Mapping Paddy Rice Planting Area in Dongting Lake Area Combining Time Series Sentinel-1 and Sentinel-2 Images

Abstract

Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to the monsoon climate in southern China, it is cloudy and rainy throughout the year, which makes it difficult to obtain accurate information on rice cultivation based on optical time series images. Conventional image synthesis is prone to omission or redundancy of spectral and temporal features that are potentially important for rice-growth identification, making it difficult to determine the optimal features for high-precision mapping of paddy rice. To address these issues, we develop a method to granulate the effective use interval of classification features by extracting phenological signatures of rice to obtain cost-effective and highly accurate mapping results. Two steps are involved in this method: (1) analyzing the information on various features (spectra, polarization, and seasonal regularity) to identify three key phenological periods throughout the lifespan of paddy rice; (2) identifying the features with the highest class separation between paddy rice, non-paddy crops, and wetlands under different phenological stages; and (3) removing redundant features to retain the optimal feature combinations. Subsequently, the obtained feature sets are used as input data for the random forest classifier. The results showed that the overall accuracy of the identified rice results was 95.44% with F1 scores above 93% for both single- and double-cropping rice. Meanwhile, the correlation coefficient of our mapped rice area compared with the official statistics of rice area at county and district levels was 0.86. In addition, we found that combining Sentinel-1 and Sentinel-2 images for rice recognition was better than using Sentinel-1 or Sentinel-2 alone, and the classification accuracy was improved by 5.82% and 2.39%, which confirms that the synergistic Sentinel-1 and Sentinel-2 data can effectively overcome the problem of missing optical images caused by clouds and rain. Our study demonstrates the potential of distinguishing mixed rice-cropping systems in subtropical regions with fragmented rice-field distribution in a cloudy and rainy environment, and also provides a basis for the rational layout of rice production and improvement of cultivation systems.

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

“…Flood signals in rice fields can be effectively captured by combining data from multiple sources for a wider range of spatial and temporal observations [23,75,76]. This also helps to extend the AF-OB method or its idea to areas with humid, cloudy, and rainy climates.…”

A Decision Rule and Machine Learning-Based Hybrid Approach for Automated Land-Cover Type Local Climate Zones (LCZs) Mapping Using Multi-Source Remote Sensing Data

“…Remote Sensing (RS) allows us to monitor global forest cover, agricultural land and other land covers and provides a cost-effective and efficient way to map land-cover type LCZs on a large scale [9], [22], [23], [24]. Sentinel 2 near-infrared (B8), red-edge (B5, B6, B7) and red (B4) bands are particularly useful for agriculture, vegetation and, forest type mapping [25].…”

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

“…However, as optical remote sensing data, Sentinel-2 is susceptible to cloud interference. Over the past two years, many scholars have chosen Sentinel-1 as an alternative to enhancing optical time series [6,[29][30][31]. The integration of Sentinel-1/2 fused time series data and phenology features was employed to enhance the effective utilization intervals of classified features and to map the distribution of rice planting.…”

“…The integration of Sentinel-1/2 fused time series data and phenology features was employed to enhance the effective utilization intervals of classified features and to map the distribution of rice planting. This approach yielded improvements of 5.82% and 2.39% in accuracy compared to the use of Sentinel-1 or Sentinel-2 time series data alone, respectively [29]. Fatchurrachman et al [32] Synthesized the time series dataset of Sentinel-1 VH polarization and Sentinel-2 NDVI month by month, and conducted unsupervised classification of rice by k-means clustering.…”