Cropland Data Extraction in Mekong Delta Based on Time Series Sentinel-1 Dual-Polarized Data

Abstract

In recent years, synthetic aperture radar (SAR) has been a widely used data source in the remote sensing field due to its ability to work all day and in all weather conditions. Among SAR satellites, Sentinel-1 is frequently used to monitor large-scale ground objects. The Mekong Delta is a major agricultural region in Southeast Asia, so monitoring its cropland is of great importance. However, it is a challenge to distinguish cropland from other ground objects, such as aquaculture and wetland, in this region. To address this problem, the study proposes a statistical feature combination from the Sentinel-1 dual-polarimetric (dual-pol) data time series based on the m/χ decomposition method. Then the feature combination is put into the proposed Omni-dimensional Dynamic Convolution Residual Segmentation Model (ODCRS Model) of high fitting speed and classification accuracy to realize the cropland extraction of the Mekong Delta region. Experiments show that the ODCRS model achieves an overall accuracy of 93.85%, a MIoU of 88.04%, and a MPA of 93.70%. The extraction results show that our method can effectively distinguish cropland from aquaculture areas and wetlands.

Single-Season Rice Area Mapping by Combining Multi-Temporal Polarization Decomposition Components and the Two-Stage Segmentation Method

“…User Accuracy = Precesion = TP TP + FP (12) It can be seen that for the rice region, the mapping accuracy of the ODCRS model is 88.70%, and the user accuracy is 84.19%, proving the effectiveness of the proposed features for rice mapping and the two-stage segmentation structure.…”

“…Multi-temporal C-band (4-8 GHz) RADASAT-2 data were used by [7] to achieve high-precision fine classification of rice fields. The time series backscatter coefficient data of TerraSAR in the X-band (8)(9)(10)(11)(12) from May to October were used by Nelson et al to monitor rice in South and Southeast Asia [8].…”

“…Polarization decomposition methods can reveal the deep scattering mechanism of ground objects, while utilizing the intensity and phase information of ground object scattering. There are already many applications of polarization decomposition methods in other remote sensing fields, such as cropland extraction [12], forest biomass estimation [13], crop growth descriptor estimation [14], etc. The polarization decomposition method has some applications in rice monitoring, such as rice phenology monitoring [15,16], lodging monitoring [17], etc.…”

“…Xu et al [24] proposed the HRUnet (High Precision UNet) for the task of cropland extraction, which achieved an accuracy of 92.81% in the experiment. In order to achieve the goal of reducing the computational complexity and complexity of rice mapping, the ODCRS model was selected for experiments on extracting rice planting areas, showing excellent performance in extracting cropland in rice planting areas due to the good balance between the loss of detail features and information redundancy [12]. To further reduce the difficulty in sample making for training deep learning models, a two-stage segmentation structure was used in the study, which also helped to improve the mapping accuracy.…”