Abstract
The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.
Highlights
We aimed to use the cloud removal interpolation method and data fusion method to reconstruct remote sensing time series data and map paddy rice areas combining these with a phenology-based algorithm, and provide a new method to map paddy rice in cloudy and rainy areas
The spatiotemporal data fusion and phenology-based paddy rice mapping methodology mainly involved the following steps (Figure 4): (1) removing thick clouds, i.e., for the Sentinel-2 and Landsat 8 OLI images, thick clouds were removed with modified neighborhood similar pixel interpolator (MNSPI) approach and, for the MODIS images, clouds were removed with the quality assessment (QA) band; (2) flexible spatiotemporal data fusion (FSDAF) prediction
After the cloud removal process, we compared the reflectance values of restored pixel with the reflectance values process, we compared the cloud reflectance of original images in the patch. values of restored pixel with the reflectance values of the accuracy evaluation originalFor images in the cloud patch.of the FSDAF model, we evaluated the accuracy of prediction comparingofthe predicted with the image
Summary
As a staple food, feeds almost half the world’s population [1]. It is of important to obtain the spatial distribution of paddy rice in a timely and accurate fashion [2,3,4,5,6]. Mapping paddy rice is significant for understanding and evaluating regional, national, and global issues such as food security, climate change, disease transmission, and water resource utilization [6]. Southwest China is a key paddy rice planting area. This area has a lot of precipitation and is cloudy and foggy. With the continuous development of satellite remote sensing technology, our ability to monitor and map paddy rice fields has improved. Time series of remote sensing data, such as MODIS and Landsat, have been
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