Early Crop Identification Study Based on Sentinel-1/2 Images with Feature Optimization Strategy

Jiansong Luo, Min Xie, Qiang Wu, Jun Luo, Qi Gao, Xuezhi Shao and Yongping Zhang ()
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Jiansong Luo: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China
Min Xie: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China
Qiang Wu: College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
Jun Luo: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China
Qi Gao: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China
Xuezhi Shao: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China
Yongping Zhang: College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China

Agriculture, 2024, vol. 14, issue 7, 1-28

Abstract: The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region.

Keywords: Sentinel-1/2; google earth engine; crop mapping; Hetao Irrigation District; random forest model; model transfer (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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