Winter wheat is one of the most crucial food crops in the world. North China Plain has also been one of the largest planting regions in China. It is vital to accurately monitor the spatiotemporal pattern of winter wheat planting area, in order to predict the grain yield for the national food security. Many previous studies have concentrated on the planting area of winter wheat over only a few years. It is still lacking in dynamic monitoring in the long term. The recently released products of large-scale winter wheat often suffer from coarse spatial resolution or limited temporal coverage. Furthermore, there are significant disparities in the areas, spatial patterns, and dynamics of winter wheat planting areas, as indicated by various remote sensing products. Therefore, it is very necessary to explore the spatial and temporal evolution of the winter wheat planting area. Taking the North China Plain as the research area, this study aims to develop the dynamic-thresholding decision tree classification, according to the thematic maps and the phenological characteristics of winter wheat. Crop phenology was also characterized under diverse climate conditions and years. The static thresholds were then reduced to dynamically calculate the relative phenological changes in the greening and browning periods per year. Land use maps were utilized to identify the potential training samples for the subsequent classification. A field test was finally carried out to monitor the dynamics of winter wheat planting areas in the North China Plain from 2003 to 2022. The results show that: 1) high accuracy was achieved in extracting the winter wheat planting areas, with a multi-year mean overall accuracy of 93.44% and strong alignment with statistical data. Notably, the accurate delineation was realized in more fragments, such as Beijing, Tianjin, and southern Henan, compared with the rest products. 2) The winter wheat planting area overall increased by 23% over the past 20 years. Moreover, there was a great variation in the space and time of winter wheat planting areas at a grid scale of 5 km×5 km. A consistent decrease was found in some regions, including the west part of Henan Province and the central-west part of Hebei Province. The continuous increase was in the areas like the eastern part of Shandong Province and the central-eastern part of Hebei Province. The planting area increased significantly after increasing in the remaining regions. 3) The continuously-planting areas of winter wheat only accounted for 5% of the total planting area (defined as the land with winter wheat planting in one or more years) in the study period. Planting times of less than 10 years were observed in 55% of the total, indicating a potential widespread occurrence of cropland fallow and abandonment. 4) Winter wheat planting areas also remained relatively stable in the central and southern parts of Hebei Province, the western part of Shandong Province, and the central-northern part of Henan Province. Conversely, frequent changes were found in the Beijing-Tianjin-Hebei urban clusters and the mountainous areas. Therefore, a novel approach was introduced to monitor the long-term planting areas of winter wheat on a large scale. The findings can provide a strong reference to better understand the spatiotemporal evolution of winter wheat planting areas in the North China Plain.
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Accurate monitoring of rice planting areas is vital for ensuring national food security, evaluating greenhouse gas emissions, optimizing water resource allocation, and maintaining agricultural ecosystems. In recent years, the integration of remote sensing technologies—particularly the fusion of optical and synthetic aperture radar (SAR) data—has significantly enhanced the capacity to monitor crop distribution, even under challenging weather conditions. However, many current studies still rely heavily on phenological features captured at specific key stages, such as the transplanting phase, while overlooking the complete temporal dynamics of vegetation and water-related indices throughout the entire rice growth cycle. There is an urgent need for a method that fully leverages the time-series characteristics of remote sensing indices to enable accurate, scalable, and timely rice mapping.
Focusing on the Hangjiahu Plain, a typical rice-growing region in eastern China, a novel approach—dynamic NDVI-SDWI Fusion method for rice mapping (DNSF-Rice) was proposed in this research to accurately extract rice planting areas by synergistically integrating Sentinel-1 SAR and Sentinel-2 optical imagery on the google earth engine (GEE) platform. The methodological framework included the following three steps: First, using Sentinel-2 imagery, a time series of the normalized difference vegetation index (NDVI) was constructed. By analyzing its temporal dynamics across key rice growth stages, potential rice planting areas were identified through a threshold-based classification method; Second, a time series of the Sentinel-1 dual-polarized water index (SDWI) was generated to analyze its dynamic changes throughout the rice growth cycle. A thresholding algorithm was then applied to extract rice field distribution based on microwave data, considering the significant irrigation involved in rice cultivation; Finally, the spatial intersection of the NDVI-derived and SDWI-derived results was intersected to generate the final rice planting map. This step ensures that only pixels exhibiting both vegetation growth and irrigation signals were classified as rice. The classification datasets spanned five consecutive years from 2019 to 2023, with a spatial resolution of 10 m.
The proposed method demonstrated high accuracy and robust performance in mapping rice planting areas. Over the study period, the method achieved an overall accuracy of over 96% and an F1-Score exceeding 0.96, outperforming several benchmark products in terms of spatial consistency and precision. The integration of NDVI and SDWI time-series features enabled effective identification of rice fields, even under the challenging conditions of frequent cloud cover and variable precipitation typical in the study area. Interannual analysis revealed a consistent increase in rice planting areas across the Hangjiahu Plain from 2019 to 2023. The remote sensing-based rice area estimates were in strong agreement with official agricultural statistics, further validating the reliability of the proposed method. The fusion of optical and SAR data proved to be a valuable strategy, effectively compensating for the limitations inherent in single-source imagery, especially during the cloudy and rainy seasons when optical imagery alone was often insufficient. Furthermore, the use of GEE facilitated the rapid processing of largescale time-series data, supporting the operational scalability required for regional rice monitoring. This study emphasized the critical importance of capturing the full temporal dynamics of both vegetation and water signals throughout the entire rice growth cycle, rather than relying solely on fixed phenological stages.
By leveraging the complementary advantages of optical and SAR imagery and utilizing the complete time-series behavior of NDVI and SDWI indices, the proposed approach successfully mapped rice planting areas across a complex monsoon climate region over a five-year period. The method has been proven to be stable, reproducible, and adaptable for large-scale agricultural monitoring applications.
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