross-Regional Crop Classification Based on Sentinel-2

Author:

He Jie1,Zeng Wenzhi12ORCID,Ao Chang1,Xing Weimin3,Gaiser Thomas4ORCID,Srivastava Amit Kumar45ORCID

Affiliation:

1. State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China

2. College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China

3. College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China

4. Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany

5. Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg, Germany

Abstract

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Funder

Key Research and Development Program of Heilongjiang Province

Program of the National Natural Science Foundation of China

Publisher

MDPI AG

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