Abstract
Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and Landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user's accuracy, producer's accuracy, F1-score, and overall accuracy of 0.91 ± 0.004, 0.74 ± 0.010, 0.82, and 0.98 ± 0.001 (± value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 ± 0.86 × 10~3 km~2. Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.
NETL
NSTL
Springer Nature