Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

Abstract

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China

“…Meanwhile, the comparison with the existing NLCD-Rice data revealed that the ILMP-Rice map displayed more details about the spatial distribution and lower misclassification in the fragmented areas with diverse land-cover types ( Figure 10 and Table 3). A similar algorithm has also been proven to perform better than the conventional phenology-based algorithm based on a single satellite sensor in areas with a single-cropping system [14]. These results suggest that the fusion-and phenology-based algorithms have great potential for mapping rice paddies in the complex landscapes of southern China.…”

“…Second, pixel-and phenology-based algorithms were applied for mapping rice paddies. This algorithm has mostly been used in the cold temperate zones of northeast Asia, where there is one single-cropping season [3,14,24]. Our study extended the algorithm based on fused time series images into the subtropical region, where there are small cropland field sizes and multiple cropping systems employed.…”

“…Compared with previous algorithms used in areas with a single-cropping system [14], our approach could also be applied to improve the mapping of rice paddy cropping intensity in areas with complex cropping systems in southern China. Masking other land-cover types has proven essential for mapping rice paddies and cropping intensity [13], and improved mapping of rice paddy planting areas at high resolutions can improve mapping of cropping intensity [26].…”

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

“…Rice is one of the most important staple food in the world, especially in China. There are more than half of the population worldwide taking rice as their staple food [1,2]. Timely and accurate monitoring of rice growth is of great significance for food security and yield estimation [3,4].…”

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

“…The data fusion technique [18,19], which is used by CHNs to fuse data in this paper, is a common and effective way to eliminate the data redundancy, reduce the data size, and decrease the energy consumption. This paper uses an improved back propagation neural network (BPNN) to implement the data fusion.…”